This course takes a real-world approach to the ML Workflow through a case study. An ML team faces several ML business requirements and use cases. The team must understand the tools required for data management and governance and consider the best approach for data preprocessing.
Machine Learning in the Enterprise
Machine Learning in the Enterprise
This course is part of multiple programs.
Instructor: Google Cloud Training
Sponsored by EmployNV
33,425 already enrolled
Gain insight into a topic and learn the fundamentals.
4.6
(1,472 reviews)
Intermediate level
Some related experience required
Flexible schedule
Approx. 19 hours
Learn at your own pace
81%
Most learners liked this course
What you'll learn
Describe data management, governance, and preprocessing options
Identify when to use Vertex AutoML, BigQuery ML, and custom training
Implement Vertex Vizier Hyperparameter Tuning
Explain how to create batch and online predictions, setup model monitoring, and create pipelines using Vertex AI
Skills you'll gain
- Process Improvement and Optimization
- Machine Learning Software
- Cloud Services
- Cloud Management
- Data Strategy
- Digital Transformation
- Data Pipelines
- Process Management
- Cloud Solutions
- Hybrid Cloud Computing
- Business Process Improvement
- Business Process Management
- Workflow Management
- Data Management
- Artificial Intelligence and Machine Learning (AI/ML)
- Databases
- Cloud Computing
- Data Governance
- Multi-Cloud
- Public Cloud
- Cloud Infrastructure
- Continuous Improvement Process
- Data Architecture
- Data Integration
- Business Management
- Operations Management
- Information Management
- Cloud Applications
- Applied Machine Learning
- Data Engineering
- Data Storage
- Cloud Security
- Data Processing
- Operational Excellence
- Computer Science
- MLOps (Machine Learning Operations)
- Information Technology
- Google Cloud Platform
- Cloud Platforms
- Extract, Transform, Load
- Big Data
- Business Operations
- Machine Learning
Details to know
Shareable certificate
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Assessments
6 assignments
Taught in English
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There are 10 modules in this course
This module provides an overview of the course and its objectives.
What's included
1 video
This module discusses the ML enterprise workflow and the purpose of each step.
What's included
2 videos1 reading1 assignment
This module reviews Google’s enterprise data management and governance tools: Feature Store, Data Catalog, Dataplex, and Analytics Hub.
What's included
9 videos1 reading1 assignment1 app item
This module reviews the art and science of machine learning and neural networks. We'll also discuss how to train custom ML models using Vertex AI.
What's included
7 videos2 readings1 assignment1 app item
In this module we discuss how to do hyperparameter tuning using Vertex AI Vizier.
What's included
3 videos1 reading1 assignment1 app item
This module covers Vertex AI prediction and model monitoring. We'll first discuss batch and online predictions using pre-built and custom containers, then we'll review model monitoring, which is a service that helps manage the performance of your ML models.
What's included
4 videos1 reading1 assignment1 app item
This module discusses Vertex AI pipelines and how to build them to orchestrate your ML workflow.
What's included
3 videos2 readings1 assignment2 app items
This module reviews best practices for a number of different machine learning processes in Vertex AI.
What's included
5 videos1 reading
This module is a summary of the Machine Learning in the Enterprise course.
What's included
4 readings
This module is a summary of the Machine Learning on Google Cloud course series.
What's included
1 video1 reading
Instructor
Instructor ratings
4.6 (81 ratings)Offered by
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