WA3293
Applied Data Science and Practical Machine Learning with AWS SageMaker and AutoML Training
This course teaches students essential skills and knowledge to develop and deploy cutting-edge machine learning models. Students will learn machine learning fundamentals, work with the latest tools and techniques, and apply their skills in real-world applications.
Course Details
Duration
5 days
Prerequisites
- Proficiency in Python programming
- Understanding of data analysis and manipulation techniques
- Familiarity with Python Pandas or Numpy is recommended
- Basic knowledge of machine learning concepts, algorithms, and model evaluation
- Familiarity with AWS, and some experience with S3, IAM, and EC2 services
Target Audience
- Data Scientists
- Machine Learning Engineers
- Data Analysts
- Other professionals with a data analysis background looking to expand their machine learning and cloud-based deployment skills.
- Those interested in applying AutoML techniques for faster model development and deployment
Skills Gained
- Understand the data science life cycle
- Set up a SageMaker environment
- Train and evaluate ML models using SageMaker
- Deploy ML models
- Work with an AWS AutoML or auto-sklearn environment
- Work with ML models using H2O's automated machine learning
- Understand neural networks and deep learning
Course Outline
- Chapter 1. Data processing phases and the data science life cycle
- Introduction to the data science life cycle
- Data exploration and visualization
- Data cleaning and preprocessing
- Feature engineering
- Model selection and evaluation
- Tuning ML: data, parameters, hyperparameters, and artifacts
- MLI, tuning through data selection/enrichment, analyzing the manifold
- MLI tools and techniques
- Chapter 2. Working with ML algorithms on SageMaker
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- Introduction to SageMaker
- Setting up a SageMaker environment
- Training and evaluating ML models using SageMaker's built-in algorithms
- Fine-tuning ML models using SageMaker's hyperparameter tuning
- Chapter 3. Deploying ML models as executable artifacts
- Introduction to deploying ML models as executable artifacts
- Creating and deploying ML models as REST APIs using SageMaker
- Updating and serving ML models using SageMaker's A/B testing and blue/green deployments
- Chapter 4. AWS AutoML and auto-sklearn
- Introduction to Canvas and AWS AutoML
- Costs and examples
- AutoML as auto-hyperparameter tuning with auto-sklearn
- Setting up an AWS AutoML or auto-sklearn environment
- Training and evaluating ML models using AWS AutoML or auto-sklearn
- Fine-tuning ML models using AWS AutoML or auto-sklearn's hyperparameter tuning
- Chapter 5. Automated machine learning with H2O
- Fully automated ML (auto parameter tuning and auto feature engineering)
- H2O libraries, driverless AI, etc
- H2O automl vs auto-sklearn (libraries compared/side-by-side0
- Introduction to H2O and its automated machine learning capabilities
- Setting up an H2O environment (mention JRE req’s)
- Training and evaluating ML models using H2O's automated machine learning
- Fine-tuning ML models using H2O's hyperparameter tuning
- Chaper 6. Neural Networks (NN)
- Neural networks basics and intro
- NN’s as autoML
- Common NN topologies and applications (RNN, CNN, LSTM, etc)
- Thin layer NN, examples, and lab (using TF)
- Deep Learning
- Libraries (Keras vs. TF vs. pytorch)
Upcoming Course Dates
