Course Overview
The ML Workflow is covered in this course using a real-world, hands-on approach. A case study method is used to show an ML team dealing with various ML business requirements and use cases. This team needs to be aware of the technologies needed for data management and governance and think about the best method for preparing data, which could range from giving an overview of Dataflow and Dataprep to using BigQuery.
To create machine learning models for two particular use cases, the team has three possibilities. This course covers the rationale behind the team's decision to employ AutoML, BigQuery ML, or customised training to accomplish their goals. In this course, personalised training is discussed in further detail. We outline the needs for customised training, including training code structure, storage, and loading of big datasets, as well as exporting a trained
Building a customised machine learning model will enable you to develop a container image without having much experience with Docker.
The Vertex Vizier hyperparameter tweaking technique is examined by the case study team to see how it can enhance model performance. We delve into some theory to gain a deeper understanding of model improvement. Regularization, sparsity, and many other crucial terms and ideas are covered. We conclude with a summary of model monitoring and prediction, as well as how Vertex AI can control ML models.
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Pre-requisites
Some knowledge with basic machine learning concepts Basic proficiency with a scripting language; Python preferred
Audience Profile
- Data Analysts
- Data Engineers
- Data Scientists
- ML Engineers
- ML Software Engineers
Learning Objectives
- List the methods for data management, governance, and preparation.
- Determine the appropriate times to employ Vertex AutoML, BigQuery ML, and custom training
- Implement tuning of the vertex vizier hyperparameters
- Describe how to use Vertex AI to set up model monitoring, establish pipelines, and make batch and online predictions.
Content Outline
This module provides an introduction of the course and its objectives.
This module explains the ML enterprise workflow and the purpose of each step.
This module recaps and discusses Google's enterprise data management and governance tools: Data Catalog, Feature Store, Dataplex, and Analytics Hub.
The art and science of the machine learning and neural networks are covered in this module. We'll also go through utilising Vertex AI to train unique ML models.
This module discusses how to do hyperparameter tuning using Vertex AI Vizier.
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, a service that helps manage the performance of your ML models.
This module discusses Vertex AI pipelines and how to build them to orchestrate your ML workflow.
This module reviews best practices for various machine learning processes in Vertex AI.
This module is a summary of the Machine Learning in the Enterprise course.
This module summarizes the Machine Learning on Google Cloud course series.
FAQs
A: Machine learning is a pathway to creating artificial intelligence, which is one of the enterprise's primary drivers of machine learning use. There is some disagreement over the exact nature of the relationship between AI and machine learning.
A: By unearthing priceless insights hidden in your corporate databases, machine learning aids enterprises in increasing efficiency and revenue. At every stage of a business, from developing new product concepts to getting the goods delivered to the customer, machine learning eliminates friction.
A: Machine learning provides vital data analytics that eCommerce companies can leverage to delve deeper into their data to identify and target high-value customers. For example, you can use ML to look at your total customer value and get early indicators that suggest a customer is a high lifetime value spender.
A: Radiant has highly intensive selection criteria for Technology Trainers & Consultants who deliver training programs. Our trainers & consultants undergo a rigorous technical and behavioral interview and assessment process before they are onboarded in the company.
Our Technology experts/trainers & consultants carry deep-dive knowledge in the technical subject & are certified by the OEM.
Our training programs are practically oriented with 70% – 80% hands-on training technology tools. Our training program focuses on one-on-one interaction with each participant, the latest content in the curriculum, real-time projects, and case studies during the training program.
Our faculty will quickly provide you with the knowledge of each course from the fundamental level, and you are free to ask your doubts at any time from your respective faculty.
Our trainers have the patience and ability to explain complex concepts simplistically with depth and width of knowledge.
To ensure quality learning, we provide support sessions even after the training program.
A: To attend the training session, you should have operational Desktops or Laptops with the required specification and a good internet connection to access the labs.
A: We would always recommend you attend the live session to practice & clarify the doubts instantly and get more value from your investment. However, if, due to some contingency, you have to skip the class, Radiant Techlearning will help you with the recorded session of that particular day. However, those recorded sessions are not meant only for personal consumption and NOT for distribution or any commercial use.
A: Radiant Techlearning has a data center containing a Virtual Training environment for participants' hand-on-practice.
Participants can easily access these labs over Cloud with the help of a remote desktop connection.
Radiant virtual labs allow you to learn from anywhere in the world and in any time zone.
A: The learners will be enthralled as we engage them in the natural world and Oriented industry projects during the training program. These projects will improve your skills and knowledge and give you a better experience. These real-time projects will help you a lot in your future tasks and assignments.