AI Engineering Bootcamp: Build, Train & Deploy Models with AWS SageMaker

What Does an AI Engineer Do?

In a nutshell, an AI Engineer oversees the complete development process of an AI application, leveraging artificial intelligence at its core. They adapt various AI models, including Large Language Models, to meet specific requirements.

(For a more detailed explanation, check out our blog post here)

This role encompasses a range of tasks from constructing models with specialized datasets to training, fine-tuning, deploying, and scaling these models with cloud solutions.

The demand for this profession is skyrocketing, and it continues to evolve alongside the changing AI landscape.

Introducing AWS SageMaker

AWS SageMaker, also known as Amazon SageMaker, is a fully managed service that enables you to swiftly build, train, and deploy machine learning models on a large scale. By relieving you of the burdens of infrastructure management, it allows you to focus on the exciting aspects of creating your unique AI projects!

In summary, it stands out as a premier AI tool widely used by AI Engineers, Machine Learning Engineers, Developers, and Data Scientists.

Curious to know what makes AWS SageMaker exceptionally appealing?

It offers a seamless end-to-end machine learning experience that's accessible regardless of your expertise level!

Whether you're a veteran in AI or a novice, SageMaker features intuitive tools and an easy-to-navigate interface, making machine learning achievable for everyone.

If you aspire to design and implement your own AI applications, this is the perfect starting point.

Why Choose This Course?

Simply put, it's the top-notch, most relevant, and hands-on AI Engineering Bootcamp course available online, designed to impart practical AWS SageMaker skills while enabling you to gain real-world application experience.

But of course, we might be a little biased. Here’s a detailed breakdown of this AWS SageMaker course to help you form your own opinion:

1. Introduction: Start off with an overview of the course structure, meet your fellow students, and get introduced to your exceptional instructor: Senior Machine Learning Engineer, Patrik Szepesi!

2. Configuring Our AWS Environment and Best Practices: Jump right in by setting up your AWS account, establishing secure IAM roles, and learning best practices. You'll also discover how to set up the AWS SageMaker domain, modify UI settings, and decode SageMaker Studio's pricing models.

3. A Friendly Introduction to HuggingFace in Amazon SageMaker: Gain hands-on experience integrating HuggingFace with AWS SageMaker. This section covers how to set up SageMaker with PyTorch and deploy pre-built HuggingFace models for tasks like sentiment analysis, including autoscaling considerations.

4. Collecting a Dataset for Our Multiclass Text Classification Task: Dive into dataset sourcing and preparation for a multiclass text classification project. You'll learn to source datasets, create S3 buckets, and effectively upload data to AWS.

5. Exploratory Data Analysis: Engage in exploratory data analysis to unveil insights from your datasets, covering data visualization practices and techniques to identify patterns that may inform subsequent modeling decisions.

6. Preparing Our Training Notebook: This practical segment guides you in setting up training environments in Amazon SageMaker, including notebook configuration and Python scripting for HuggingFace estimators, essential for efficient model training!

7. Discovering Tokenizations and Encodings: This exciting section explores crucial NLP processes like tokenization and encoding, foundational to AI models like LLMs (Large Language Models). You'll engage in hands-on exercises to create notebooks, grasp tokenization vocabulary, and see these techniques in action during model training.

8. Setting Up Data Loading with PyTorch: Learn to create dataset loader classes and configure PyTorch DataLoader, pivotal aspects of efficiently managing vast amounts of data during model training.

9. Choose Your Journey: A brief pause to prepare you for advanced topics ahead, helping you establish your path in exploring deeper technical aspects of machine learning and AI models. Decide whether you want to take a deep dive into the mathematics involved or not (we understand that math isn't everyone’s favorite!).

10. Mathematics Behind Large Language Models and Transformers: This thorough section equips you with the mathematical fundamentals and functions of Transformers and Large Language Models, including multi-head attention, positional encodings, and the theory behind attention mechanisms.

11. Tailoring Our Model Architecture in PyTorch: Learn to modify a DistilBERT model by incorporating dropout, linear layers, and ReLU functionalities, which is crucial for adapting the model to specific classification assignments and datasets.

12. Developing the Accuracy, Training, and Validation Function: Focus on crafting robust training and validation functions ensuring the effective assessment and operation of models under various conditions.

