DeepLearning.AI - Machine Learning Modeling Pipelines in Production
- Offered byCoursera
Machine Learning Modeling Pipelines in Production at Coursera Overview
Duration | 21 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Advanced |
Official Website | Explore Free Course  |
Credential | Certificate |
Machine Learning Modeling Pipelines in Production at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 3 of 4 in the Machine Learning Engineering for Production (MLOps) Specialization
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Advanced Level ¢?¢ Some knowledge of AI / deep learning Intermediate Python skills Experience with any deep learning framework (PyTorch, Keras, or TensorFlow)
- Approx. 21 hours to complete
- English Subtitles: English
Machine Learning Modeling Pipelines in Production at Coursera Course details
- 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.
- 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. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills.
Machine Learning Modeling Pipelines in Production at Coursera Curriculum
Week 1
Neural Architecture Search
Search Space
Performance Estimation
AutoML on the Cloud
Model Complexity
Ensemble Learning
Cascaded Classifier Ensemble
Ensemble Visualization
SMOTE
Classification Threshold in Simple vs Complex Models
Precomputing and Caching Predictions
Predictions by Entity vs Feature Combinations
Offline Inference
Online Inference
Resource Costs and Constraints
Models Deployed on Server
Serving ML Models
TensorFlow Serving
Saving and Examining a Model
Installing TensorFlow Serving
Running TensorFlow Serving
Welcome to Alpha Testing Modeling Pipelines for Production ML
Colab of Ungraded TF Tutorial - Ensemble Model
Colab of Ungraded TF Tutorial - Multiple Serving Signatures
Colab of W1 Programming Assignment
AutoML
Ensemble Learning
Precomputing Predictions
Comparing Model Performance
Prepare Models for Serving
Week 2
Machine Learning Modeling Pipelines in Production at Coursera Admission Process
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