Machine Learning: Developer

Learn to integrate machine learning and artificial intelligence into tools and applications

This path is designed for builders and software developers. Learn how machine learning (ML) and artificial intelligence (AI) can help you better partner with data scientists to innovate with ML technologies. Then supplement the skills you've learned with optional training.

Learn more about the courses in each learning progression below.

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

    ML Building Blocks: Services and Terminology

    Clarify 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

    The Elements of Data Science

    Learn to build and continuously improve machine learning models by covering problem formulation, exploratory data analysis, feature engineering, model training, tuning and debugging, as well as model evaluation and productionizing.

    Digital | 8 hours

    Developing Machine Learning Applications

    Explore Amazon’s fully managed ML platform, Amazon SageMaker.

    Digital | 2.5 hours

    Practical Data Science with Amazon SageMaker

    Explore real-world use cases with Machine Learning (ML) and using Amazon SageMaker in the new 1-day classroom training course.

    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.

    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

    Exam Readiness: AWS Certified Machine Learning – Specialty

    Explore the AWS Certified Machine Learning - Specialty exam’s topic areas, see how they relate to machine learning on AWS, and map them to machine learning (ML) and deep learning (DL) foundational areas for future self-study.

    Classroom | 4 hours
    Digital | 2 hours

  • AWS DeepRacer: Driven by Reinforcement Learning

    Build, train, and deploy models to develop your core ML skills.

    Digital | 90 minutes

    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.

  • AWS Certification

    AWS Certified Machine Learning - Specialty

    The AWS Certified Machine Learning - Specialty certification was created by AWS experts and validates in-demand skills required to build and tune data models. Differentiate yourself and your organization in this growing field.

    Exam  |  170 minutes