Course Description

This course will give you hands-on experience optimizing, deploying, and scaling a variety of production ML models. You'll learn how to build scalable, accurate, and production-ready models for structured data, image data, time-series, and natural language text, along with recommendation systems.

Objectives

• Implement the various flavors of production ML systems—static, dynamic, and continuous training; static and dynamic inference; and batch and online processing.
• Solve an ML problem by building an end-to-end pipeline, going from data exploration, preprocessing, feature engineering, model building, hyperparameter tuning, deployment, and serving.
• Develop a range of image classification models from simple linear models to high-performing convolutional neural networks (CNNs) with batch normalization, augmentation, and transfer learning.
• Forecast time-series values using CNNs, recurrent neural networks (RNNs), and LSTMs.
• Apply ML to natural language text using CNNs, RNNs, LSTMs, reusable word embeddings, and encoder-decoder generative models.
• Implement content-based, collaborative, hybrid, and neural recommendation models in TensorFlow.

Audience

• Data Engineers and programmers interested in learning how to apply machine learning in practice.
• Anyone interested in learning how to leverage machine learning in their enterprise.

Prerequisites

There are no prerequisites for this course.

Content

The course includes presentations, demonstrations, and hands-on labs.

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-Premise.
• 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 problem4m.

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.