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.
이 강좌는 Machine Learning Engineering for Production (MLOps) 특화 과정의 일부입니다.
제공자:


이 강좌에 대하여
• Some knowledge of AI / deep learning
• Intermediate Python skills
• Experience with any deep learning framework (PyTorch, Keras, or TensorFlow)
배울 내용
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.
귀하가 습득할 기술
- 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)
제공자:

deeplearning.ai
DeepLearning.AI is an education technology company that develops a global community of AI talent.
강의 계획표 - 이 강좌에서 배울 내용
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.
검토
- 5 stars71.74%
- 4 stars17.93%
- 3 stars6.27%
- 2 stars2.24%
- 1 star1.79%
MACHINE LEARNING MODELING PIPELINES IN PRODUCTION의 최상위 리뷰
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.
This course filled in some black holes in my knowledge and I found it very helpful.
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
Machine Learning Engineering for Production (MLOps) 특화 과정 정보
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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