Transform your Machine Learning career through AWS Jam

Are you ready to move beyond theoretical machine learning (ML) knowledge and gain the practical skills employers are actively seeking? Whether you’re an ML engineer, DevOps professional, or developer, turning classroom concepts into production-ready solutions requires hands-on experience.

This is where AWS Jam comes in. By combining intensive classroom training with gamified hands-on challenges, AWS Jam provides the practical experience you need to implement ML solutions at scale. You’ll work with production-grade tools, tackle real-world scenarios, and build confidence in your ability to deploy ML solutions in actual business environments.

In this post, we explore how AWS Jam can transform your ML engineering capabilities through a unique combination of structured learning and practical application. You’ll discover two flexible learning paths, understand the eight real-world challenges you’ll tackle, and learn how this experience can accelerate your career growth in ML engineering

Understanding AWS Jam

AWS Jam represents an innovative approach to cloud learning where participants immerse themselves in simulated, real-world scenarios within an AWS Management Console sandbox environment. It’s designed to help you develop practical Amazon Web Services (AWS) skills by solving open-ended problems using various AWS services. During each challenge, you’ll have access to clues that can guide you through difficult sections while encouraging exploration and experimentation.

The experience takes place in a controlled environment where you can safely test solutions and learn from the results. This hands-on approach helps bridge the gap between theoretical knowledge and practical application, so you can build confidence in implementing AWS solutions. The competitive element, complete with points and leaderboards, creates an engaging learning environment that enhances knowledge retention and problem-solving skills.

Two paths to ML excellence

We understand that different professionals have different learning needs. That’s why we offer two distinct AWS Jam experiences. Machine Learning Engineering on AWS with AWS Jam provides a comprehensive learning journey that combines 3 days of instructor-led training with a fourth day dedicated to AWS Jam challenges. During the classroom portion, you’ll build a strong foundation in ML engineering practices and AWS services. During Jam day, you’ll immediately apply this knowledge in practical scenarios, reinforcing your learning through hands-on problem-solving.

For professionals who already possess strong ML fundamentals, we offer AWS Jam – Machine Learning Engineering on AWS as a standalone one-day experience. This intensive format focuses purely on hands-on challenges, during which you can validate and expand your existing knowledge through practical application.

Eight real-world challenges that mirror production environments

Let’s explore the eight challenges that participants will tackle during the AWS Jam:

• Challenge 1 – The LLM Fine-tuning challenge takes participants through deploying and customizing a large language model (LLM). Working with Amazon SageMaker notebooks and AWS Lambda functions, engineers learn best practices for model optimization that directly applies to customizing AI models for specific business needs.
• Challenge 2 – In the ML Pipeline Automation challenge, participants build end-to-end ML pipelines in SageMaker. They learn to automate model training and evaluation processes, implementing model registration workflows that create scalable, repeatable ML processes.
• Challenge 3 – The Data Wrangling Mastery challenge focuses on using Amazon SageMaker Data Wrangler for customer satisfaction (CSAT) data processing. Participants handle missing data and outliers while implementing data transformation pipelines—essential skills for preparing customer feedback data for analysis.
• Challenge 4 – During the A/B Testing Implementation challenge, engineers design and execute A/B tests in SageMaker. They analyze test results, make data-driven decisions, and implement statistical significance measurements to optimize model performance.
• Challenge 5 – The Predictive Analytics challenge involves building models to predict match outcomes. Participants deploy models using SageMaker endpoints and implement monitoring and logging, gaining experience in creating predictive systems for real-time decisions.
• Challenge 6 – In the Responsible AI Implementation challenge, participants develop credit risk prediction models while implementing bias detection and mitigation. This challenge emphasizes building ethical AI systems in financial services, ensuring model fairness and transparency.
• Challenge 7 – The Employee Retention Modeling challenge tasks participants with creating attrition prediction models using XGBoost. They implement feature engineering for human resources (HR) data and deploy models for real-time predictions, supporting HR decision-making with ML.
• Challenge 8 – The No-Code ML Development challenge introduces Amazon SageMaker Canvas for model creation. Participants implement ML solutions without coding and learn to share and deploy models across teams, supporting the democratization of ML across organizations.

Learning through competition

What makes AWS Jam distinctive is its focus on real-world scenarios in a team-based environment. As you work through each challenge, you’ll apply AWS best practices in a safe, controlled setting. This approach ensures that the skills you develop directly translate to your day-to-day work as an ML engineer. The team-based format encourages collaboration and knowledge sharing, essential skills in professional environments.Completing AWS Jam equips you with hands-on experience in production-grade ML tools and practical problem-solving skills. You’ll gain deep familiarity with AWS ML services and best practices, along with the confidence to implement ML solutions at scale. This practical experience, combined with exposure to real-world scenarios, provides valuable expertise that employers seek in ML engineers.

Take the next step

Ready to advance your ML engineering career? Visit Machine Learning Engineering on AWS to view upcoming class dates and secure your spot to enroll now in our next class. Join the growing community of ML engineers who are building their futures through hands-on experience with AWS Jam.

Additionally, follow the AWS Training and Certification Blog for our monthly course launch updates (published the last week of each month) and AWS Skill Builder for all the latest training offerings.