Advanced Machine Learning with TensorFlow on Google Cloud Platform (MLTF)

Module 1: Machine Learning on Google Cloud Platform

• Effective ML
• Fully Managed ML

Module 2: Explore the Data

• Exploring the Dataset
• BigQuery
• BigQuery and AI Platform Notebooks

Module 3: Creating the Dataset

• Creating a Dataset

Module 4: Build the Model

• Build the Model

Module 5: Operationalize the Model

• Operationalizing the Model
• Cloud AI Platform
• Train and Deploy with Cloud AI Platform
• BigQuery ML
• Deploying and Predicting with Cloud AI Platform

Module 6: Architecting Production ML Systems

• 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

Module 7: Ingesting Data for Cloud-Based Analytics and ML

• Data On-Premises
• Large Datasets
• Data on Other Clouds
• Existing Databases

Module 8: Designing Adaptable ML Systems

• Adapting to Data
• Changing Distributions
• Right and Wrong Decisions
• System Failure
• Mitigating Training-Serving Skew Through Design
• Debugging a Production Model

Module 9: Designing High-Performance ML Systems

• 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

Module 10: Hybrid ML Systems

• Machine Learning on Hybrid Cloud
• KubeFlow
• Embedded Models
• TensorFlow Lite
• Optimizing for Mobile

Module 11: Welcome to Image Understanding with TensorFlow on GCP

• Images as Visual Data
• Structured vs. Unstructured Data

Module 12: Linear and DNN Models

• Linear Models
• DNN Models Review
• Review: What is Dropout?

Module 13: Convolutional Neural Networks (CNNs)

• Understanding Convolutions
• CNN Model Parameters
• Working with Pooling Layers
• Implementing CNNs with TensorFlow

Module 14: Dealing with Data Scarcity

• The Data Scarcity Problem
• Data Augmentation
• Transfer Learning
• No Data, No Problem

Module 15: Going Deeper Faster

• Batch Normalization
• Residual Networks
• Accelerators (CPU vs GPU, TPU)
• TPU Estimator
• Neural Architecture Search

Module 16: Pre-built ML Models for Image Classification

• Pre-Built ML Models
• Cloud Vision API
• AutoML Vision
• AutoML Architecture

Module 17: Working with Sequences

• Sequence Data and Models
• From Sequences to Inputs
• Modeling Sequences with Linear Models
• Modeling Sequences with DNNs
• Modeling Sequences with CNNs
• The Variable-Length problem

Module 18: Recurrent Neural Networks

• Introducing Recurrent Neural Networks
• How RNNs Represent the Past
• The Limits of What RNNs Can Represent
• The Vanishing Gradient Problem

Module 19: Dealing with Longer Sequences

• LSTMs and GRUs
• RNNs in TensorFlow
• Deep RNNs
• Improving our Loss Function
• Working with Real Data

Module 20: Text Classification

• Working with Text
• Text Classification
• Selecting a Model
• Python vs Native TensorFlow

Module 21: Reusable Embeddings

• Historical Methods of Making Word Embeddings
• Modern Methods of Making Word Embeddings
• Introducing TensorFlow Hub
• Using TensorFlow Hub Within an Estimator

Module 22: Recurrent Neural NetworksEncoder-Decoder Models

• Introducing Encoder-Decoder Networks
• Attention Networks
• Training Encoder-Decoder Models with TensorFlow
• Introducing Tensor2Tensor
• AutoML Translation
• Dialogflow

Module 23: Recommendation Systems Overview

• Types of Recommendation Systems
• Content-Based or Collaborative
• Recommendation System Pitfalls

Module 24: Content-Based Recommendation Systems

• Content-Based Recommendation Systems
• Similarity Measures
• Building a User Vector
• Making Recommendations Using a User Vector
• Making Recommendations for Many Users
• Using Neural Networks for Content-Based Recommendation Systems

Module 25: Collaborative Filtering Recommendation Systems

• Types of User Feedback Data
• Embedding Users and Items
• Factorization Approaches
• The ALS Algorithm
• Preparing Input Data for ALS
• Creating Sparse Tensors For Efficient WALS Input
• Instantiating a WALS Estimator: From Input to Estimator
• Instantiating a WAL Estimator: Decoding TFRecords
• Instantiating a WALS Estimator: Recovering Keys
• Instantiating a WALS Estimator: Training and Prediction
• Issues with Collaborative Filtering
• Cold Starts

Module 26: Neural Networks for Recommendation Systems

• Hybrid Recommendation System
• Context-Aware Recommendation Systems
• Context-Aware Algorithms
• Contextual Postfiltering
• Modeling Using Context-Aware Algorithms

Module 27: Building an End-to-End Recommendation System

• Architecture Overview
• Cloud Composer Overview
• Cloud Composer: DAGs
• Cloud Composer: Operators for ML9
• Cloud Composer: Scheduling
• Cloud Composer: Triggering Workflows with Cloud Functions
• Cloud Composer: Monitoring and Logging