The data then gets prepared in formats to be used by people such as business analysts, data analysts, and data scientists. The format of the data will be different depending on the intended audience. Part of the Data Engineer’s role is to figure out how to best present huge amounts of different data sets in a way that an analyst, scientist, or product manager can analyze.

What does a data engineer do?

A data engineer is an engineer who creates solutions from raw data. A data engineer develops, constructs, tests, and maintains data architectures.

Let’s review some of the big picture concepts as well finer details about being a data engineer.

What does a data engineer do – the big picture

Data engineers will often be dealing with raw data. Many of them are already familiar with SQL or have experience working with databases, whether they’re relational or non-relational. They need to understand common data formats and interfaces, and the pros and cons of different storage options.

Data engineers are responsible for transforming data into an easily accessible format, identifying trends in data sets, and creating algorithms to make the raw data more useful for business units.

Data engineers have the ability to convert raw data into useful insights. Data scientists are very grateful for the work done by data engineers to prepare data so that they can turn it into insights. 

What does a data engineer do – details

The architecture that a data engineer will be working on can include many components. The architecture can include relational or non-relational data sources, as well as proprietary systems and processing tools. The data engineer will often add services and tools to the architecture in order to make sure that data scientists have access to it at all times.

Earlier we mentioned ETL or extract, transform, load. Data engineers use the data architecture they create to load, extract and transform raw data. Raw data can often contain errors and anomalies such as duplicates, incompatibilities, and mismatches. Data engineers will review the data and suggest ways to improve its quality and reliability. 

How data engineers use tools – a basic example

An import tool that can handle data could be used to ignore rows not meeting certain criteria and only import those rows. Data could be a string, a number, or a particular length.

You could use a Python script to convert or replace specific characters within those fields. Creative data engineers will be able to identify problems in data quickly and will be able to find the best solutions.

How to become a data engineer

Here’s a 6-step process to become a data engineer:

1. Understand data fundamentals
2. Get a basic understanding of SQL
3. Have knowledge of regular expressions (RegEx)
4. Have experience with the JSON format
5. Understand the theory and practice of machine learning (ML)
6. Have experience with programming languages

1. Understand data fundamentals

Understanding how data is stored and structured by machines is a foundation. For example, it’s good to be familiar with the different data types in the field, including:

• variables
• varchar
• int char
• prime numbers
• int numbers 

Also, named pairs and their storage in SQL structures are important concepts. These fundamentals will give you a solid foundation in data and datasets.

2. Get a basic understanding of SQL

A second requirement is to have a basic understanding of SQL. Knowing SQL means you are familiar with the different relational databases available, their functions, and the syntax they use.

3. Have knowledge of regular expressions (RegEx)

It is essential to be able to use regular expressions to manipulate data. Regular expressions can be used in all data formats and platforms.

4. Have experience with the JSON format

It’s good to have a working knowledge of JSON. For example, you can learn about how JSONs are integral to non-relational databases – especially data schemas, and how to write queries using JSON.

5. Understand the theory and practice of machine learning (ML)

A good understanding of the theory and practice of machine learning will be helpful as you architect solutions for data scientists. This is important even if working with ML models may not be part of your daily routine. 

6. Have experience with programming languages

Having programming knowledge is more of an option than a necessity but it’s definitely a huge plus. Some good options are Python (because of its flexibility and being able to handle many data types), as well as Java, Scala, and Go.

Soft skills for data engineering

Problem solving using data-driven methods

It’s key to have a data-driven approach to problem-solving. Rely on the real information to guide you.

Ability to communicate complex concepts and visualize them

Data engineers will need to collaborate with customers, integration partners, and internal technology teams. Sharing your insights with people of various backgrounds and understanding what they are trying to convey is always helpful.

Strong sense of ownership

Take initiative to solve complex problems, because that’s what this job is about. You will be given a framework and a job goal – it’s up to you to figure out the rest.

Tools and resources for data engineering

The following are tools that are important in data engineering, along with courses that explain how to use them and where they fit in the job role.

Databases, relational and non-relational

It’s good to understand database architectures. Some basic real-world examples are:

• Relational, SQL database: e.g. Microsoft SQL Server
• Document-oriented database: MongoDB (classified as NoSQL) 

The Basics of Data Management, Data Manipulation and Data Modeling

This learning path focuses on common data formats and interfaces. The path will help you understand common data formats you might encounter as a data engineer, starting with SQL.

MongoDB Configuration and Setup

Watch an example of deploying MongoDB to understand its benefits as a database system.

Apache Kafka

Amazon MSK and Kafka Under the Hood

Apache Kafka is an open-source streaming platform. Learn about the AWS-managed Kafka offering in this course to see how it can be more quickly deployed.

Apache Spark

Apache Spark

In this lecture, you’ll learn about Spark – an open-source analytics engine for data processing. You learn how to set up a cluster of machines, allowing you to create a distributed computing engine that can process large amounts of data. 

Apache Hadoop

Introduction to Google Cloud Dataproc

Hadoop allows for distributed processing of large datasets. In this course, get the real-world context of Hadoop as a managed service as part of Google Cloud Dataproc, used for big data processing and machine learning. 

Python

Introduction to Python for Programmers

Python is a powerful and flexible scripting language that can handle many data types. This course is a quick summary of the theory and practice of Python for users who already have a programming background.

Java

Introduction to Java

Java is a robust, complicated, but proven language that forms the base of much data engineering work. This learning path covers the basics of Java, including syntax, functions, and modules. These courses teach you how to write Java applications and functions using object-oriented principles.

Data Engineering Certifications

There’s probably no better way to both educate yourself in data engineering and prove to employers what you know than through certifications from the big cloud providers. 

The following certification learning paths provide updated, proven, detailed methods to learn everything you need about data engineering.

AWS Data Engineering

AWS Certified Data Analytics Specialty (DAS-01) Certification Preparation

This learning path covers the five domains of the exam. This includes understanding the AWS data analysis services and how they interact with one another. It also explains how AWS data services fit into the data lifecycle of storage, processing, visualization, and storage.

Azure Data Engineering

Foundational Certification

DP-900 Exam Preparation: Microsoft Azure Data Fundamentals

This certification path is for technical as well as non-technical individuals who wish to show their knowledge about core data concepts and how these are implemented using Azure data services.

You’ll learn about the basics of data concepts, relational and non-relational Azure data, and how to describe an Azure analytics workload.

Associate Certifications 

DP-203 Exam Preparation: Data Engineering on Microsoft Azure

This certification learning path will teach you how to manage and deploy a range of Azure data solutions. This exam will test your knowledge in four areas: designing and building data storage; designing, developing and managing data processing; designing and monitoring security; and optimizing data storage.

Google Cloud Data Engineering

Google Data Engineer Exam – Professional Certification Preparation

This certification learning path helps you understand and work with BigQuery, Google’s managed cloud data warehouse. You’ll learn how to load, query, and process your data. You’ll learn how to use machine learning for analysis, build data pipelines, and use BigTable for big data applications.

What is Big Data Engineering?

