Course Overview:

This course covers the following topics:

Introduction to Amazon SageMaker Studio: An overview of SageMaker Studio’s features, including its integrated Jupyter notebooks, model debugging, and experimentation tools.

Data Preprocessing: Techniques for preparing and cleaning datasets for training and inference.

Model Building and Training: How to build and train machine learning models using SageMaker’s built-in algorithms and AutoML capabilities.

Model Deployment and Monitoring: Deploying models to SageMaker endpoints and monitoring their performance.

Continuous Integration/Continuous Deployment (CI/CD) Pipelines: Automating the process of deploying and updating models in production.

After completing this course, students will be able to:

  • Use Amazon SageMaker Studio IDE to develop, train, and deploy machine learning models.
  • Prepare and clean datasets for training and inference.
  • Build and train models with SageMaker's algorithms and AutoML capabilities.
  • Deploy models to SageMaker endpoints and monitor performance.
  • Automate model deployment with CI/CD pipelines.
  • Debug and experiment with models for improved performance.

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Key Features:

  • Hands-on labs for real-world application.

  • A comprehensive curriculum that covers all aspects of using SageMaker Studio.

  • Instruction from expert instructors.

  • A flexible schedule with online and in-person options.

  • A certification upon completion.

Who Should Attend:

  • Data scientists and machine learning practitioners
  • Professionals interested in Amazon SageMaker and AWS tools
  • Individuals looking to enhance their data science skills.

Prerequisites:

  • AWS Technical Essentials (1–day AWS instructor led course)

    • We recommend students who are not experienced data scientists complete the following two courses followed by 1-year on-the-job experience building models prior to taking this course:

  • The Machine Learning Pipeline on AWS (4–day AWS instructor led course)
  • Deep Learning on AWS (1–day AWS instructor led course)

    • Learning objective of the course:

    • Understand the key concepts of data science and machine learning.
    • Use Amazon SageMaker Studio for data exploration, model development, and model deployment.
    • Train and evaluate machine learning models using SageMaker algorithms and AutoML.
    • Deploy machine learning models to SageMaker endpoints for inference.
    • Monitor model performance using SageMaker's built-in monitoring tools.
    • Set up CI/CD pipelines to automate model deployment and updating.
    • Debug and experiment with models for improved performance.

    Course Outline: Download Course Outline

    • JupyterLab Extensions in SageMaker Studio
    • Demonstration: SageMaker user interface demo

    • Using SageMaker Data Wrangler for data processing
    • Hands-On Lab: Analyze and prepare data using Amazon SageMaker Data Wrangler
    • Using Amazon EMR
    • Hands-On Lab: Analyze and prepare data at scale using Amazon EMR
    • Using AWS Glue interactive sessions
    • Using SageMaker Processing with custom scripts
    • Hands-On Lab: Data processing using Amazon SageMaker Processing and SageMaker Python SDK
    • SageMaker Feature Store
    • Hands-On Lab: Feature engineering using SageMaker Feature Store

    • SageMaker training jobs
    • Built-in algorithms
    • Bring your own script
    • Bring your own container
    • SageMaker Experiments
    • Hands-On Lab: Using SageMaker Experiments to Track Iterations of Training and Tuning Models

    • SageMaker Debugger
    • Hands-On Lab: Analyzing, Detecting, and Setting Alerts Using SageMaker Debugger
    • Automatic model tuning
    • SageMaker Autopilot: Automated ML
    • Demonstration: SageMaker Autopilot
    • Bias detection
    • Hands-On Lab: Using SageMaker Clarify for Bias and Explainability
    • SageMaker Jumpstart

    • SageMaker Model Registry
    • SageMaker Pipelines
    • Hands-On Lab: Using SageMaker Pipelines and SageMaker Model Registry with SageMaker Studio
    • SageMaker model inference options
    • Amazon SageMaker Studio for Data Scientists
    • Testing strategies, performance, and optimization
    • Hands-On Lab: Inferencing with SageMaker Studio

    • Amazon SageMaker Model Monitor
    • Discussion: Case study
    • Demonstration: Model Monitoring

    • Accrued cost and shutting down
    • Updates
    • Capstone
    • Environment setup
    • Challenge 1: Analyze and prepare the dataset with SageMaker Data Wrangler
    • Challenge 2: Create feature groups in SageMaker Feature Store
    • Challenge 3: Perform and manage model training and tuning using SageMaker Experiments
    • Challenge 4: Use SageMaker Debugger for training performance and model optimization
    • Challenge 5: Evaluate the model for bias using SageMaker Clarify
    • Challenge 6: Perform batch predictions using model endpoint
    • Challenge 7: Automate full model development process using SageMaker Pipeline

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    Course ID: 19949

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    Sincere thanks to CloudThat and the Placement Team for providing excellent training and placement support throughout my journey. The entire experience was very professional, supportive, and career-oriented. Coming from a B.Pharmacy background, transitioning into the IT sector was a completely new journey for me. But with the guidance, support, and quality training provided by CloudThat, I was able to build strong knowledge in Cloud and DevOps and successfully get placed in the IT industry. The training sessions helped me gain practical understanding and confidence in technical concepts. A very special and heartfelt thanks to my Placement Manager for the continuous support, motivation, encouragement, and regular follow-ups throughout the entire placement process. Their dedication towards students is truly outstanding. The way they guided and motivated me at every step really boosted my confidence and played a major role in helping me achieve this opportunity successfully. I would also like to sincerely thank my Trainer for explaining concepts in a clear, structured, and industry-oriented way, which helped me improve both my technical and interview skills. I am truly happy and grateful to be a part of this learning journey. I highly recommend CloudThat for anyone looking for quality training and excellent placement support in Cloud and DevOps. Thank you once again for all the support and guidance.

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    I would like to express my sincere thanks to Cloud That for providing such a valuable learning experience in Cloud and DevOps. The training helped me gain practical knowledge through hands-on sessions and real-world scenarios, which made the concepts much clearer. The support from the trainers throughout the journey was truly helpful in building my confidence. I'm happy to share that I have secured a Cloud and DevOps internship, and I'm grateful for the guidance and mentorship I received during this journey. A special thanks to Harish Krishna Erramilli Sir for his continuous support and encouragement

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