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.
提供方


课程信息
• Some knowledge of AI / deep learning
• Intermediate Python skills
• Experience with any deep learning framework (PyTorch, Keras, or TensorFlow)
您将学到的内容有
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.
您将获得的技能
- 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)
提供方

deeplearning.ai
DeepLearning.AI is an education technology company that develops a global community of AI talent.
授课大纲 - 您将从这门课程中学到什么
Week 1: Neural Architecture Search
Learn how to effectively search for the best model that will scale for various serving needs while constraining model complexity and hardware requirements.
Week 2: Model Resource Management Techniques
Learn how to optimize and manage the compute, storage, and I/O resources your model needs in production environments during its entire lifecycle.
Week 3: High-Performance Modeling
Implement distributed processing and parallelism techniques to make the most of your computational resources for training your models efficiently.
Week 4: Model Analysis
Use model performance analysis to debug and remediate your model and measure robustness, fairness, and stability.
审阅
- 5 stars69.45%
- 4 stars17.99%
- 3 stars6.69%
- 2 stars3.34%
- 1 star2.51%
来自MACHINE LEARNING MODELING PIPELINES IN PRODUCTION的热门评论
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.
I enjoyed this course a lot. It gave me a lot of ideas on how I can improve my models and make my workflow more efficient. Thank you.
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
This is very helpful course to understand the life of model specially after its deployment.
关于 Machine Learning Engineering for Production (MLOps) 专项课程
Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well.

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