You can call it a buzzword, but big data engineering is the umbrella term for everything in the data engineering world. Typically in big data engineering, you have to interface with huge data processing systems and databases in large-scale computing environments. These environments are often cloud-based to take advantage of the distributed, scalable nature of cloud solutions, as well as turnkey set up in order to speed up development and deployment.

What’s the difference between a data engineer and a data scientist?

These roles can be combined, but they work well together. Data scientists and data engineers are two roles that require different skills and have distinct tasks.

Data engineers design, test and maintain data. Data scientists organize and manipulate data in order to gain insight. Data engineers are responsible for creating data that scientists can use.

Although things aren’t always perfectly separated in the real world, think of the data engineer as the controller of the data and its infrastructure, and the data scientist as the specialist who gathers insights from the curated data.

Both roles are important and need cooperation and respect to work well together and achieve a successful outcome.

How much do data engineers make?

As of early 2022, some of the top salary sites online show the following numbers for an average base salary for a data engineering role in the United States:

• Glassdoor: $112,000
• Payscale: $93,000
• Indeed: $116,000

FAQ 

Is data engineering easy?

It’s not easy, and it’s not the easiest role to get into, but it’s definitely interesting and rewarding. Some industry experts complain that there is a huge gap between self-educated and actual-world data engineers. This is due to a lack of relevant college or university programs that prepare you for data engineering.

Do you need math for data engineering?

In general, data engineering is not math-heavy. It would be helpful to be familiar with statistics and probability to get a sense of what data scientists in your team will do. A good understanding of problem solving from a software engineering and cloud architect point of view will help for daily issues.

Are data engineers in demand?

Yes, data engineers are in demand, especially as companies realize that the hype of data science is built on the foundation of work from data engineers. The most marketable data engineers have multi-cloud experience to help them make an impact in any environment.

Do data engineers code?

Yes, data engineers can expect to do a lot of data pipeline coding so they should be comfortable with programming languages and debugging issues. It’s helpful to be fluent in SQL, Python, and R.

The post What is Data Engineering? Skills, Tools, and Certifications appeared first on Cloud Academy.

New content on Cloud Academy

At any time, you can find all of our new releases by going to our Training Library and finding the section titled “New this month in our library.” You can also keep track of what new training is coming for the next 4-6 weeks with our Content Roadmap.

• AWS
• Microsoft Azure
• Data Science/Artificial Intelligence
• DevOps
• Google Cloud Platform
• Programming
• Webinars
• Platform

Data Science/Artificial Intelligence

Learning Path: Wrestling with Data

In this learning path, we dive into the various tools and techniques available for manipulating information and data sources. We then show you how you can use this knowledge to actually solve some real-world problems.

Learning Path: Introduction to Data Visualization

This learning path explores data sources and formatting, and how to present data in a way that provides meaningful information. You’ll look at data access patterns, and how different interfaces allow you to access the underlying information. This learning path also provides a practical, real-world example of how all this theory plays out in a business scenario. 

Course: Data Wrangling with PANDAS

In this course, we are going to explore techniques to permit advanced data exploration and analysis using Python. We will focus on the Pandas library, focusing on real-life case scenarios to help you to better understand how data can be processed using Pandas.

Course: Wrestling With Data

In this course, we’re going to do a deep dive into the various tools and techniques available for manipulating information and data sources along with showing you at the end of it how you can actually solve some real-world problems. If you are trying to handle increasingly complex data sets and round out your experience as a professional data engineer, this is a great course to get a practical field-based understanding.

DevOps

Hands-on Lab: Installing and Running Applications with Docker Enterprise Universal Control Plane

In this lab, you will learn how to install UCP onto bare Docker hosts to create a multi-node installation from the ground up. You will also learn how to deploy applications onto the UCP cluster you create using the web interface.

Lab Challenge: Docker Swarm Playground

This playground provides a Docker swarm cluster comprised of one manager node and two worker nodes. You have full access to swarm nodes with, each having docker, docker-compose, and relevant command-line completions already installed and running. You have full access to the underlying host as well so you are not restricted compared to an environment that runs Docker in Docker (dind).

Hands-on Lab: Deploying Infrastructure with Terraform

Terraform is an infrastructure automation tool that allows companies to manage infrastructure through code. This provides many benefits such as greater recovery, predictability, and speed. In this lab, you will create a Terraform configuration to deploy a VPC in AWS.

Course: Tech Talk: Building Automated Machine Images with HashiCorp Packer

In this Tech Talk, you’ll watch a presentation on HashiCorp’s Packer and how it can be used to build automated machine images and then deploy the new image into a production environment with a CI/CD pipeline. You’ll follow virtually as a group of IT professionals discuss the tool and its uses. 

Google Cloud Platform

Course: Managing GCP Operations Monitoring

This course shows you how to monitor your operations on GCP. It starts with monitoring dashboards. You’ll learn what they are and how to create, list, view, and filter them. You’ll also see how to create a custom dashboard right in the GCP console.

Course: Managing and Investigating Service Incidents on GCP

Managing and investigating service incidents is an important part of the maintenance process. It is a necessity that can be laboring but with the right organization, understanding of the systems, the knowledge of processes, and the discipline to adhere to best practices, it can be optimized. This course will focus on the predominant parts of managing service incidents and utilizing Google Cloud Platform to aid in the endeavor.

Hands-on Lab: Scaling an Application Through a Google Cloud Managed Instance Group

In this lab, you will create an instance template, an instance group with the autoscaling enabled, and you will then attach an HTTP load balancer to the instance group to load balance the traffic to the VM group. You will also perform a stress test to check that the autoscaling is working properly.

Lab Challenge: Google Cloud Scaling Applications Challenge

In this lab challenge, you will need to prove your knowledge of highly available and scalable applications by creating infrastructure on Google Compute Engine. The objectives you will need to achieve represent essential skills that a Google Certified Associate Cloud Engineer and Google Certified Professional Cloud Architect need to have. 

Lab Challenge: Google Cloud SQL Challenge

In this lab challenge, you will need to prove your knowledge of Google Cloud SQL by creating a production ready Cloud SQL instance. The objectives you will need to achieve represent essential skills that a Google Certified Associate Cloud Engineer and Google Certified Data Engineer need to have.

Programming

Course:  Building a Python Application: Course One

One of the best ways to learn new programming languages and concepts is to build something. Learning the syntax is always just the first step. After learning the syntax the question that arises tends to be: what should I build? Finding a project to build can be challenging if you don’t already have some problems in mind to solve. This course is broken up into sprints to give you a real-world development experience, and guide you through each step of building an application with Python.

Hands-on Lab: Introduction to Graph Database With Neo4j

In this lab, you will understand the core principles of a graph database (especially a property graph) and you will install the Neo4j DBMS on an EC2 instance. This lab is intended for data engineers who want to switch to the graph data model or developers who need to build an application based on a graph database.

Hands-on: Lab: Constructing Regular Expression Character Classes

Regular Expressions are a tool for searching and manipulating text. In this lab, you will use the Python programming language to learn the basics of how to use a regular expression and you’ll learn about the different character classes available for matching different types of characters.

