Machine Learning on Google Cloud

0 days
MLGC - Version:1
Description
This course teaches you how to build Vertex AI AutoML models without writing a single line of code; build BigQuery ML models knowing basic SQL; create Vertex AI custom training jobs you deploy using containers (with little knowledge of Docker0; use Feature Store for data management and governance; use feature engineering for model improvement; determine the appropriate data preprocessing options for your use case; write distributed ML models that scale in TensorFlow; and leverage best practices to implement machine learning on Google Cloud. Learn all this and more!
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  • How Google Does Machine Learning

    • Objectives
      • What are best practices for implementing machine learning on Google Cloud? What is
      • Vertex AI and how can you use the platform to quickly build, train, and deploy AutoML
      • machine learning models without writing a single line of code? What is machine
      • learning, and what kinds of problems can it solve?
      • Google thinks about machine learning slightly differently: it’s about providing a unified
      • platform for managed datasets, a feature store, a way to build, train, and deploy
      • machine learning models without writing a single line of code, providing the ability
      • to label data, create Workbench notebooks using frameworks such as TensorFlow,
      • SciKit Learn, Pytorch, R, and others. Our Vertex AI Platform also includes the ability
      • to train custom models, build component pipelines, and perform both online and batch
      • predictions. We also discuss the five phases of converting a candidate use case to be
      • driven by machine learning, and consider why it is important to not skip the phases. We
      • end with a recognition of the biases that machine learning can amplify and how
      • to recognize them.
      • • Describe the Vertex AI Platform and how it is used to quickly build, train, and deploy
      • AutoML machine learning models without writing a single line of code.
      • • Describe best practices for implementing machine learning on Google Cloud.
      • • Develop a data strategy around machine learning.
      • • Examine use cases that are then reimagined through an ML lens.
      • • Leverage Google Cloud Platform tools and environment to do ML.
      • • Learn from Google's experience to avoid common pitfalls.
      • • Carry out data science tasks in online collaborative notebooks.
    • Activities:
    • • Hands-On Labs
    • • Module Quizzes
    • • Module Readings

  • Launching into Machine Learning

    • Objectives
      • The course begins with a discussion about data: how to improve data quality and
      • perform exploratory data analysis. We describe Vertex AI AutoML and how to build,
      • train, and deploy an ML model without writing a single line of code. You will understand
      • the benefits of Big Query ML. We then discuss how to optimize a machine learning
      • (ML) model and how generalization and sampling can help assess the quality of ML
      • models for custom training.
      • • Describe Vertex AI AutoML and how to build, train, and deploy an ML model without
      • writing a single line of code.
      • • Describe Big Query ML and its benefits.
      • • Describe how to improve data quality.
      • • Perform exploratory data analysis.
      • • Build and train supervised learning models.
      • • Optimize and evaluate models using loss functions and performance metrics.
      • • Mitigate common problems that arise in machine learning.
      • • Create repeatable and scalable training, evaluation, and test datasets.
      • • Hands

    • Activities
      • • Hands-On Labs
      • • Module Quizzes
      • • Module Readings

  • TensorFlow on Google Cloud
    • Objectives:
    • The modules cover designing and building a TensorFlow input data pipeline, building
    • ML models with TensorFlow and Keras, improving the accuracy of ML models, writing
    • ML models for scaled use, and writing specialized ML models.
    • • Create TensorFlow and Keras machine learning models.
    • • Describe TensorFlow key components.
    • • Use the tf.data library to manipulate data and large datasets.
    • • Build a ML model using tf.keras preprocessing layers.
    • • Use the Keras Sequential and Functional APIs for simple and advanced model
    • creation. Understand how model subclassing can be used for more
    • customized models.
    • • Use tf.keras.preprocessing utilities for working with image data, text data, and
    • sequence data.
    • • Train, deploy, and productionalize ML models at scale with Cloud AI Platform.
    • Activities:
    • • Hands-On Labs
    • • Module Quizzes
    • • Module Readings

  • Feature Engineering

    • Objectives
      • Want to know about Vertex AI Feature Store? Want to know how you can improve
      • the accuracy of your ML models? What about how to find which data columns make
      • the most useful features? Welcome to Feature Engineering, where we discuss good
      • versus bad features and how you can preprocess and transform them for optimal use
      • in your models. This course includes content and labs on feature engineering using
      • BigQuery ML, Keras, and TensorFlow.
      • • Describe Vertex AI Feature Store.
      • • Compare the key required aspects of a good feature.
      • • Combine and create new feature combinations through feature crosses.
      • • Perform feature engineering using BigQuery ML, Keras, and TensorFlow.
      • • Understand how to preprocess and explore features with Dataflow and Dataprep
      • by Trifacta.
      • • Understand and apply how TensorFlow transforms features.
    • Activities:
    • • Hands-On Labs
    • • Module Quizzes
    • • Module Readings

  • Machine Learning in the Enterprise

    • Objectives
      • This course encompasses a real-world practical approach to the ML Workflow: a case
      • study approach that presents an ML team faced with several ML business
      • requirements and use cases. This team must understand the tools required for data
      • management and governance and consider the best approach for data preprocessing:
      • from providing an overview of Dataflow and Dataprep to using BigQuery
      • for preprocessing tasks.
      • The team is presented with three options to build machine learning models for two
      • specific use cases. This course explains why the team would use AutoML, BigQuery
      • ML, or custom training to achieve their objectives.
      • A deeper dive into custom training is presented in this course. We describe custom
      • training requirements from training code structure, storage, and loading large datasets
      • to exporting a trained model.
      • You will build a custom training machine learning model, which allows you to build
      • a container image with little knowledge of Docker.
      • The case study team examines hyperparameter tuning using Vertex Vizier and how it
      • can be used to improve model performance. To understand more about model
      • improvement, we dive into a bit of theory: we discuss regularization, dealing with
      • sparsity, and many other essential concepts and principles. We end with an overview
      • of prediction and model monitoring and how Vertex AI can be used to manage
      • ML models.
    • Activities:
    • • Hands-On Labs
    • • Module Quizzes
    • • Module Readings
Contact Us
03-6176666
03-6176677 info@sela.co.il

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