MLOps1 (GCP): Deploying AI & ML Models in Production using Google Cloud Platform

Most data science projects fail. There are various reasons why, but one of the primary reasons is the challenge of deployment. One piece to the deployment puzzle is understanding how data engineers can effectively work with data scientists to monitor and iterate on model performance, which is why we developed this course: MLOps1 (GCP) - Deploying AI & ML Models in Production using Google Cloud Platform

Monthly Subscription Starting at AED 99 + VAT

Level Awareness
Duration 26 hours
Course by Statistics.com
Total students 782 enrolled
Offered by

About

This is the second of three courses in the Machine Learning Operations Program using Google Cloud Platform (GCP).

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 (GCP): 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 will 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

Skills you learn

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

Auto Summary

Discover the essentials of deploying AI and ML models in production with "MLOps1 (GCP): Deploying AI & ML Models in Production using Google Cloud Platform." This edX course, ideal for IT and computer science enthusiasts, emphasizes collaboration between data engineers and data scientists to monitor and enhance model performance. With a 26-hour duration, it offers both Professional and Starter subscription options, catering to learners seeking foundational awareness in MLOps. Join now to master deployment challenges on Google Cloud Platform!