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


Course Overview

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.

Who should attend

  • 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


This course is part of the following Certifications:


To get the most out of this course, participants should have:

  • Knowledge of machine learning and TensorFlow to the level covered in Machine Learning on Google Cloud coursework
  • Experience coding in Python
  • Knowledge of basic statistics
  • Knowledge of SQL and cloud computing (helpful)

Course Objectives

This course teaches participants the following skills:

  • 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

Outline: 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

Prices & Delivery methods

Online Training

5 days

  • Online Training: CAD 3,955
  • Online Training: US$ 2,995

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