Brochure
Enquire- Specialization: Advanced Machine Learning on Google Cloud
- Welcome
Expert
Online
2 Weeks
Quick facts
| particular | details | ||
|---|---|---|---|
|
Medium of instructions
English
|
Mode of learning
Self study
|
Mode of Delivery
Video and Text Based
|
Learning efforts
10 Hours Per Week
|
Course and certificate fees
certificate availability
Yes
certificate providing authority
Coursera
The syllabus
Module 1: Introduction to Advanced Machine Learning on Google Cloud
Videos
Readings
- How to download course resources
- How to send feedback
Module 2: Architecting Production ML Systems
Videos
- Architecting ML systems
- Data extraction, analysis, and preparation
- Model training, evaluation, and validation
- Trained model, prediction service, and performance monitoring
- Training design decisions
- Serving design decisions
- Designing from scratch
- Using Vertex AI
- Lab Introduction: Structured data prediction
- Getting Started with Google Cloud and Qwiklabs
Reading
- Architecting production ML systems
Assignment
- Architecting production ML systems
App Item
- Lab: Structured data prediction using Vertex AI
Module 3: Designing Adaptable ML Systems
Videos
- Introduction
- Adapting to data
- Changing distributions
- Lab: Adapting to data
- Right and wrong decisions
- System failure
- Concept drift
- Actions to mitigate concept drift
- TensorFlow data validation
- Components of TensorFlow data validation
- Lab Introduction: Introduction to TensorFlow data validation
- Lab Introduction: Advanced visualizations with TensorFlow data validation
- Mitigating training-serving skew through design
- Lab Introduction: Serving ML predictions in batch and real-tIme
- Lab Debrief: Serving ML predictions in batch and real-time
- Diagnosing a production model
Reading
- Designing adaptable ML systems
Assignment
- Designing adaptable ML systems
App Items
- Lab: Introduction to TensorFlow data validation
- Lab: Advanced visualizations with TensorFlow data validation
- Lab: Vertex AI: Training and Serving a Custom Model
Module 4: Designing High-Performance ML Systems
Videos
- Introduction
- Training
- Predictions
- Why distributed training is needed
- Distributed training architectures
- TensorFlow distributed training strategies
- Mirrored strategy
- Multi-worker mirrored strategy
- TPU strategy
- Parameter server strategy
- Lab Introduction: Distributed training with Keras
- Lab Introduction: Distributed training using GPUs on Google Cloud’s AI Platform (Multi-Worker)
- Training on large datasets with tf.data API
- Lab introduction: TPU-speed data pipelines
- Inference
Reading
- Designing high-performance ML systems
Assignment
- Designing high-performance ML systems
App Items
- Lab: Distributed training with Keras
- Lab: TPU-speed data pipelines
Module 5: Building Hybrid ML Systems
Videos
- Introduction
- Machine Learning on Hybrid Cloud
- KubeFlow
- Lab Introduction: Kubeflow Pipelines with AI Platform
- Embedded models
- TensorFlow Lite
- Optimizing TensorFlow for mobile
- Summary
Reading
- Hybrid ML systems
Assignment
- Hybrid ML systems
App Item
- Running Pipelines on Vertex AI
Module 6: Summary
Video
- Course summary
Readings
- Production Machine Learning systems - readings
- All quiz questions and answers
Instructors
Google Cloud Training
Instructor
Google Cloud
Articles
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