Hands-on: Lab: Working with Regular Expressions: Special Characters and Anchors

In this lab, you will learn how to use quantifiers to match sequences of characters in different ways, you will learn about anchors, and you will learn how to use capture groups. 

Hands-on: Lab: Using Regular Expressions Effectively in the Real World

In this lab, you will learn how to use quantifiers to match sequences of characters in different ways, you will learn about anchors, and you will learn how to use capture groups. 

Webinars

Office Hours: AWS Solutions Architect – Associate | Domain 1 of 4: Design Resilient Architectures

Take a deep dive on Domain 1: Design Resilient Architectures of the AWS Solutions Architect – Associate exam.

Office Hours: Decoupling Architectures Like There’s No Tomorrow

Learn all about decoupling architectures, how to set them up, and manage them, successfully with our experts.

Platform

Learn, Grow, Succeed: Introducing Training Plans for Individuals

We’re excited to announce that we’ve released a new game-changing feature for individual users that has already proven to help our largest enterprise customers increase newly acquired skills by 5x: Training Plans.

Stay updated

As always, we use Cloud Academy Blog to keep you up-to-date on the latest technology and best practices. All of our new blogs, reports, and updates go directly to our Cloud Academy social media accounts. For the latest updates, follow and like us on the following social media platforms:

• YouTube
• LinkedIn
• Facebook
• Twitter

The post New Content: AWS Data Analytics – Specialty Certification, Azure AI-900 Certification, Plus New Learning Paths, Courses, Labs, and More appeared first on Cloud Academy.

New content on Cloud Academy

At any time, you can find all of our new releases by going to our Training Library and finding the section titled “New this month in our library.” You can also keep track of what new training is coming for the next 4-6 weeks with our Content Roadmap.

• Alibaba
• AWS
• Microsoft Azure
• Data Science/Artificial Intelligence
• DevOps
• Security
• Webinars
• Platform

Alibaba

Learning Path: Alibaba Cloud Certified Associate (ACA) Preparation

Just starting out in the world of Alibaba Cloud? Let this learning path be the first step on your journey. It covers the essential aspects of this fast-growing platform, introducing you to the fundamental services you’ll need in order to build your own Alibaba Cloud infrastructure.

Course: Alibaba Server Load Balancer (SLB)

This course provides an introduction to Alibaba’s Server Load Balancer service, also known as SLB. The course begins with a brief intro to load balancing in general and then takes a look at Alibaba SLB and its three main components.

Data Science/Artificial Intelligence

Learning Path: The Beginner’s Guide to Machine Learning and Artificial Intelligence

This Learning Path provides an introduction to Machine Learning for beginners and​ is designed to be a gentle introduction, which means we’ll be starting at the ground up and focusing on giving students the tools and materials they need to navigate the space. 

Learning Path: The Basics of Data Management, Data Manipulation and Data Modelling

Take this learning path to get the basics of everything data-related. Learn about data sources, data formats, databases, SQL. This learning path focuses on understanding common data formats and interfaces. It explores some common data formats that you’ll encounter as a data engineer, and you’ll get a deep understanding of the pros and cons of storing your data in different ways.

Learning Path: Moving From Spreadsheets

This learning path gives you a fully hands-on introduction to machine learning with a focus on databases. It is comprised entirely of our interactive hands-on labs which means that you will get practical experience from the get-go.

DevOps

Hands-on Lab: Spinnaker Pipelines – Deploying Resources into Kubernetes

Spinnaker is an open source, multi-cloud continuous delivery platform for releasing software changes with high velocity and confidence. In this Lab scenario, you’ll first install and configure Spinnaker into its own dedicated Kubernetes cluster. As you advance, you’ll get into Spinnaker Pipelines, using Spinnaker with Kubernetes clusters, and extending the build and deployment even further.

Lab Challenge: Ansible Configuration Management Troubleshooting Challenge

In this lab challenge your Ansible configuration management skills are put to the test. You need to complete several tasks using Ansible to configure a host to serve a web application over HTTP and a web page over HTTPS. Use your knowledge of Ansible concepts and the command-line interface (CLI) to troubleshoot and resolve the issues in the provided configuration to pass all of the checks before time runs out.

Lab Challenge: Terraform Deploy AWS Resources Challenge

In this lab challenge, you will put your infrastructure development skills to the test. You will be tasked with developing an infrastructure solution using Terraform by modifying an existing Terraform configuration to include deploying a subnet and EC2 resource.

Course: Introduction to Kubernetes – refresh

Kubernetes is a production-grade container orchestration system that helps you maximize the benefits of using containers. Kubernetes provides you with a toolbox to automate deploying, scaling, and operating containerized applications in production. This course will teach you all about Kubernetes including what it is and how to use it.

Hands-on Lab: Webserver Creation using Pulumi to Manage Infrastructure

Pulumi is an Infrastructure as Code tool that supports multiple cloud providers. The key feature of Pulumi is that it allows you to describe your infrastructure using any of the popular programming languages it supports. In this lab, you will learn how to install and configure the Pulumi command-line tool, and you will learn how to use it to create a web server.

Security

Hands-on Lab: Cryptanalysis of Substitution Ciphers

Monoalphabetic ciphers are simple substitution ciphers where only one alphabet is used to substitute the characters from the plaintext and replace them one-for-one, where each character in the plaintext is always substituted with the same character in the ciphertext. This means that these ciphertexts are susceptible to frequency analysis. In this lab you will explore this by decrypting a classical cipher, the Caesar cipher.

Hands-on Lab: Asymmetric Encryption RSA Demonstration

RSA is a modern asymmetric encryption standard used in public key cryptographic schemes to share secret values and electronically sign documents. The algorithm works by using modulo mathematics to share numbers between parties, encrypting a value with a public key which only the holder of a separate, secret, private key. Each key pair is generated is calculated using several mathematic functions and equations. You will explore the RSA encryption algorithm in this lab by encrypting and decrypting a message.

Hands-on Lab: Dictionary Attacking a Web Application with Hydra and Burp Suite

In this lab, you will be attacking a Linux machine named Metasploitable, running the Damn Vulnerable Web App (DVWA). The DVWA is an open source web app written to be vulnerable to a host of different security exploits, designed for security professionals to practice their skills and conduct research.

Hands-on Lab: Cracking Hashes with John the Ripper

This lab is part of a series on cyber network security. You will be looking at different hashing algorithms, each of which were commonly used to store passwords on Windows and UNIX systems. You will be using John the Ripper to crack some password files.

Hands-on Lab: Configuring Active Directory Group Policies

You will be using the AD environment to create a new Group Policy Object (GPO) and apply it to a user account in order to enforce some security settings. Group Policy Objects are technical definitions of a user’s permissions and rights within a domain. GPOs are a powerful tool that enable a domain administration team to apply the principle of least privilege across thousands of users quickly and efficiently.

