trainocate-gcp-training-b

GCPAMLTF - Advanced Machine Learning with TensorFlow on Google Cloud Platform

Overview

Duration: 5 days
This training focuses on advanced machine learning topics using Google Cloud Platform where you will get hands-on experience optimizing, deploying, and scaling production ML models of various types in hands-on labs. This training picks up where “Machine Learning on Google Cloud Platform” left off and teaches you how to build scalable, accurate, and production-ready models for structured data, image data, time-series, and natural language text. It ends with a module on building 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.

Content

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.
  • On demand
  • Take this course on demand
  • Upcoming classrooms
  • There are no upcoming instructor-led sessions 

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

To get the most out of this training, participants should have:
  • Knowledge of machine learning and TensorFlow to the level covered in Machine Learning on Google Cloud Platform coursework.
  • Experience coding in Python.
  • Knowledge of basic statistics.
  •  Knowledge of SQL and cloud computing (helpful).

Certification

n/a

Schedule

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