Level Up Your Machine Learning Skills with New AWS Course
Machine learning (ML) continues to transform how organisations solve complex challenges—from personalised customer experiences to operational efficiency at scale. But developing robust, scalable, and production-ready ML solutions requires more than just good algorithms.
If you’re ready to take your machine learning skills to the next level and engineer real-world ML systems, AWS has a new virtual, instructor-led course designed just for you.
Introducing: Machine Learning Engineering on AWS
This intermediate-level, three-day course—Machine Learning Engineering on AWS—is tailor-made for ML professionals who want to deepen their understanding of how to build, deploy, orchestrate, and operationalise ML solutions on the AWS Cloud.
Whether you’re an aspiring machine learning engineer or a DevOps professional looking to expand your ML know-how, this course will guide you through each critical step of the ML engineering lifecycle.
The curriculum blends theory with practice, combining presentations, live demonstrations, and hands-on labs to ensure you walk away with practical, job-ready skills.
You’ll explore powerful tools like Amazon SageMaker and Amazon EMR, and learn to transform data, select models, train algorithms, and deploy them at scale using AWS’s modern cloud-native infrastructure.
Aligned with the New AWS ML Engineer Associate Certification
This course is also directly aligned with the AWS Certified Machine Learning Engineer – Associate, launched in 2024.
If you’re looking to validate your ability to design and deploy production-grade ML solutions on AWS, this course is an ideal step toward certification.
The hands-on labs and real-world scenarios will help reinforce the practical skills needed to succeed in the exam and in your day-to-day role.
What You’ll Learn
By the end of the course, you’ll be equipped to:
- Understand ML fundamentals and how they apply within AWS
- Prepare and transform data for ML using services like SageMaker Data Wrangler and AWS Glue
- Choose and tune models using SageMaker’s built-in algorithms and tools like Autopilot
- Deploy models effectively with considerations for scalability, cost, and security
- Automate ML pipelines with MLOps best practices including CI/CD and monitoring
- Detect data drift and implement remediation strategies using SageMaker Model Monitor.
Who Should Attend?
This course is ideal for:
- ML engineers and data scientists with a basic understanding of machine learning concepts and Python
- Developers and DevOps professionals looking to move into ML engineering
- Anyone working with ML workflows on AWS who wants to build production-grade systems
Basic familiarity with AWS services, Git, and common Python libraries like Pandas and Scikit-learn is recommended to get the most from the experience.
Why Train with Bespoke?
At Bespoke, we specialise in helping tech teams build future-ready skills through hands-on, instructor-led AWS training.
Our experienced trainers bring real-world insights into every session, making complex topics approachable and applicable. We offer flexible delivery options to suit your team’s schedule—online or face-to-face.
If you’re serious about growing your ML capability and earning a certification that proves it, this course is your next step. Register your interest with Bespoke, and keep an eye out for the first course date coming soon!