13. Optimizer Functions, Model Parameters, and Cross Entropy Loss: Delve into optimization functions, model parameter adjustments, and the intricacies of the cross entropy loss function, essential for training proficient machine learning models.

14. Managing Resource Limit Errors Prior to Training and Deployment: Prepare for potential stumbling blocks by learning how to tackle resource limit errors through effective AWS quota management and increase requests.

15. Initiating Our Training Job and Monitoring it in AWS CloudWatch: Things are starting to get thrilling! Here you'll commence your training jobs in SageMaker while learning to observe and troubleshoot these jobs via AWS CloudWatch, gaining insights into the performance and well-being of your models.

16. Deploying Our Multiclass Text Classification Endpoint in SageMaker: This essential section navigates through the deployment of the trained multiclass text classification model as an Amazon SageMaker endpoint, ensuring you comprehend the steps to render your models accessible for practical applications.

17. Load Testing Our Machine Learning Model: Just because a model is live doesn’t guarantee its functionality! You'll undertake load testing on deployed models to assess performance and scalability, setting the stage for robust and effective real-world deployments.

18. Production-Grade Deployment of Our Machine Learning Model: Stepping you through deploying your model on a production scale, including setting up AWS Lambda functions and API Gateway, and evaluating the deployment with tools like Postman. This is where theory turns into practice!

19. Tidying Up Resources: Finally, it's essential to wrap things up properly to embrace real-world best practices! This section emphasizes the need for tidying up AWS resources to control costs and sustain an organized cloud environment.

What Job Opportunities Will This Course Open Up?

The fields of AI and machine learning are incredibly sought after. If you're aiming to catch the AI wave, mastering SageMaker is a fantastic starting point. This skill is fundamental in various lucrative careers leading the charge in Artificial Intelligence, including:

AI Engineer & Machine Learning Engineer: Focus on designing, developing, and personalizing machine learning models, deploying them in production ecosystems. Skills in model training, enhancement, and deployment are essential.

AI Specialist: Specializes in crafting applications using AI technologies and machine learning models.

Data Scientist: Engages in scrutinizing and interpreting complex datasets to guide informed decision-making in businesses. Mandates expertise in data preparation, exploratory analysis, and model creation.

AI Research Scientist: Conducts investigations to progress the fields of artificial intelligence and machine learning. Knowledge of advanced machine learning theories, including attention mechanisms and large language models, is necessary.

Cloud Engineer: Concentrates on the architecture, planning, managing, sustaining, and supporting applications in the cloud. Requires familiarity with AWS offerings and deployment best practices.

DevOps Engineer: Bridges development and operations by automating software delivery processes and infrastructure modifications. Needs proficiency in deploying and monitoring ML models using AWS CloudWatch.

Software Engineer: Involves building software applications, which may include integrated machine learning components. Needs capabilities in merging ML models with applications to guarantee their scalability and performance.

Data Engineer: Responsible for constructing and upkeeping data pipelines, ensuring data is accurate, reliable, and ready for analytics. Requires proficiency in data storage solutions like AWS S3 and preparation methods.

Technical Product Manager: Supervises the creation and deployment of technology products, including those involving machine learning. An understanding of the technical facets of machine learning deployment and monitoring is crucial.

What Else Should You Be Aware Of?

By becoming a ZTM member, you'll gain access to all of our bootcamp courses, projects, and resources.

Additionally, you can join our exclusive live online community classroom to learn alongside thousands of fellow students, alumni, mentors, TAs, and instructors.

Most importantly, you’ll be learning from an industry expert (Patrik) with real-world experience as an AI & Machine Learning Engineer. He shares the specific strategies and techniques he employs in his position.

Lastly, like all ZTM courses, this course is always evolving. It will be refreshed consistently in line with changing industry standards, making it your go-to guide for effectively using Amazon SageMaker now and throughout your career.

Join thousands of Zero To Mastery graduates who have secured jobs at top companies like Google, Tesla, Amazon, Apple, IBM, JP Morgan, Facebook, Shopify, and more.

Our graduates come from varied backgrounds, ages, and experiences, with many starting as complete novices.

So, there’s no reason why you can't join them.

And you risk nothing. You can start learning immediately, and if this course isn't what you anticipated, we’ll refund your money 100% within 30 days. No strings attached.