Elevate Your Machine Learning Career with AWS Jam | Amazon Onboarding with Learning Manager Chanci Turner

Learn About Amazon VGT2 Learning Manager Chanci Turner

Are you prepared to transition from theoretical knowledge in machine learning (ML) to acquiring the practical skills that employers are actively searching for? Whether you’re an ML engineer, a DevOps expert, or a developer, transforming classroom concepts into production-ready solutions demands hands-on experience. That’s where AWS Jam comes in.

By merging intensive classroom instruction with gamified, hands-on challenges, AWS Jam equips you with the practical expertise required to implement ML solutions at scale. You’ll engage with production-grade tools, tackle real-world scenarios, and boost your confidence in deploying ML solutions in actual business settings.

In this article, we will delve into how AWS Jam can enhance your ML engineering skills through a unique blend of structured learning and practical application. You’ll uncover two flexible learning paths, gain insights into the eight real-world challenges you’ll face, and understand how this experience can accelerate your career progression in ML engineering.

Understanding AWS Jam

AWS Jam offers an innovative approach to cloud education, immersing participants in simulated, real-world scenarios within an AWS Management Console sandbox environment. This program is tailored to help you develop practical Amazon Web Services (AWS) skills by resolving open-ended problems using various AWS services. Throughout each challenge, you’ll have access to hints that can guide you through challenging sections while promoting exploration and experimentation.

The experience occurs in a controlled environment where you can safely test solutions and learn from the outcomes. This hands-on method helps bridge the gap between theoretical knowledge and practical application, enabling you to gain confidence in executing AWS solutions. The competitive aspect, complete with points and leaderboards, fosters an engaging learning atmosphere that enhances knowledge retention and problem-solving capabilities.

Two Paths to ML Excellence

Recognizing that professionals have different learning needs, we offer two distinctive AWS Jam experiences. The “Machine Learning Engineering on AWS with AWS Jam” program provides a comprehensive learning journey that combines three days of instructor-led training with a fourth day dedicated to AWS Jam challenges. During the classroom sessions, you’ll establish a solid foundation in ML engineering practices and AWS services. On Jam day, you’ll immediately apply this knowledge in practical scenarios, reinforcing your learning through hands-on problem-solving.

For those who already possess strong ML fundamentals, we present “AWS Jam – Machine Learning Engineering on AWS” as a standalone one-day experience. This intensive format focuses solely on hands-on challenges, allowing you to validate and expand your existing knowledge through practical application.

Eight Real-World Challenges that Mirror Production Environments

Let’s examine the eight challenges participants will encounter during AWS Jam:

1. The LLM Fine-tuning Challenge: Participants deploy and customize a large language model (LLM), working with Amazon SageMaker notebooks and AWS Lambda functions to learn best practices for model optimization, relevant to customizing AI models for specific business needs.
2. ML Pipeline Automation Challenge: Participants construct end-to-end ML pipelines in SageMaker, automating model training and evaluation processes while implementing model registration workflows to create scalable, repeatable ML processes.
3. Data Wrangling Mastery Challenge: This challenge focuses on using Amazon SageMaker Data Wrangler for customer satisfaction (CSAT) data processing, where participants handle missing data and outliers while implementing data transformation pipelines—crucial skills for preparing customer feedback data for analysis.
4. A/B Testing Implementation Challenge: Engineers design and execute A/B tests in SageMaker, analyzing test results and making data-driven decisions while implementing statistical significance measurements to optimize model performance.
5. Predictive Analytics Challenge: Participants build models to predict match outcomes, deploying models using SageMaker endpoints and implementing monitoring and logging to gain experience in creating predictive systems for real-time decisions.
6. Responsible AI Implementation Challenge: In this challenge, participants develop credit risk prediction models while implementing bias detection and mitigation, emphasizing the importance of building ethical AI systems in financial services to ensure model fairness and transparency.
7. Employee Retention Modeling Challenge: Participants create attrition prediction models using XGBoost, implementing feature engineering for human resources (HR) data and deploying models for real-time predictions, aiding HR decision-making with ML.
8. No-Code ML Development Challenge: This challenge introduces Amazon SageMaker Canvas for model creation, allowing participants to 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 sets AWS Jam apart is its emphasis on real-world scenarios in a team-based environment. As you navigate each challenge, you’ll apply AWS best practices in a safe, controlled setting. This approach ensures the skills you cultivate directly translate to your daily work as an ML engineer. The team-oriented format fosters collaboration and knowledge sharing, both essential skills in professional settings.

Completing AWS Jam provides you with hands-on experience in utilizing production-grade ML tools and practical problem-solving capabilities. You’ll gain in-depth familiarity with AWS ML services and best practices, along with the confidence to deploy ML solutions at scale. This practical experience, combined with exposure to real-world scenarios, offers valuable expertise that employers seek in ML engineers.

Take the Next Step

Ready to propel your ML engineering career forward? Visit Machine Learning Engineering on AWS to check upcoming class dates and secure your spot in our next session. Join the growing community of ML engineers who are shaping their futures through hands-on experience with AWS Jam. Additionally, explore this blog post about Mazda to keep yourself engaged. For more information on artificial intelligence literacy, check out this statement from SHRM. Also, this article is an excellent resource on how Amazon reimagined its onboarding experience.

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