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
    • 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
USD $2,995
Online Virtual Class
Scheduled
Date: Jul 1 - 5, 2024
Time: 10 AM - 6 PM ET
USD $2,995
Online Virtual Class
Scheduled
Date: Aug 26 - 30, 2024
Time: 10 AM - 6 PM ET