MLOps Platforms: Amazon SageMaker and Azure ML

This course is part of MLOps | Machine Learning Operations Specialization

Instructors: Noah Gift +1 more

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What you'll learn

  •   Apply exploratory data analysis (EDA) techniques to data science problems and datasets.
  •   Build machine learning modeling solutions using both AWS and Azure technology.
  •   Train and deploy machine learning solutions to a production environment using cloud technology.
  • Skills you'll gain

  •   Containerization
  •   Machine Learning
  •   AWS SageMaker
  •   Serverless Computing
  •   MLOps (Machine Learning Operations)
  •   Data Pipelines
  •   Cloud Solutions
  •   Machine Learning Algorithms
  •   Exploratory Data Analysis
  •   Data Analysis
  •   Feature Engineering
  •   Microsoft Azure
  •   Amazon Web Services
  •   Predictive Modeling
  •   Artificial Intelligence and Machine Learning (AI/ML)
  • There are 5 modules in this course

    Through a series of hands-on exercises, you will gain an intuition for basic machine learning algorithms and practical experience working with these leading Cloud platforms. By the end of the course, you will be able to deploy machine learning solutions in a production environment using AWS and Azure technology. Week 1. Explore data engineering with AWS technology. We’ll discuss topics such as getting started with machine learning on AWS, creating data repositories, and identifying and implementing solutions for data ingestion and transformation. Week 2. Gain basic data science skills with AWS technology. You will learn data cleaning techniques, perform feature engineering, data analysis, and data visualization for machine learning. We’ll prioritize using serverless solutions that are available on AWS to make the process more efficient. Week 3. Learn machine learning models with AWS technology. We’ll examine how to select appropriate models for the task at hand, choose hyperparameters, train models on the platform, and evaluate models. Week 4. Learn MLOps with AWS: the final phase of putting machine learning into production. We’ll discuss topics such as operationalizing a machine learning model, deciding between CPU and GPU, and deploying and maintaining the model. Week 5. Learn how to work with data and machine learning in a second leading Cloud-based platform: Azure ML.

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