Master the AWS AI and MLOps ecosystem. Deploy models with SageMaker, build Gen AI applications with Bedrock, set up production ML pipelines, and prepare for AWS AI certifications.
End-to-end ML on SageMaker: Studio notebooks, training jobs, built-in algorithms, custom containers, hyperparameter tuning, and SageMaker Pipelines for automated workflows.
Build generative AI applications with Amazon Bedrock. Access foundation models (Claude, Llama, Titan), implement RAG with Knowledge Bases, and deploy AI agents.
Serverless ML inference with AWS Lambda. Package models, manage dependencies, handle cold starts, set up API Gateway, and build cost-effective real-time prediction endpoints.
Design and build ML data lakes on S3. Data organisation, partitioning, Lake Formation, Glue ETL, Athena querying, and building feature stores for ML pipelines.
Orchestrate complex ML workflows with Step Functions. Training, evaluation, approval gates, deployment, and rollback -- all as serverless state machines.
Monitor models in production with SageMaker Model Monitor. Detect data drift, concept drift, bias, and set up alerts. CloudWatch dashboards and automated retraining triggers.
Control AWS AI costs. Spot instances for training, right-sizing endpoints, auto-scaling, Savings Plans, and FinOps practices specifically for ML workloads.
Targeted preparation for the AWS Machine Learning Specialty and AWS AI Practitioner certifications. Exam strategies, practice questions, and knowledge gap analysis.
Cloud engineers and architects who want to specialise in AI and ML workloads on AWS
Data scientists and ML engineers looking to deploy models professionally on AWS infrastructure
Technical professionals preparing for AWS AI and Machine Learning certification exams
Basic cloud knowledge required. You should be familiar with AWS fundamentals (EC2, S3, IAM) and have basic Python skills. Some ML understanding is helpful but not strictly required.
Interactive sessions with real-time Q&A and screen sharing
All sessions recorded and available for 12 months after the course
Real-world projects that build your portfolio as you learn
Personal mentoring sessions to address your specific questions
Industry-recognised certificate upon successful completion
Our instructors are seasoned practitioners with years of experience building production AI systems. They hold certifications across major cloud platforms and have trained thousands of professionals worldwide.
Yes. You will need an AWS account with billing enabled. Most labs stay within Free Tier limits, but plan for approximately 50-100 pounds in AWS costs over the 6 weeks. We provide cost-saving tips throughout.
Yes. The final module is dedicated to certification preparation for the AWS Machine Learning Specialty exam. The entire course content aligns with the certification domains.
Choose based on which cloud your organisation uses or which you plan to work with. The concepts transfer well between clouds. If unsure, AWS has the largest market share for ML workloads.
No, and we cover alternatives. Lambda for inference, ECS/EKS for containerised models, and Bedrock for Gen AI. SageMaker is the most comprehensive, but knowing when to use simpler services is equally important.