Hands-on Lab: Configuring IPsec on a Windows 2016 Server

IPsec is a framework of open standards for ensuring private, secure communications over IP networks through the use of cryptographic security services. The Microsoft Windows implementation of IPsec is based on standards developed by the Internet Engineering Task Force (IETF) IPsec working group. You will configure IPsec on a Windows Server 2016 machine in this lab. 

Hands-on Lab: SSL Handshake Analysis using Wireshark

Secure Sockets Layer (SSL) is a protocol which allows web HTTPS applications to exchange information securely. Wireshark is a network protocol analyzer that security professionals can use to filter and search through in order to understand traffic that has been logged using tcpdump or a similar tool. You will be analyzing a network traffic capture of an SSL handshake and then using a private key to decrypt and extract a file from the capture.

Hands-on Lab: Exploiting the Heartbleed Bug using MetaSploit

The Heartbleed bug is a serious vulnerability that was discovered to exist on web servers using the OpenSSL cryptographic library, a popular implementation of the TLS protocol for web servers. This exploit will work on any unpatched web servers running an OpenSSL instance in either client or server mode. You will exploit the Heartbleed bug in this lab.

Hands-on Lab: Configuring Access Control Lists on Cisco Routers

Standard ACLs allow a network security technician to control the traffic moving through their network and limit its propagation based on the kind of traffic and the source address. Cisco devices use a proprietary Command Line Interface (CLI) called the Internetworking Operating System (IOS) to configure and manage them. You will be using a Cisco virtual network simulator called Cisco Packet Tracer to learn networking and protocol functions.

Hands-on Lab: Using Snort to Detect a Brute Force Hydra Attack

pfSense is a FreeBSD based router/firewall that can be configured with various plugin modules which can enable network operations and defend a network from malicious behavior in the form of an IDS/IPS module called Snort. You will be conducting a dictionary attack on the Metasploitable DVWA using Hydra and Burp Suite in Kali Linux and attempting to detect it on the router using Snort and the community ruleset. 

Hands-on Lab: Using the Man-In-The-Middle Framework (MITMf) to Bypass HTTPS Strict Transport Security (HSTS)

This exercise will familiarize you with the Man-In-The-Middle framework (MITMf) and how an attacker might use this toolset to attack clients on your network. The particular attack we will be performing in this exercise will be bypassing the HSTS policy. You will use MITMf to bypass HTTPS Strict Transport Security (HSTS) in this lab.

Hands-on Lab: Using the Low Orbit Ion Cannon (LOIC) to Perform Denial of Service (DoS) Attacks

Denial of service can take many forms, but the basic purpose of an attack is to disrupt the availability of a service and to prevent normal operations from occurring. The most common method of attacking availability is network-based. The LOIC tool you will be using in this lab was made famous during Anonymous’ attacks on the church of scientology during Project Chanology.

Hands-on Lab: Performing FilePwn Using the Man-In-The-Middle Framework (MITMf)

The MITMf is a collection of tools, written into an easy-to-use framework by byt3bl33d3r, which an attacker can use to simplify the construction and execution of Man-In-The-Middle (MITM) attacks. The MITM attack you will be carrying out will be utilizing a feature called “FilePwn”, where the attacker can inject a malicious payload into or fully replace a file that the victim is downloading from an HTTP website.

Webinars

Stop Messing Around and Start Passing Your AWS Exams

Preparing for a cert can be stressful, we know it. Wouldn’t it be wonderful if you could gather key insights on how to tackle and pass an AWS exam from someone who has already gone through a bunch of them? Look no further! In this on-demand webinar, our AWS Certification Specialist, Stephen Cole, will guide you through some general strategies that you can apply when taking your first AWS exam, from home or on-site. 

Office Hours: Nail the Google Associate Cloud Engineer Exam

This is your chance to take your career a step further. How? By preparing for the Google Associate Cloud Engineer Exam and passing it, of course! Join Guy Hummel, Google and Azure Content Lead, and Tom Mitchell, Google and Azure Trainer, in this on-demand webinar to learn all of the secrets behind this exam. Our Google experts will break down the five subject areas assessed in the exam, and using samples, explain how to analyze questions to determine the correct answer.

Platform

Introducing SCORM to Create Custom Content With Ease

Your infrastructure is custom-built to fit your organization’s unique needs. Shouldn’t your tech training be, too? Capturing all the nuances of your production environment within your cloud training helps teams perform with accuracy and precision in the real world. With SCORM, you can quickly create personalized training that’s also highly interactive, easy to manage, and easy to scale across teams.

Stay updated

As always, we use Cloud Academy Blog to keep you up-to-date on the latest technology and best practices. All of our new blogs, reports, and updates go directly to our Cloud Academy social media accounts. For the latest updates, follow and like us on the following social media platforms:

• YouTube
• LinkedIn
• Facebook
• Twitter

The post New Content: Azure DP-100 Certification, Alibaba Cloud Certified Associate Prep, 13 Security Labs, and Much More appeared first on Cloud Academy.

I recently had the opportunity to attend the 21st International Conference on Artificial Intelligence in Education (AIED 2020), and it was a great opportunity to hear about some of the latest research, as well as presenting a contribution to the field. The conference was supposed to be held in Ifrane, Morocco from July 6-10 2020, but due to the COVID-19 pandemic, it was held completely online in a virtual format.

Organizing an international conference is no easy task, and having to move everything online having only a few weeks to do so makes it even more difficult. However, the organizers did a great job with organizing an event that could be attended by people from all over the world, spanning different time zones. There were several tracks in parallel, and every presentation was held as a Zoom meeting, with twenty minutes for the presentation and ten minutes for a Q&A session.

Key takeaways

Here are some of the key takeaways from the AIED conference, focusing on only some of the many interesting topics which were presented.

Online learning

Recent technological advancements enabled the possibility of replicating many aspects of traditional education online, from university classes to general courses for acquiring new skills. However, in most cases, the approach adopted in online education is a carbon copy of what is done in person, and this limits what could be achieved with online learning. This issue was particularly visible in the last few months when education was forced to move (almost completely) online, even though it was not ready to do so. 

This concern was mentioned several times in the keynote presentations at the conference and, in particular, in the panel discussion that was held the third day. Significantly, it was titled “COVID-19 and the Future of AI in Education and Training” and it focused heavily on the challenges of online education, which should be looked at in a different way from the challenges of on-site education. While this is generally true for all the aspects of the educational activity, it requires special attention while developing AI systems for education. 

Modeling students

Many talks at AIED20 focused on techniques for creating students’ models, trying to model the learning process (both at an individual level and at a classroom level), with the goal of understanding how students learn. Such information can be leveraged to understand which are the aspects that lead to better academic results and possibly personalize the learning experience to improve students’ outcomes. 

This is a very broad category, and I mention here only some of the works which were presented at the conference.

One work, titled “The sound of inattention,” tried to predict students mind-wandering using features from the instructor’s speech as input. In practice, the authors tried to understand whether it is possible to recognize which aspects of the instructors’ speech (e.g. frequency, talking speed, etc.) cause the students to lose attention. Experimental results show that it is possible to predict students’ inattention from such features, suggesting that instructors could focus on those aspects while giving their lectures, especially in the case of pre-recorded video lectures.

