DeepLearning.AI - Deploying Machine Learning Models in Production
- Offered byCoursera
- Public/Government Institute
Deploying Machine Learning Models in Production at Coursera Overview
Duration | 6 hours |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Advanced |
Official Website | Explore Free Course  |
Credential | Certificate |
- Overview
- Highlights
- Course Details
- Curriculum
Deploying Machine Learning Models in Production at Coursera Highlights
- Reset deadlines in accordance to your schedule.
- Earn a Certificate upon completion
- Start instantly and learn at your own schedule.
Deploying Machine Learning Models in Production at Coursera Course details
- In the fourth course of Machine Learning Engineering for Production Specialization, you will deliver deployment pipelines by productionizing, scaling, and monitoring model serving that require different infrastructure; establish procedures to mitigate model decay and performance drops; and establish best practices and apply progressive delivery techniques to maintain and monitor a continuously operating production system.
Deploying Machine Learning Models in Production at Coursera Curriculum
Model Serving: Introduction
Course Overview
Introduction to Model Serving
Introduction to Model Serving Infrastructure
Deployment Options
Improving Prediction Latency and Reducing Resource Costs
Creating and deploying models to AI Prediction Platform
Installing TensorFlow Serving
Model Serving: Patterns and Infrastructure
Model Serving Architecture
Model Servers: TensorFlow Serving
Model Servers: Other Providers
Scaling Infrastructure
Online Inference
Data Preprocessing
Batch Inference Scenarios
Batch Processing with ET
Model Management and Delivery
Experiment Tracking
Tools for Experiment Tracking
Introduction to MLOps
MLOps Level 0
MLOps Levels 1&2
Developing Components for an Orchestrated Workflow
Managing Model Versions
Continuous Delivery
Progressive Delivery
Model Monitoring and Logging
Why Monitoring Matters
Observability in ML
Monitoring Targets in ML
Logging for ML Monitoring
Tracing for ML Systems
What is Model Decay?
Model Decay Detection
Ways to Mitigate Model Decay
Responsible AI
Legal Requirements for Secure and Private AI
Anonymization and Pseudonymisation
Right to be Forgotten
Specialization recap and farewell
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