Want to Learn Cloud Computing? Begin Your Journey with AWS!

Cloud computing has revolutionized the way companies build and run applications. Instead of investing heavily in on-premises infrastructure, you can now leverage on-demand, scalable services from cloud providers like Amazon Web Services (AWS) to power anything from simple websites to sophisticated AI/ML workloads. As more companies adopt cloud, cloud skills have become table stakes for modern tech careers. If you‘re looking to break into cloud computing, there‘s no better place to start than with AWS.

AWS is the undisputed leader in the public cloud infrastructure market. According to Synergy Research Group, AWS held 33% market share in Q1 2022, more than Microsoft Azure and Google Cloud combined. AWS now has an $80B annual revenue run rate, growing 37% per year (AWS Q1 2022 earnings).

This dominance means most companies using public cloud are using AWS in some capacity. Over 80% of enterprises are running apps on or experimenting with AWS as of 2022 (Flexera 2022 State of the Cloud Report). There are now hundreds of thousands of active AWS customers across virtually every industry – from startups like Airbnb and Netflix to enterprises like Siemens and Samsung, and the US federal government (AWS Customer Success page).

The takeaway is clear – AWS skills are in high demand and short supply. According to data from Indeed, the average yearly salary for AWS-related jobs in the US is $108,000, among the highest of any tech skills (Indeed AWS Salary Data). If you‘re looking to maximize your career potential in the cloud computing industry, becoming an AWS expert is the way to go.

Key AWS Concepts and Terminology

At its core, AWS provides on-demand access to computing, storage, database, and networking resources in the cloud. But the AWS ecosystem has grown to include over 200 services across a wide range of categories. Learning to use AWS can seem overwhelming at first, especially with so many interrelated services and concepts. Here are some key terms you‘ll need to know:

• Regions and Availability Zones: AWS has a global infrastructure broken up into regions (physical locations like us-east-1) and Availability Zones (isolated data centers within regions). AWS has 26 launched regions with 84 AZs as of July 2022, far more than any other provider (AWS Global Infrastructure).

• VPC: Virtual Private Cloud, a logically-isolated section of the AWS cloud where you launch resources in a virtual network you define.

• EC2: Elastic Compute Cloud, AWS‘s flagship service that provides resizable virtual machines in the cloud.

• S3: Simple Storage Service, an object storage service that provides scalable storage in the cloud.

• IAM: Identity and Access Management, the service that controls access to AWS resources through users, groups, and permissions.

• Lambda: AWS‘s serverless compute platform that lets you run code without provisioning servers.

• CloudFormation: AWS‘s infrastructure as code service that lets you model and set up AWS resources using declarative templates.

We‘ll dive deeper into AWS OpsWorks later, but these core services and concepts form the foundation that OpsWorks and other higher-level services build on top of. Having a solid understanding of the AWS basics will make it easier to learn and utilize the more advanced services.

Why AWS for AI/ML Workloads

While AWS provides value for hosting any type of workload, it has become especially popular for AI, machine learning, and data science applications. This is due to a combination of powerful managed services and flexible infrastructure options that make it easier to build and scale intelligent applications.

According to Gartner, AWS is the leader in cloud AI developer services, ahead of Microsoft Azure and Google Cloud (Gartner 2021 Magic Quadrant for Cloud AI Developer Services). Customers like Airbnb, Lyft, Intuit, Tinder, Yelp, and the NFL are using AWS to power ML use cases from personalization and forecasting to computer vision and natural language processing.

One of the key considerations for AI/ML workloads is latency. ML models, especially deep learning neural networks, can be very computationally intensive and require processing huge amounts of data for training and inference. Reducing latency is critical to providing real-time experiences powered by ML, like recommendation engines, chatbots, and fraud detection.

AWS provides several solutions to minimize latency for AI/ML:

1. Powerful GPU instances: AWS offers EC2 instance types with up to 8 NVIDIA A100 Tensor Core GPUs and 400 Gbps networking for machine learning training and inference. Using GPUs can dramatically speed up computation of large ML models.

2. Elastic Inference: Elastic Inference allows you to attach low-cost GPU acceleration to any EC2 instance for ML inference. This makes it more cost-effective to scale ML-powered applications.

3. Inferentia: AWS Inferentia is a custom ML inference chip that provides high performance and low latency at a lower cost per inference than comparable GPU instances. Inferentia is available on EC2 Inf1 instances and Amazon SageMaker.

4. SageMaker Edge Manager: SageMaker Edge Manager optimizes ML models to run on edge devices like smart cameras and robots. This enables low-latency inference close to the data source, reducing bandwidth requirements.

