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.
Ce cours fait partie de la Spécialisation Machine Learning Engineering for Production (MLOps)
Offert par

À propos de ce cours
• Some knowledge of AI / deep learning
• Intermediate Python skills
• Experience with any deep learning framework (PyTorch, Keras, or TensorFlow)
Ce que vous allez apprendre
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.
Compétences que vous acquerrez
- 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)
Offert par

deeplearning.ai
DeepLearning.AI is an education technology company that develops a global community of AI talent.
Programme de cours : ce que vous apprendrez dans ce cours
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.
Avis
- 5 stars71,74 %
- 4 stars17,93 %
- 3 stars6,27 %
- 2 stars2,24 %
- 1 star1,79 %
Meilleurs avis pour MACHINE LEARNING MODELING PIPELINES IN PRODUCTION
This is very helpful course to understand the life of model specially after its deployment.
Excellent!! Ver, Very Very Good. Learn a lot. Thank you for sharing.
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
It was really a wonderful and amazing course. I really learnt about what all goes in creating a successful ML project
À propos du Spécialisation Machine Learning Engineering for Production (MLOps)
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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