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

Price
$2,995.00 USD

Duration
5 Days

Delivery Methods
Virtual Instructor Led
Private Group

Request More Information

Download Course Details

Course Details Only
Course Details & Schedule

Skip to Class Dates

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.

Course 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

Who Should Attend?

Data Engineers and programmers interested in learning how to apply machine learning in practice. Anyone interested in learning how to build and operationalize TensorFlow models.

Top-rated instructors: Our crew of subject matter experts have an average instructor rating of 4.8 out of 5 across thousands of reviews.
Authorized content: We maintain more than 35 Authorized Training Partnerships with the top players in tech, ensuring your course materials contain the most relevant and up-to date information.
Interactive classroom participation: Our virtual training includes live lectures, demonstrations and virtual labs that allow you to participate in discussions with your instructor and fellow classmates to get real-time feedback.
Post Class Resources: Review your class content, catch up on any material you may have missed or perfect your new skills with access to resources after your course is complete.
Private Group Training: Let our world-class instructors deliver exclusive training courses just for your employees. Our private group training is designed to promote your team’s shared growth and skill development.
Tailored Training Solutions: Our subject matter experts can customize the class to specifically address the unique goals of your team.

Course Prerequisites

Experience coding in Python

Knowledge of basic statistics

Knowledge of SQL and cloud computing (helpful)

Agenda

1 - Machine Learning on Google Cloud Platform

Effective ML
Fully Managed ML

2 - Explore the Data

Exploring the Dataset
BigQuery
BigQuery and AI Platform Notebooks

3 - Creating the Dataset

Creating a Dataset

4 - Build the Model

Build the Model

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

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

7 - Ingesting Data for Cloud-Based Analytics and ML

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

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

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

10 - Hybrid ML Systems

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

11 - Welcome to Image

Images as Visual Data
Structured vs. Unstructured Data

12 - Linear and DNN Models

Linear Models
DNN Models Review
Review: What is Dropout?

13 - Convolutional Neural Networks (CNNs)

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

14 - Dealing with Data Scarcity

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

15 - Going Deeper Faster

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

16 - Pre-built ML Models for Image Classification

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

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

18 - Recurrent Neural Networks

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

19 - Dealing with Longer Sequences

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

20 - Text Classification

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

21 - Reusable Embeddings

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

22 - Recurrent Neural NetworksEncoder-Decoder Models

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

23 - Recommendation Systems Overview

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

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

25 - Collaborative Filtering

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

26 - Neural Networks for Recommendation Systems

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

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

Upcoming Class Dates and Times

Jun 10, 11, 12, 13, 14
9:00 AM - 5:00 PM

ENROLL $2,995.00 USD

Virtual: Online - CST

Jul 22, 23, 24, 25, 26
8:00 AM - 4:00 PM