In addition to these purpose-built solutions, the general best practices we covered before for reducing latency – like choosing regions close to users, distributing across AZs, and caching data – still apply to AI/ML workloads. By combining powerful hardware options with a global infrastructure footprint and intelligent architectures, AWS helps companies deploy fast, scalable, and cost-effective AI/ML applications.

AWS AI/ML Services

Another major draw to AWS for AI/ML is the breadth of managed services that make it easy to add intelligent features without needing deep data science expertise. Here are some of the key services in the AWS AI/ML portfolio:

• SageMaker: A fully-managed machine learning platform that spans the ML workflow from data labeling to model training, tuning, and deployment. SageMaker lets data scientists and developers quickly build, train and deploy ML models at scale.

• Textract: Automatically extracts text, handwriting, and structured data from scanned documents using computer vision and ML.

• Rekognition: A computer vision service that can identify objects, people, text, activities, and inappropriate content in images and videos.

• Comprehend: A natural language processing (NLP) service that finds insights and relationships in text through entity recognition, sentiment analysis, language detection, and more.

• Forecast: Time-series forecasting service that uses ML to deliver highly accurate forecasts from historical data.

• Personalize: Builds real-time recommendations and personalization into applications using the same ML technology as Amazon.com.

• Polly: Turn text into lifelike speech with deep learning-powered text-to-speech.

This is just a small sampling of the AI services AWS offers. There are also services for conversational AI, document analysis, business metrics anomaly detection, fraud detection, and more. The unifying theme of these services is that they allow companies to leverage pre-trained models to quickly add intelligent features without investing in developing ML expertise in-house.

For companies that need more customization than the AI services provide, AWS offers platforms like SageMaker to build and deploy models, along with infrastructure for running ML workflows like Deep Learning AMIs and containers. Taken together, AWS provides an unmatched platform for organizations of all types to integrate AI/ML into their applications and processes.

Getting Started with AWS

Convinced that AWS is the place to be for aspiring cloud and AI/ML professionals? The first step is diving in and getting hands-on experience with the core AWS services. The best way to do this is by signing up for the AWS Free Tier, which provides free access to over 100 AWS services for up to 12 months. This will let you experiment with EC2, S3, IAM, and more without incurring any costs.

Once you have an account, start working through online labs and tutorials to learn the basics of launching EC2 instances, storing data in S3, and controlling access with IAM. AWS provides several free digital training resources:

• AWS Skill Builder: 500+ free digital courses on topics ranging from cloud basics to advanced networking, security, ML, and more. Includes hands-on labs and whitepapers.

• AWS Well-Architected Labs: A set of hands-on labs for learning to build applications following AWS best practices across key areas like security, reliability, performance, and cost optimization.

• AWS Workshops: Self-paced workshops with hands-on tutorials for learning specific AWS services like EC2, Lambda, SageMaker, etc.

As you progress, pursue an AWS Certification to validate your expertise. AWS offers 11 certifications across four learning paths: foundational, architect, operations, and developer. The foundational AWS Certified Cloud Practitioner is a good starting point, and then you can specialize with an Associate or Professional level cert.

"The value in AWS certifications is in demonstrating to current and future employers that you have a specific set of skills, knowledge and expertise in cloud computing. And perhaps more importantly, in going through the certification process, you are expanding your knowledge of the AWS cloud, which can help further your career."

• Maureen Lonergan, Vice President of AWS Training and Certification

Beyond the digital training, nothing beats real-world experience. Dive in and start building your own cloud applications on AWS with services like EC2, Lambda, S3, and DynamoDB. Contribute to open source projects built on AWS, or find opportunities to work on cloud migration or development projects at your current company. The key is to constantly challenge yourself and get exposure to new services and architectures.

Finally, tap into the wealth of community resources out there. Follow the official AWS Blog and subscribe to AWS What‘s New for the latest news and announcements. Join local AWS Meetups or the AWS Innovate virtual conference to learn from other cloud pros. Ask questions on the AWS forums or Stack Overflow when you get stuck.

Conclusion and Next Steps

Cloud computing, and AWS in particular, has become an absolutely essential skill set for modern technology professionals. As more companies move to the cloud and look to integrate AI/ML into their applications, the demand for these skills will only increase – as will the career opportunities for those who have them.

"AWS is ever-evolving. When you have your core fundamentals in and the understanding of the moving parts in AWS, the learning becomes easier. We are in a cloud generation—I truly see a long and sustainable career by learning AWS."

• Trushna Karkhanis, Software Development Manager at Amazon Web Services

Getting started with AWS can seem daunting given the sheer number of services and concepts you need to learn. But by understanding the key building blocks and diving in with hands-on experience through the AWS Free Tier and digital training resources, you can steadily build your skills and accelerate your career.

So what are you waiting for? Sign up for your AWS account, start the Cloud Practitioner learning path, and begin your journey to becoming an AWS and cloud computing expert today!