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Machine Learning Engineer
Artificial intelligence and machine learning (AI/ML) are on the rise. In this course, you will spend a day in the life of a data scientist so that you can efficiently collaborate with data scientists and build applications that integrate with ML. You will learn the basic process that data scientists use to develop ML solutions on Amazon Web Services (AWS) using Amazon SageMaker. You will experience the steps to build, train, and deploy an ML model through instructor-led demonstrations and exercises.
1. introduction to machine learning
- Advantages of machine learning (ML)
- Types of ML approaches
- Framework for the business problem
- Quality of the forecast
- Processes, roles and responsibilities for ML projects
2. prepare a data record
- Data analysis and preparation
- Tools for data preparation
- Demonstration: Review of Amazon SageMaker Studio and notebooks
- Practical exercise: Data preparation with SageMaker Data Wrangler
3. training a model
- Steps for training a model
- Selecting an algorithm
- Training the model in Amazon SageMaker
- Practical exercise: Training a model with Amazon SageMaker
- Amazon CodeWhisperer
- Demonstration: Amazon CodeWhisperer in SageMaker Studio Notebooks
4. evaluation and coordination of a model
- Evaluation of the model
- Model tuning and hyperparameter optimization
- Practical exercise: Model tuning and hyperparameter optimization with Amazon SageMaker
5. inserting a model
- Model use
- Practical exercise: Deploying a model on a real-time endpoint and generating a prediction
6. operational challenges
- Responsible ML
- ML team and MLOps
- Automation
- Monitoring
- Updating the models (model testing and provision)
7 Other tools for model creation
- Different tools for different skills and business requirements
- Code-free ML with Amazon SageMaker Canvas
- Demonstration: Overview of Amazon SageMaker Canvas
- Amazon SageMaker Studio Lab
- Demonstration: Overview of the SageMaker Studio Lab
- (Optional) Practical exercise: Integrating a web application with an Amazon SageMaker Model endpoint
- Discuss the benefits of different types of machine learning for solving business problems.
- Describe the typical processes, roles and responsibilities in a team that develops and deploys ML systems.
- Explain how data scientists use AWS tools and ML to solve a common business problem.
- Summarize the steps a data scientist takes to prepare data.
- Summarize the steps a data scientist takes to train ML models.
- Summarize the steps a data scientist takes to evaluate and tune ML models.
- Summarize the steps to deploy a model to an endpoint and make predictions.
- Describe the challenges of operationalizing ML models.
- Matching AWS tools with their ML function.
This course allows you to try out new skills and apply your knowledge to your working environment through a variety of practical exercises.
This course is aimed at data science professionals, machine learning professionals, developers developers and engineers engineers as well as system architects.
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