Machine Learning: Data Platform Engineer

Learn how architecture, data, and storage support advanced machine learning modeling and intelligence workloads

Machine Learning  |  Business Decision Maker  |  Data Platform Engineer  |  Data Scientist  |  Developer   

This path is designed for data platform engineers. Learn machine learning (ML) will change data ingestion, system requirements and performance, and customer experiences. Then supplement the skills you've learned with optional training.

Learn more about the courses in each learning progression below.

  • Follow this recommended sequence of courses and exams to build your AWS Cloud skills in this Learning Path.

    ML Building Blocks: Services and Terminology

    These two courses clarify both the machine learning stack and the terms and processes that will help you build a good foundation in machine learning.

    Digital  |  40 minutes

    Process Model: CRISP-DM on the AWS Stack

    Walk through the CRISP-DM methodology and framework and then apply the model's six phases to your daily work. 

    Digital  |  50 minutes

    Data Analytics Fundamentals

    In this self-paced course, you will learn about the process for planning data analysis solutions and the various data analytic processes that are involved. This course takes you through five key factors that indicate the need for specific AWS services in collecting, processing, analyzing, and presenting your data.

    Digital  |  3.5 hours

    Machine Learning Data Readiness

    This course focuses on the concept of data readiness in the context of machine learning (ML). You will learn how to determine data readiness and identify when to employ data readiness as part of your ML process.

    Digital  |  1 hour

    Storage Deep Dives

    These courses are designed for enterprise storage engineers to learn how to architect and manage highly available solutions, with a focus on AWS storage services.

    Digital  |  Course lengths vary

    Machine Learning Security

    Secure your applications and environments with specific topics detailing NACLs, security groups, AWS identity and access management, and encryption key management.

    Digital  |  30 minutes

    Big Data on AWS

    This course introduces you to cloud-based big data solutions such as Amazon Elastic MapReduce (EMR), Amazon Redshift, Amazon Kinesis, and the rest of the AWS big data platform.

  • The Machine Learning Pipeline on AWS

    Explore how to use the machine learning (ML) pipeline to solve a real business problem in a project-based learning environment. You will learn about each phase of the pipeline from presentations and demonstrations by AWS instructors. You will then apply that knowledge to complete a project solving one of three business problems. By the end of the course, you will have successfully built, trained, evaluated, tuned, and deployed an ML model using Amazon SageMaker that solves your selected business problem.

    Speaking of: Machine Translation and NLP

    These courses explore how machines interact with the human language. Review AWS services that help you with neural networks and natural language processing topics like automatic speech recognition, natural and fluent language translation, and insights and relationships in text.

    Digital  |  80 minutes

    Seeing Clearly: Computer Vision Theory

    This curriculum explores how machines achieve understanding of images and videos. 

    Digital  |  2.5 hours

  • Optional training

    Exploring the Machine Learning Toolset

    Review some of the AWS machine learning services you can use to build models and add intelligence to applications.

    Digital | 80 minutes