In the third course of Machine Learning Engineering for Production Specialization, you will build models for different serving environments; implement tools and techniques to effectively manage your modeling resources and best serve offline and online inference requests; and use analytics tools and performance metrics to address model fairness, explainability issues, and mitigate bottlenecks.
This course is part of the Machine Learning Engineering for Production (MLOps) Specialization
Offered By

About this Course
• Some knowledge of AI / deep learning
• Intermediate Python skills
• Experience with any deep learning framework (PyTorch, Keras, or TensorFlow)
Could your company benefit from training employees on in-demand skills?
Try Coursera for BusinessWhat you will learn
Apply techniques to manage modeling resources and best serve batch and real-time inference requests.
Use analytics to address model fairness, explainability issues, and mitigate bottlenecks.
Skills you will gain
- Explainable AI
- Fairness Indicators
- automl
- Model Performance Analysis
- Precomputing Predictions
• Some knowledge of AI / deep learning
• Intermediate Python skills
• Experience with any deep learning framework (PyTorch, Keras, or TensorFlow)
Could your company benefit from training employees on in-demand skills?
Try Coursera for BusinessOffered by
Syllabus - What you will learn from this course
Week 1: Neural Architecture Search
Week 2: Model Resource Management Techniques
Week 3: High-Performance Modeling
Week 4: Model Analysis
Reviews
- 5 stars64.56%
- 4 stars20.52%
- 3 stars6.62%
- 2 stars5.29%
- 1 star2.98%
TOP REVIEWS FROM MACHINE LEARNING MODELING PIPELINES IN PRODUCTION
This course is helpful. Enrich my knowledge with data concepts, optimal high-performance model tools and model debugging.
There were a lot of useful information and practical insights about the subject of the course. The material on Tensorflow-specific modules felt a bit unorganized and cumbersome to go through.
It was really a wonderful and amazing course. I really learnt about what all goes in creating a successful ML project
Some of the topics were too advanced and instructor assumes that we know those basics. It felt rush through little bit and more of reading slides then explaining at many places
About the Machine Learning Engineering for Production (MLOps) Specialization

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