MLOps1 (AWS): Deploying AI & ML Models in Production using Amazon Web Services from edX

This is the second of three courses in the Machine Learning Operations Program using Amazon Web Services (AWS).

Data Science, AI, and Machine Learning projects can deliver an amazing return on investment. But, in practice, most projects that look great in the lab (and would work if implemented!) never see the light of day. They could save or make the organization millions of dollars but never make it all the way into production. What’s going on? It turns out that making decisions in a whole new way is a big challenge to implement--for many technical, business and human-nature reasons. After decades of experience though, our team has learned how to turn this around and actually get working models into production the great majority of the time. A key part of deployment is excellence in data engineering, and is why we developed this course: MLOps1(AWS): Deploying AI & ML Models in Production.

You will get hands-on experience with topics like data pipelines, data and model “versioning”, model storage, data artifacts, and more.

Most importantly, by the end of this course, you will know...

What data engineers need to know to work effectively with data scientists
How to embed a predictive model in a pipeline that takes in data and outputs predictions automatically
How to monitor the model’s performance and follow best practices

What you'll learn

What data engineers need to know in order to work effectively with data scientists
How to use a machine learning model to make predictions
How to embed that model in a pipeline that takes in data and outputs predictions automatically
How to measure the performance of the model and the pipeline, and how to log those metrics
How to follow best practices for “versioning” the model and the data
How to track and store model and data artifacts

What's inside

Syllabus

Week 1: The Machine Learning Pipeline

AI Engineering Role

ML pipeline lifecycle

Week 2: The Model in the Pipeline

Case Study for the Course

Model Understanding

Week 3: Monitoring Model Performance

Logging and Metric Selection

Model and Data Versioning

Week 4: Training Artifacts and Model Store

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Teaches learners how to monitor model performance, which is a key part of ensuring that the model is working as expected and is not biased

Develops skills in data engineering, which is increasingly important in many industries

Taught by instructors who are recognized for their work in the field and who work at the forefront of the tech industry

Covers topics that are highly relevant to industry

Requires students to have some prior knowledge of data science and machine learning

Focuses on deploying models on AWS, which may not be relevant for all learners

Career center

Learners who complete MLOps1 (AWS): Deploying AI & ML Models in Production using Amazon Web Services will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists use many of the same skills as ML Engineers, Machine Learning Engineers, and Machine Learning Scientists. This course covers basic machine learning principles and provides an in-depth examination of the creation and monitoring of machine learning pipelines. Those who want to maximize their potential to become Data Scientists should consider taking this course.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers are professionals who work with data scientists to apply machine learning to solve business problems. As a Machine Learning Engineer, you would likely use and manage the machine learning pipelines that this course concerns itself with creating. Additionally, you would be responsible for monitoring the performance of the models within the pipeline. Therefore, this course is highly relevant to Machine Learning Engineers.

See salaries and explore the career path for Machine Learning Engineer

Data Engineer

This course focuses on how to prepare data for machine learning models. It also discusses how to measure and maintain the performance of a machine learning pipeline. Therefore, this course is highly relevant to Data Engineers.

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Machine Learning Scientist

As a Machine Learning Scientist, you may find yourself creating and monitoring the machine learning pipelines that this course covers. You would also be responsible for monitoring those pipelines, training models, and applying those models to data. This course provides many tools and demonstrates many best practices that Machine Learning Scientists should be familiar with.

See salaries and explore the career path for Machine Learning Scientist

Software Engineer

This course teaches the skills needed to create, deploy, and manage machine learning pipelines. As a Software Engineer, you may be involved in some of these tasks. Therefore, this course may be of some use to Software Engineers.

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Business Analyst

This course may be useful for Business Analysts who want to improve their data analysis skills.

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Project Manager

Project Managers may benefit from the organizational and planning elements of this course.

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Data Analyst

This course covers many foundational elements of data analysis and could be useful to someone looking to work in the field.

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Systems Analyst

This course may be useful to a Systems Analyst who wants to gain a higher level understanding of machine learning.

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Database Administrator

This course may be useful to a Database Administrator who wants to gain a higher level understanding of machine learning.

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Statistician

This course covers many foundational elements of statistics and could be useful to someone looking to work in the field.

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Financial Analyst

This course may be useful to a Financial Analyst who wants to gain a higher level understanding of machine learning.

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Operations Research Analyst

This course may be useful to an Operations Research Analyst who wants to gain a higher level understanding of machine learning.

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Marketing Analyst

This course may be useful to a Marketing Analyst who wants to gain a higher level understanding of machine learning.

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Management Consultant

This course may be useful to a Management Consultant who wants to gain a higher level understanding of machine learning.

See salaries and explore the career path for Management Consultant

MLOps1 (AWS)

Deploying AI & ML Models in Production using Amazon Web Services

What you'll learn

What's inside

Syllabus

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