About AWS and Machine Learning (SageMaker and Bedrock)
Amazon Web Services (AWS) stands as the premier cloud platform, providing a powerful environment for developers to build top-tier, scalable applications. When utilized correctly, cloud-based applications can typically deliver around 50% cost savings compared to self-hosting solutions.
AWS Machine Learning and AI, implemented as SageMaker and Bedrock, is the leading AI application platform for building industrial AI applications.
To fully leverage these benefits, it is essential to have a thorough understanding of AWS services and best practices. This course is designed for software developers, architects, project leaders, machine learning and AI specialists, and professionals, focusing on architecting and maintaining AWS-based Machine Learning and AI solutions. It includes a balanced mix of 50% lectures and 50% hands-on implementation labs within the AWS environment.
AWS Machine Learning and AI Cert helps participants achieve the level of knowledge needed for certification.
Beyond that, this 5-day course gives a good understanding of Machine Learning and AI. Official courses usually teach only the AWS technology for ML and AI, but leave the students to their own devices as far as core knowledge is concerned. By contrast, this course supplies the missing foundation.
Amplified by official AWS labs and by leading-edge AI technologies, this course not only helps achieve the certification, but goes beyond, by making the participants into experts in completing ML and AI projects.
Audience:
• Software developers
• Architects
• Project leaders
• Machine learning and AI specialists
Prerequisites:
• Familiarity with programming in at least one language
• Be able to navigate Linux command line
• Basic knowledge of command line Linux editors (VI / nano)
• Helpful: Basic understanding of AWS Cloud infrastructure (Amazon S3 and CloudWatch)
Lab environment:
• Lab AWS environment will be provided for students.
Zero Install: There is no need to install software on students’ machines.
Course Outline
Day 1
• Introduction
− Overview of Cloud Computing
− Benefits of Cloud over On-Premises Solutions
• What is ML and AI today
− Evolution of ML and AI
− Key Principles and Practices
• AWS Overview
− Regions and AZs
− Foundational Services
− AWS Command Line Tools (AWS CLI), install and use
• Introduction to Amazon SageMaker
− Amazon SageMaker and Jupyter notebooks
− Lab: Introduction to Amazon SageMaker
• Converting a business problem into an ML problem
• Demo: Amazon SageMaker Ground Truth – Human in the Looop
• Lab: Amazon SageMaker Ground Truth
Day 2
• ML and AI implementation
– Bedrock and SageMaker capabilities and algorithms for model building and deployment.
– AWS data storage and processing services for preparing data for modeling.
• Preprocessing
• Data collection and integration
− Lab: Data Preprocessing (including project work)
• Model preparation
− Overview of algorithms and choosing the right algorithm
− Loss functions and gradient descent
− Formatting and splitting your data for training
− Demo: Create a training job in Amazon SageMaker
• Model Training
− Classification models
− Regression models
− Lab: Model training
• Feature Engineering and Model Tuning
− Feature extraction, selection, creation, and transformation
− Hyperparameter tuning
• Demo: SageMaker hyperparameter optimization
Day 3
• Practice model training and evaluation
− Lab 3: Model Training and Evaluation (including project work)
• Deploying applications and infrastructure on AWS
– Monitoring tools for logging and troubleshooting ML and AI systems
– AWS services for the automation and orchestration of CI/CD pipelines
– AWS security best practices for identity, access management, encryption, and data protection
– Lab
Day 4 – Certification exam specifics
• Exam-specific areas
• Fundamentals of AI and ML
• Fundamentals of Generative AI
• Applications of Foundation Models
• Guidelines for Responsible AI
• Security, Compliance, and Governance for AI Solutions
Day 5
• Machine Learning and AI in more detail
− Amazon Augmented AI (Amazon A2I), Bedrock, Comprehend, & Fraud Detector
− Amazon Kendra, Lex, Personalize, Polly, Q, & Rekognition
− Amazon SageMaker, Textract, Transcribe, & Translate
• Security, Identity, and Compliance
− AWS Artifact, Audit Manager, Identity and Access Management (IAM)
− Amazon Inspector, Key Management Service (AWS KMS), Macie, AWS Secrets Manager
• Analytics
− AWS Data Exchange, Amazon EMR, AWS Glue
− AWS Glue DataBrew, Lake Formation, OpenSearch Service, QuickSight, & Redshift
• Security, Compliance, and Governance for AI Solutions
− AWS services and features to secure AI systems
− Source citation and documenting data origins
− Best practices for secure data engineering
− Security and privacy considerations for AI systems