Another work focused on a technique to detect “wheel-spinning” in an online examination platform. Wheel-spinning is what happens when a student keeps answering a problem, without successfully doing so. The proposed technique is interesting since it allows us to understand whether the platform should intervene to free the student from wheel-spinning, possibly suggesting to revise some content or to perform other exams before trying the current one again. Indeed, some research works — including one presented at AIED20 — showed that, if a student is stuck on a problem-solving task, by taking a break performing some other tasks and later returning to the original problem, we increase the probability that the student manages to solve the task.

Natural Language Processing techniques for supporting education

Natural Language Processing (NLP) focuses on the interactions between computers and natural human languages, particularly how to program computers to process and analyze large amounts of natural language data. Recent years have witnessed a tremendous advancement in  NLP, and it is becoming increasingly used in many different domains. For instance, we have been experimenting with it at Cloud Academy for scaling and automating quiz creation and for calibrating newly generated questions. Several papers about NLP techniques were presented at the AIED conference, including our contribution.

From a high-level perspective, NLP papers presented at this conference focused on three aspects which are of high interest to Cloud Academy: automated grading of students’ answers, question generation, and quality control for new questions. 

Automatic grading of students’ answers and essays is a challenging but extremely relevant task, especially for online education. Indeed, one of the issues of online learning is the large number of students that instructors have to deal with, which makes the manual correction of exams almost impossible. For this reason, multiple-choice questions (MCQ) are often the choice for online examinations, since they can be automatically corrected. However, using open-ended questions together with MCQs would improve the accuracy of students’ grading. If we were able to reliably correct open-ended questions in an automatic way, it would hugely help online examinations.

Question generation, too, can be very useful for helping instructors create effective questions from a given corpus of documents, such as all the lectures of a course. Indeed, question creation is a very time-consuming process, and providing some automatically generated candidate questions — which can be chosen, modified, or discarded by the instructor — can enable faster question generation.

Also, new questions (both automatically and manually generated) need to be assessed before being used in actual exams, and this was the focus of some other works in the conference. Indeed, not all the questions are suited for scoring students, and it is necessary to perform some kind of quality assurance to find the ones that should not be used. Using NLP techniques, it is possible to detect and discard the questions that should not be used to score students.

Supporting teachers and students

Some talks presented techniques for supporting teachers and students, focusing more on facilitating the human-computer interaction in the educational domain. In particular, the major interest was on Virtual Teaching Assistants (VTAs). VTAs are chat-based or speech-based applications that can automatically answer some of the students’ requests to reduce the number of emails and messages that the instructors have to answer. Indeed, VTAs can answer the most common questions (which are asked many times by different students),  allowing the instructors to focus on the requests that really require a human-to-human interaction.

Final thoughts

This was the second conference that our team attended in a virtual format this year, having taken part in the Learning Analytics and Knowledge Conference in March.

Again, the pace of the presentation was quite intense but very well organized, given that researchers from different time-zones presented their contributions.

The location and timing for next year’s conference have not been announced yet, but I am already looking forward to the progress of the research in the community and how the many interesting ideas presented this year will be developed further.

The post Takeaways from the International Conference on Artificial Intelligence in Education (AIED20) appeared first on Cloud Academy.

New content on Cloud Academy

At any time, you can find all of our new releases by going to our Training Library and finding the section titled “New this month in our library.” You can also keep track of what new training is coming for the next 4-6 weeks with our Content Roadmap.

• Alibaba
• AWS
• Microsoft Azure
• Google Cloud Platform
• Data Science/Artificial Intelligence
• DevOps
• Programming
• Webinars

Alibaba

Course: Alibaba Object Storage Service

This course is an introduction to the fundamental aspects of Alibaba’s Object Storage Service (OSS). It starts off by explaining the features and advantages of the service, before moving on to the concepts of OSS and security. You will then watch two demos that use real-life examples from the Alibaba Cloud platform to guide you through storage buckets and object operations.

Data Science/Artificial Intelligence

Hands-on Lab: Handling Variable Data in DynamoDB with Grace

Data can come in all varieties which can be a major drawback of having a fixed schema. NoSQL allows for any type of data to be stored, allowing your schema to evolve. In today’s world with IoT and increasing data complexity, NoSQL is becoming a standard part of many technology stacks. This lab is aimed at students with a basic understanding of Python who want to learn about Amazon DynamoDB. Students will explore programmatically and using the DynamoDB Console interface to insert and scan data.

Hands-on Lab: Reviewing Product Inventory in a Cloud SQL Database

This lab is aimed at beginners who want to move beyond spreadsheets and migrate their data into a database. After completing this lab, students will be able to use a MySQL Database in Google Cloud SQL and populate it from a CSV file. Additionally, students will learn how to use basic queries against their data.

Hands-on Lab: Visualizing Data in Amazon QuickSight

Amazon Quicksight can help you visualize, embed, and share data quickly. These valuable insights allow for users to look at quick summaries of aggregated information to make better business decisions. This lab is aimed at beginners who want to understand how to import and make their first insight.

Hands-on Lab: Working With Complex Rest APIs

The foundation of many applications involves Representational state transfer (REST) API, which is a way of structuring an API so that, through the use of HTTP verbs, one can get data. This lab will dive into understanding GET, POST, DELETE, and PATCH services. Students will learn how to use Python to interact with an API to view, create, update, and delete data.

Hands-on Lab: Dealing With Schema Changes in a Data Service

Databases are extraordinarily powerful in managing sets of data, but what do we do when management wants to add/remove/modify something that code does? This lab is aimed at students with a moderate understanding of data engineering and Python who want to understand how to update schemas to handle new features. We will walk through the changing requirements of a bug tracking application and how to handle them.

Hands-on Lab: Capturing New Knowledge from Your Data

This lab is aimed at students with a moderate understanding of data engineering and Python who want to understand how to perform aggregates on data such as COUNT(), SUM(), and AVG(). We will also look into advanced statements like CASE, and functions like DATEDIFF() and CONCAT(). We will walk through the changing requirements of a bug tracking application and how to handle them.

Hands-on Lab: Using Python to Drive Insights in BigQuery

In today’s world, we have to deal with ever-growing datasets. These often require specialized computer solutions to handle the extremely large volume. Google BigQuery makes it easy to query, process, and visualize large datasets. In this lab, we will get started with BigQuery to analyze a large public dataset.

Hands-on Lab: Drawing Insights with BigQuery ML

Machine learning is proving to be very powerful in gathering insights with your data. BigQuery ML allows the user to perform Machine Learning training, evaluation, and prediction on large sets of data. This lab will explain some of the basic concepts along with an example of training a linear regression and binary logistic regression model.

Hands-on Lab: Performing K-Means Clustering With Python

K-Means learning is a machine learning technique used to divide a dataset into clusters to analyze its results. This classification algorithm divides a large group of data into smaller groups to maximize the similarity between data points. We will walk through applying and analyzing the K-Means clustering algorithm on a set of data using the Python libraries: pandas, scikit-learn, and matplotlib.

