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Statistics.comX: MLOps2 (GCP): Data Pipeline Automation & Optimization 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 to automate your pipeline’s functions and continuously optimize its performance, which is why we developed this course, MLOps2 (GCP): Data Pipeline Automation & Optimization using Google Cloud Platform.
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Course Insight
Suitable for intermediate learners. Works well as a continuation after mastering Computer Science fundamentals. It bridges the gap toward advanced, production-level engineering.
Intermediate FriendlyCertification IncludedSelf-Paced LearningProject-Based
SKILLS TO
MASTER
Practical ApplicationTrending
Real-world project implementationBest Practices
Industry standard workflows and guidelinesCareers:Data Scientist, Data Analyst, Machine Learning Engineer.
Quick Facts
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What You’ll Learn
- How to meet the differing requirements of model training versus model inference in your pipeline.
- How to check for model drift, data drift, and feedback loops.
- How to apply the principles of Continuous Integration (CI), Continuous Delivery (CDE) and Continuous Deployment (CD).
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Description
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 to automate your pipeline’s functions and continuously optimize its performance, which is why we developed this course, MLOps2 (GCP): Data Pipeline Automation & Optimization using Gogle Cloud Platform. In this course you will learn how to set up automated monitoring of your data pipeline for prediction. Data drift, model drift and feedback loops can impair model performance and model stability, and you will learn how to monitor for those phenomena. You will also learn about setting triggers and alarms, so that operators can deal with problems with model instability. You will also cover ethical issues in machine learning and the risks they pose, and learn about the "Responsible Data Science" framework.
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