Overview of AWS Machine Learning
The AWS Machine Learning Training course is designed to provide participants with a comprehensive understanding of machine learning concepts and techniques using AWS services. Through a combination of lectures, hands-on exercises, and case studies, participants will learn how to build, train, and deploy machine learning models on the AWS platform.
The course covers a range of topics, including AWS services for machine learning, data preprocessing, feature engineering, model selection, model training, hyperparameter tuning, and deployment. Participants will also learn how to evaluate and monitor machine learning models, and how to use AWS tools for data visualization, model interpretation, and performance optimization.
Stalwart Learning is committed to providing practical and hands-on training, and this course is no exception. Participants will have access to a variety of AWS tools and services throughout the training, including Amazon SageMaker, Amazon Rekognition, Amazon Comprehend, and Amazon Personalize. They will also engage in hands-on exercises and use cases to reinforce their learning and practical application of AWS machine learning concepts and techniques.
The course is suitable for individuals with basic knowledge of Python programming and familiarity with AWS services. It is ideal for data scientists, machine learning engineers, software developers, and IT professionals who want to build their skills in machine learning on the AWS platform.
By the end of the training, participants will have a solid understanding of AWS machine learning best practices, services, and tools, and be well-prepared to apply their knowledge to real-world scenarios. They will also be prepared to take the AWS Certified Machine Learning – Specialty certification exam.
Duration
24-40 Hours
Module 1: Introduction to AWS Machine Learning
- Introduction to machine learning concepts and techniques
- Overview of AWS machine learning services and features
- Understanding the AWS machine learning ecosystem
- Exploring real-world applications of AWS machine learning
Module 2: AWS Machine Learning Tools and Services
- Introduction to Amazon SageMaker for building and deploying machine learning models
- Utilizing AWS DeepLens for deep learning applications
- Implementing Amazon Rekognition for image and video analysis
- Exploring other AWS machine learning services such as Amazon Comprehend, Amazon Polly, and Amazon Lex
Module 3: Data Preparation and Feature Engineering
- Data collection and preprocessing techniques for machine learning
- Exploratory data analysis (EDA) using AWS tools
- Feature selection and engineering with AWS services
- Data labeling and annotation for supervised learning
Module 4: Model Training and Evaluation
- Choosing appropriate algorithms and models for different tasks
- Training machine learning models with Amazon SageMaker
- Evaluating model performance and accuracy
- Implementing hyperparameter tuning for model optimization
Module 5: Model Deployment and Management
- Deploying machine learning models with Amazon SageMaker
- Implementing inference pipelines and batch processing with AWS services
- Monitoring and managing deployed models using AWS tools
- Model retraining and versioning in production environments
Module 6: Advanced Machine Learning Techniques
- Deep learning with AWS services such as Amazon SageMaker and AWS DeepLens
- Natural Language Processing (NLP) with Amazon Comprehend and Amazon Lex
- Time Series Forecasting with Amazon Forecast
- Reinforcement Learning with AWS RoboMaker and Amazon SageMaker RL
Module 7: Model Interpretability and Explainability
- Techniques for interpreting and explaining machine learning models
- Utilizing AWS services for model interpretability and explainability
- Addressing biases and fairness in machine learning models
- Ensuring ethical and responsible AI practices
Module 8: Model Deployment in Production Environments
- Implementing scalable and reliable model deployment architectures
- Infrastructure considerations for deploying machine learning models
- Integration of machine learning models with other AWS services
- Managing security and compliance in production ML systems
Module 9: AWS Machine Learning Best Practices
- Designing end-to-end machine learning solutions with AWS
- Scaling and optimizing machine learning workflows
- Ensuring data privacy and protection in machine learning projects
- Applying industry best practices for successful AWS machine learning implementations
Module 10: AWS Machine Learning Exam Preparation
- Reviewing exam objectives and test-taking strategies
- Practicing with sample questions and scenarios
- Final exam review and preparation