DevOps

Learning Path: Site Reliability Engineering (SRE) Foundation

This learning path will provide you with an introduction to the principles and practices that enable enterprises to reliably and economically scale critical services. Adopting SRE into your own organization requires re-alignment, with a new focus on engineering and automation, and the adoption of a range of new working paradigms. The SRE learning path consists of eight courses, each focusing on a particular aspect of SRE.

Course: Introduction to Helm

Helm is a package manager for Kubernetes, used to simplify and enhance the deployment experience for deploying resources into a Kubernetes cluster. This training course explores Helm 3, which is the latest version of Helm building upon the successes of Helm 2. During this course, you’ll learn the fundamentals of working with Helm3, its features, and the key differences between Helm 3 and Helm 2.

Programming

Hands-on Lab: Python 3 Programming

This is a technical lab that introduces the Python 3 programming language. Python has enjoyed popularity in a variety of fields for its large scope of application and its ease of use. With Python 2 having moved to end-of-life status on January 1, 2020, there are also large amounts of legacy code that need porting to Python 3.

Webinars

Crush Mediocrity: 3 Steps to Unleash Your Team’s Cloud Potential

Learn about the main challenges that companies face and how to overcome them with an efficient and business-oriented tech training program.

Increase Big Data Application Throughput and Save up to 90% on Compute Costs

Are you running big data workloads on frameworks such as Spark or Hadoop? Join Chad Schmutzer, AWS Principal Developer Advocate, and Stuart Scott, Cloud Academy AWS Content & Security Lead, in this on-demand webinar as they explain how you can accelerate your big data application performance and lower costs by using Amazon EC2 Spot Instances with Amazon EMR.

Office Hours: Unraveling the AWS Technical Essentials

Are you getting ready to take your first steps into the AWS cloud? Are you considering the AWS Solutions Architect Associate exam? Cloud Academy can help you. If you want your first steps to have impact and focus, watch this on-demand webinar with our experts, Stephen Cole — AWS Certification Specialist and Will Meadows — Senior AWS Trainer, to learn about AWS’s foundational services: Compute, Databases, and Storage.

Stay updated

As always, we use Cloud Academy Blog to keep you up-to-date on the latest technology and best practices. All of our new blogs, reports, and updates go directly to our Cloud Academy social media accounts. For the latest updates, follow and like us on the following social media platforms:

• YouTube
• LinkedIn
• Facebook
• Twitter

The post New Content: Alibaba, Azure AZ-303 and AZ-304, Site Reliability Engineering (SRE) Foundation, Python 3 Programming, 16 Hands-on Labs, and Much More appeared first on Cloud Academy.

As you may know, we have three very different types of labs: hands-on labs (guided), lab challenges (non-guided), and our newest lab playgrounds (sandbox). Depending on your technical level, you’ll want to start with the right lab for you.

• Hands-on labs are guided experiences to learn in live cloud environments with step-by-step guidance to practice your skills without getting stuck. 
• Lab challenges are non-guided skill validation to demonstrate problem-solving skills — basically, hands-on labs with the gloves off.
• Lab playgrounds provide a safe and secure sandbox environment for you to explore your own ideas, follow along with Cloud Academy courses, or answer your own questions — all without having to install any software on your local machine. You can feel free to take risks and try unfamiliar tools and commands to learn more about working in a live environment — even if it means running into a dead end and needing to start the lab over.

New training justification letters

Since our training platform helps build the real-world experience you need before “testing” your skills in your company’s live environment, it helps mitigate major production issues and significantly adds value to your organization. To help you streamline your company approval process, we created new training justification letters. Whether you’re looking to get a personal plan for yourself or an enterprise plan for your team or a few co-workers, we have a template to help you showcase the value of using our training platform.

New content on Cloud Academy

At any time, you can find all of our new releases by going to our Training Library and finding the section titled “New this month in our library.” You can also keep track of what new training is coming for the next 4-6 weeks with our Content Roadmap.

• AWS
• Microsoft Azure
• Google Cloud Platform
• Machine Learning
• DevOps
• Programming
• Security
• Webinars

Machine Learning

Hands-on Lab: Moving From Spreadsheet to Database

Databases are essential to organizing and understanding your data. Databases allow you to ask questions or query your data to find exactly what is needed. This lab is aimed at beginners who want to move beyond spreadsheets and migrate their data into a database.

Hands-on Lab: Handling Variable Data with Grace

Data can come in all varieties, which can be a major drawback of having a fixed schema. NoSQL allows for any type of data to be stored, allowing your schema to evolve. In today’s world with IoT and increasing data complexity, NoSQL is becoming a standard part of many technology stacks. This lab is aimed at students with a basic understanding of Python who want to learn about Amazon DynamoDB. Students will explore programmatically and using the DynamoDB Console interface to insert and scan data.

Hands-on Lab: Acquiring and Storing Data in Python

Utilizing APIs to query and store data is the heart of most web applications. This lab is aimed at students who have a moderate understanding of Python, who want to understand how to query an API, manipulate the data, and store that data into a database with a more advanced schema. This lab walks through complex processing of JSON data into multiple tables, and shows how powerful Python is for data processing.

DevOps

Lab Challenge: Docker Basics Challenge

In this lab challenge, your basic Docker skills are put to the test. You need to complete several tasks using a real Docker host to complete the challenge. Use your knowledge of Docker concepts and the Docker command-line interface (CLI) in unison to pass all of the checks before time runs out.

Programming

Learning Path: Typescript: Zero to Hero

This learning path is a complete guide to building applications using Microsoft’s popular JavasScript superset TypeScript. The learning path starts with an overview of TypeScript, and how to get the TypeScript toolset installed.

Hands-on Lab: Understanding the Java Spring Framework

Spring is a popular Java-based framework for developing large-scale enterprise-level applications both on and off the web. It is based on the dependency injection design pattern and allows for building decoupled applications. It has grown in popularity over the years due to this nature and the number of supported projects that are still ongoing. Spring also provides support for connection to databases using both JDBC methods and object-relational mapping libraries such as Hibernate.

Security

Learning Path: Preparation for the (ISC)² CISSP Certification (UPDATE)

The Certified Information Systems Security Professional (CISSP) continues to be the most in-demand information security professional certification currently available. This certification learning path is suitable for anyone wanting to become certified as a CISSP.

Updated content to this learning path includes the following:

• Domain 6 Exam
• Domain 7 Exam
• Domain 8 Exam

Webinars

Office Hours: Mastering the Azure Administrator Certification

Do you want to take a really impactful step in your technical career? Join Guy Hummel, Azure Content Lead, and Tom Mitchell, Azure Trainer, to learn how to attain the Microsoft Azure Administrator Associate certification, one of the most sought after Azure certs around.

