Machine Learning: Data Platform Engineer

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

This path is designed to prepare data platform engineers for how machine learning (ML) will change data ingestion, system requirements and performance, and the customer experience for the systems, services, and applications they support. Progress through foundational, intermediate and advanced courses, and supplement your learning with optional courses.

Learn more about the courses in each learning progression below.

  • Primary progression

    Machine Learning for Business Challenges

    Machine learning (ML) can help you solve business problems in ways that weren't possible before—but you've got to think big. We'll cover ML terminology, business problems, use cases, and examples.

    Digital  |  1 hour

    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

    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

    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

    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

    Types of Machine Learning Solutions

    Review the three different disciplines for machine learning: computer vision, natural language processing, and chat bots. Go through practical applications and the AWS services used in each.  

    Digital  |  15 minutes

  • Branching content areas

    Communicating with Chat Bots

    Learn how to build smart chat bots with the Communicating with Chat Bots curriculum. 

    Digital  |  3.5 hours

    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

    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. 

    Classroom/Virtual  |  3 days

    Deep Learning on AWS

    This course introduces you to cloud-based deep learning (DL) solutions on AWS. The training will detail how deep learning is useful and explain its different concepts.

    Classroom/Virtual  |  1 day