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Applied Data Science and Practical Machine Learning with AWS SageMaker and AutoML

The course begins with thoroughly reviewing machine learning (ML) fundamentals, including exploratory data analysis, model building, and machine learning explainability. The class also covers the...

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$2,995 USD
Course Code WA3293
Duration 5 days
Available Formats Classroom, Virtual

The course begins with thoroughly reviewing machine learning (ML) fundamentals, including exploratory data analysis, model building, and machine learning explainability. The class also covers the latest automated machine learning tools and techniques, such as auto-sklearn, H2O, Auto-Keras, and AWS Auto Pilot. In addition, students also learn how and when to use AutoML for rapid development and when to train a model by hand. Students will then dive into Amazon Web Services (AWS) SageMaker to train, evaluate, and deploy models. Students will gain experience deploying and maintaining models in production environments. The course includes advanced topics, including neural networks, deep learning, transfer learning, and fine-tuning, focusing on practical applications with hands-on labs where students can hone their skills.

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

Who Can Benefit

This course is designed for data scientists, machine learning engineers, data analysts, and other professionals with a data analysis background looking to expand their machine learning and cloud-based deployment skills. It is also suitable for those interested in applying AutoML techniques for faster model development and deployment.

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

Course Details

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)
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