Reducing Costs for Your Containerized Workloads on ECS

In this webinar with Spot.io, we discussed how to optimize infrastructure for containers at 3 different stages:

1. The instance life cycle – should it run as an RI, a Spot Instance, or plain On-Demand?
2. The instance type and size – as we deploy many different ECS services, how do we best pick a mix of instances for our containerized applications?
3. The Container Utilization – how do we make sure our services and tasks are optimally sized for their needs and aren’t oversubscribed for resources?

Stay updated

As always, we use Cloud Academy Blog to keep you up-to-date on the latest technology and best practices. All of our new blogs, reports, and updates go directly to our Cloud Academy social media accounts. For the latest updates, follow and like us on the following social media platforms:

• YouTube
• LinkedIn
• Facebook
• Twitter

The post New Content: AWS, Azure, Typescript, Java, Docker, 13 New Labs, and Much More appeared first on Cloud Academy.

New Content on Cloud Academy

At any time, you can find all of our new releases by going to our Training Library and finding the section titled “New this month in our library.” You can also keep track of what new training is coming for the next 4-6 weeks with our Content Roadmap.

• AWS
• Microsoft Azure
• Google Cloud Platform
• Machine Learning
• DevOps
• Programming
• Webinars
• Platform Updates

Machine Learning

Learning Path: Practical Machine Learning

This learning path is an intense deep dive into the world of machine learning. In it, you’ll learn how to implement different machine learning models, validate their quality, and how to implement them practically.

Hands-on Lab: Getting Started with Natural Language Processing

Natural Language Processing is a form of computer learning that can understand human speech and text to drive insights. This lab is aimed at machine learning beginners who want to gain familiarity with Natural Language Processing (NLP) concepts. After completing this lab, you will be able to classify text and gather insights. Additionally, you will gain familiarity with the Amazon Comprehend UI and basic Python concepts for interacting with NLP APIs.

Hands-on Lab: Machine Learning – Training Custom Models

This lab is aimed at machine learning beginners who want to understand how to train custom models. After completing this lab, you will understand how to create a machine learning model based on a categorized set of images. Additionally, you will gain familiarity with Amazon Rekognition and basic Python concepts for interacting with the sample model.

DevOps

Courses: AKS Networking

This course explores AKS, Azure’s managed Kubernetes service, covering the fundamentals of the service and how it can be used. You’ll first learn about how as a managed service it takes care of managing and maintaining certain aspects of itself, before moving onto the core AKS concepts such as cluster design and provisioning, networking, storage management, scaling, and security. After a quick look at Azure Container Registry, the course then moves on to an end-to-end demonstration that shows how to provision a new AKS cluster and then deploy a sample cloud-native application into it.

Course: Introduction to Knative

Knative is a general-purpose serverless orchestration framework that sits on top of Kubernetes, allowing you to create event-driven, autoscaled, and scale-to-zero applications. This course introduces you to Knative, taking you through the fundamentals, particularly the components Serving and Eventing. Several hands-on demonstrations are provided in which you’ll learn and observe how to install Knative, and how to build and deploy serverless, event-driven scale-to-zero workloads.

Programming

Hands-on Lab: The C# Programming Language

This lab concentrates on the C# programming language itself, to fully prepare delegates in readiness for using the .NET Framework/Core. No object-orientated knowledge is assumed—the course provides a suitable OO primer. From basic procedural syntax to sophisticated object-oriented programming techniques, delegates will learn how to write .NET applications with code that is robust and maintainable. The lab is presented as a mixture of instructional guides and hands-on exercises.

Hands-on Lab: Programming in C

C is one of the most widely-used languages for systems software and workstation application programming, largely due to its power and flexibility. This lab will provide you a highly effective, structured approach to learning the C language.

Hands-on Lab: C for Experienced Programmers

This lab for experienced programmers is outstanding because of its emphasis on writing style, pitfalls to avoid, and techniques to use that make the code clear, concise, and maintainable. It is designed to accelerate through the foundations of C programming as appropriate to the skill level.

Hands-on Lab: Understanding the Java Spring Framework

This lab starts with how to set up simple Spring projects and define beans. We then move on to web-based MVC projects. Persistence and transactions are covered, looking at both JDBC and Hibernate ORM implementations. Finally, we cover how to secure an application, including authentication methods and user roles.

Webinars

Office Hours: Acing the AWS Solutions Architect Associate Certification 

Do you want to take a really impactful step in your technical career? Watch this on-demand webinar with our experts, Stuart Scott, AWS Content & Security Lead, and Stephen Cole, AWS Certification Specialist, to learn about how to attain the AWS Solutions Architect Associate certification, a.k.a. the most sought-after AWS cert around.

Platform updates

New enterprise plan invoicing

Our enterprise plan customers can now complete a purchase by invoice and have instant access to our platform. Our new accepted payment methods for the invoice include:

• Wire transfers
• ACH

How it works:

• There is a new “invoice” payment method option on the checkout page for enterprise customers
• If you select the new invoicing option, you can immediately start the enterprise onboarding without waiting for the invoice to be paid
• All enterprise members of that account have full access to content but limited labs sessions

Note: Member limitations are removed once the invoice is paid or when phone verification is completed

Stay updated

As always, we use Cloud Academy Blog to keep you up-to-date on the latest technology and best practices. All of our new blogs, reports, and updates go directly to our Cloud Academy social media accounts. For the latest updates, follow and like us on the following social media platforms:

• YouTube
• LinkedIn
• Facebook
• Twitter

The post New Content: AZ-500 and AZ-400 Updates, 3 Google Professional Exam Preps, Practical ML Learning Path, C# Programming, and More appeared first on Cloud Academy.

In this article, we’ll dive into the world of AI and how it can improve content creation. If you’re interested in diving deeper into AI, check out Cloud Academy’s course, Building a Chatbot on Azure. You’ll learn how to automate content creation tasks by using chatbots to answer typical questions about specific products and services.

What exactly is AI? 

Artificial intelligence (AI) is defined as the ability of a computer to emulate human intelligence processes through learning and automation. Modern AI-powered computers perform various tasks and assist us in almost every area of our lives. 

While there are still many misconceptions about AI, it’s important to understand that it’s not a threat to human employees. Oppositely, AI-powered technology can significantly improve your job as it proves to increase productivity and optimize processes.

As AI advances, computers are now able to perform many tasks, including the ability to: 

• Recognize emotions in human faces and speech
• Interpret and analyze images
• Understand, analyze, and recreate human languages
• Analyze and process complex patterns in a large amount of data
• Beat 75% of Americans in the visual intelligence test

Impressive, right? Now that computers can talk to us and score higher than us in tests, there are also ways they can help us optimize business activities and improve content efforts. 

There are many ways you can use artificial intelligence to automate the content creation process. Major media and news outlets, like The Washington Post, Forbes, The Guardian, and Reuters, use the help of “the robot reporters” to speed up and automate content creation. 

In fact, it’s possible that some of the tools you’re using for your business, like a chatbot or a logo maker, are AI-powered. In this article, we’ll discover different ways you can incorporate AI to help your content creation. 

