In MLOps (Machine Learning Operations) Platforms: Amazon SageMaker and Azure ML you will learn the necessary skills to build, train, and deploy machine learning solutions in a production environment using two leading cloud platforms: Amazon Web Services (AWS) and Microsoft Azure. This course is also a great resource for individuals looking to prepare for AWS or Azure machine learning certifications or who are working (or seek to work) as data scientists, software engineers, software developers, data analysts, or other roles that use machine learning.

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.