Production Machine Learning Systems
- Offered byCoursera
Production Machine Learning Systems at Coursera Overview
Duration | 8 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Advanced |
Official Website | Explore Free Course  |
Credential | Certificate |
Production Machine Learning Systems at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Advanced Level
- Approx. 8 hours to complete
- English Subtitles: English
Production Machine Learning Systems at Coursera Course details
- In the second course of this specialization, we will dive into the components and best practices of a high-performing ML system in production environments.
- Prerequisites: Basic SQL, familiarity with Python and TensorFlow
- This course is part of multiple programs
- This course can be applied to multiple Specializations or Professional Certificates programs. Completing this course will count towards your learning in any of the following programs:
- Advanced Machine Learning on Google Cloud Specialization
- Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate
Production Machine Learning Systems at Coursera Curriculum
Welcome to the course
Course Introduction
Getting Started with Google Cloud Platform and Qwiklabs
How to Send Feedback
Introduction
The Components of an ML System
The Components of an ML System: Data Analysis and Validation
The Components of an ML System: Data Transformation + Trainer
The Components of an ML System: Tuner + Model Evaluation and Validation
The Components of an ML System: Serving
The Components of an ML System: Orchestration + Workflow
The Components of an ML System: Integrated Frontend + Storage
Training Design Decisions
Serving Design Decisions
Designing from Scratch
Lab Intro: Structured data prediction using AI Platform
Components of ML Systems
Architecting Production ML Systems
Introduction
Data On-Premise
Large Datasets
Data on Other Clouds
Existing Databases
Demo: Load data into BigQuery
Demo: Automatic ETL Pipelines into GCP
Ingesting data for Cloud-based analytics and ML
Designing Adaptable ML systems
Introduction
Adapting to Data
Changing Distributions
Exercise: Adapting to Data
Right and Wrong Decisions
System Failure
Mitigating Training-Serving Skew through Design
Lab Intro: Serving ML Predictions in batch and real-time
Lab Solution: Serving ML Predictions in batch and real-time
Debugging a Production Model
Summary
Designing Adaptable ML Systems
Introduction
Training
Predictions
Why distributed training?
Distributed training architectures
Faster input pipelines
Native TensorFlow Operations
TensorFlow Records
Parallel pipelines
Data parallelism with All Reduce
Parameter Server Approach
Inference
Designing High-performance ML systems
Introduction
Machine Learning on Hybrid Cloud
KubeFlow
Demo: KubeFlow
Embedded Models
TensorFlow Lite
Optimizing for Mobile
Summary
Hybrid ML systems
Summary
Additional Resources
Production Machine Learning Systems at Coursera Admission Process
Important Dates
Other courses offered by Coursera
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Data ScienceMachine LearningMachine Learning and Pattern RecognitionProduction Machine Learning Systems