About this course

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

At a Glance:
Institution: Statistics.comX
Subject: Computer Science
Level: Intermediate
Prerequisites:
Predictive Analytics: Basic Modeling Techniques
Participants should be comfortable working with Python in a cloud-based environment, and will gain maximum benefit if they have some familiarity with software development, including git, logging, testing, debugging, code optimization and security.
Language: English
Video Transcript: English
Associated programs:
Professional Certificate in Machine Learning Operations with Amazon Web Services (MLOps with AWS)
Associated skills:Amazon Web Services, Software Versioning, Return On Investment, Artificial Intelligence, Data Science, Data Engineering, Operations, Forecasting, Machine Learning