How to use AI to improve content creation

1. Automate small content initiatives with natural language generation

Artificial intelligence isn’t quite in a place where robots can write entire articles for your company’s blog. At least not yet. In the meantime, one way to use AI is by creating small data-specific content pieces, like tweets, news updates, and reports with the help of the natural language generation technology. 

Not really sure what natural language generation (NLG) is? Simply put, it’s when an algorithm translates complex data into human language. In its essence, NGL software automatically generates narratives to describe structured data in a matter of milliseconds. You can use NGL software to write product descriptions and tailor customer satisfaction reports. 

The Washington Post’s “robot reporter,” called Heliograf, is a great example. Heliograf is an automated storytelling technology that covers D.C.-area high school football games based on the data submitted by football coaches. This technology allows in-house journalists to focus on in-depth reporting of the “bigger” games while keeping the local community informed. 

2. Improve personalization efforts

Did you know that 74% of marketers report an increase in customer engagement due to targeted personalization? The good news is that artificial intelligence can help your personalization and customer engagement, too. For example, you can employ AI-powered software to create personalized email marketing campaigns. They’ll manage subject line and email body personalization addressing recipients by their name or organization. AI can help your widely-distributed general email feel more personal, boosting customer engagement. 

Also, you can use AI to create highly-personalized apps. Under Armor’s health tracking app, called “Record,” is a wonderful example. The app uses the AI-powered algorithm to create workout and nutrition recommendations based on your personal data. 

3. Evaluate social media content

Also, you can use AI to track the performance of your social media content. Many marketers develop their content distribution strategies based solely on customer demographics. You can take this approach to the next level using the AI-powered software. Instead of tracking demographics only, collect and analyze customer locations, behaviors, and values. That’ll help you create super-personalized and targeted content to improve your social media campaign. 

4. Get fresh topics and keywords

Thanks to artificial intelligence, there are now tools that can help your keyword research and topic generation. AI can give you precious insight into what your customers are really interested in. 

Many keyword research tools allow you to find keywords showing their search volume, number of results, organic competition, difficulty score, search demand, and other metrics. Using keyword research tools will speed up your writing process and help you create more customer-specific content. 

There are many blog post idea generators out there to help you develop and expand your topics. Answer the Public is one of such tools. When you enter your keyword, let’s say “artificial intelligence,” you can see the most popular questions asked by real users. As simple as a few clicks, you get data and ideas to help you expand and develop your topic. 

5. Gather user-generated content

A new study shows that 90% of consumers report that user-generated content holds more influence over their buying decisions than other forms of marketing. AI can help you gather brand-related content posted by your customers on social media. This became possible due to the visual recognition technology that can analyze images and identify your products. You can use this data not only for marketing but also to get a better perspective on what works for your audience and community. Additionally, it increases your brand visibility and online presence. 

6. Use chatbots to help your customers

AI-powered chatbots can help you communicate with your customers better and enable them to self-create branded content. The first part is pretty obvious. We all know how to use chatbots for navigating customers and acting as customer service reps. 

When it comes to user-generated content, chatbots can facilitate your customers to create more of it. For example, Whole Foods launched an innovative chatbot for Facebook’s Messenger. Besides helping to navigate Whole Foods locations, the chatbot offers healthy and wholesome recipes. All you need to do is answer a series of questions (you can even use emojis to describe products) and let the chatbot do the rest. 

Alternatively, you can use chatbots to promote content sharing within your clients. Spotify’s chatbot is a great example of this strategy in real life. Subscribers can not only ask about their accounts and discover newly released music, but they can also choose songs their friends might be interested in. Then, they can easily share these recommendations with their friends within the platform. 

Final thoughts 

The rapid growth of AI-powered software is predicted to change content creation as it exists now. According to experts, AI will be able to complete any intellectual task humans can perform by the year of 2050.

But what can AI do now? AI-powered computes have learned to provide organizations with data, story templates, fresh blog ideas, and user-generated content. A combination of machine learning and customer data has the impact you need to take your content creation efforts to the next level. That’s why it is so important to analyze data at the first stages of your content creation process. Also, your content efforts can massively benefit from AI as it helps you create more personalized and targeted content. 

The post 6 Ways AI Can Improve Content Creation appeared first on Cloud Academy.

New Content on Cloud Academy

At any time, you can find all of our new releases by going to our Training Library and finding the section titled “New this month in our library.” You can also keep track of what new training is coming for the next 4-6 weeks with our Content Roadmap.

• Alibaba
• AWS
• Microsoft Azure
• Google Cloud Platform
• Data Science
• Programming
• Business Transformation
• Security
• Platform Updates

Data Science

Learning Path: Practical Data Science with Python

This learning path provides two learning experiences in one: it explores the world of data science while at the same time giving you hands-on, practical tutorials on how to use Python at an advanced level.

Programming

Learning Path: Fundamentals of R

This learning path introduces the fundamental concepts and knowledge you need to use the programming language R, a mathematical and statistical modelling language used extensively in data analysis and big data.

Business Transformation

Course: Simple Explanations to Technical Topics

Want some simple explanations to some of the technical terms and concepts about the cloud that everyone else seems to know already? This course is here to help!

Security

Learning Path: Introduction to Ethical Hacking Tools

• Nmap network scanner
• Netcat network utility
• Metasploit vulnerability exploitation tool
• Nikto web app scanner
• SQLmap SQL injection tool
• Burpsuite web app proxy
• Dirbuster vulnerability scanner
• Droopescan vulnerability exploitation tool

Learning Path: Preparation for the (ISC)² CISSP Certification

The Certified Information Systems Security Professional (CISSP) is one of the most globally recognized certifications in the information security profession. The following content was added to this learning path.

DOMAIN 8 – Software Development Security

Module 1:

• Understanding and applying security in the software development life cycle
• Enforcing security controls in the development environment

Module 2:

• The database environment
• Software development and the world of the web

Module 3:

• Considerations or secure software development
• Assessing the effectiveness of software security
• Assessing software acquisition security

Platform updates

Multi-year plans

You can now purchase two- or three-year plans at a significant discount. The two-year plan provides a 15% discount and the three-year provides an amazing discount of 25%!

How it works for non-customers

1. Choose either the enterprise plan or personal plan.
2. Use the drop-down field to select the subscription length.

How it works for personal plan customers

1. 1. Go to https://cloudacademy.com/pricing/update/
   2. Select the subscription length.
   3. Your plan will be pro-rated based on the number of months you have left in your current plan.

Note: Current enterprise customers must contact us at support@cloudacademy.com to upgrade to a multi-year plan.

Stay updated

As always, we use Cloud Academy Blog to keep you up-to-date on the latest technology and best practices. All of our new blogs, reports, and updates go directly to our Cloud Academy social media accounts. For the latest updates, follow and like us on the following social media platforms:

• YouTube
• LinkedIn
• Facebook
• Twitter

The post New Content: Alibaba, Azure Cert Prep: AI-100, AZ-104, AZ-204 & AZ-400, Amazon Athena Playground, Google Cloud Developer Challenge, and much more appeared first on Cloud Academy.