Radiant

Course Overview

What are the ideal methods for putting machine learning into practise on Google Cloud? What is Vertex AI, and how can it be utilised the platform without writing a single line of code to swiftly construct, train, and deploy AutoML machine learning models? What exactly is machine learning and what issues can it resolve?

Google has a slightly different perspective on machine learning: it focuses on offering a unified platform for managed datasets, a feature store, a way to create, train, and use machine learning models without building a single line of code, the ability to label data, and the ability to create Workbench notebooks using frameworks like TensorFlow, SciKit Learn, Pytorch, R, and others. Additionally, you can create component pipelines, train specialised models, and make realtime and batch predictions using our Vertex AI Platform. We also go over the five steps involved in transforming a candidate use case to a machine learning-driven use case and why it's important to finish each one. We conclude by discussing how to spot biases that machine learning can increase.

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Prerequisites

Some familiarity with basic machine learning concepts
Basic proficiency with a scripting language:
Python preferred

Audience Profile

Aspiring machine learning data scientists and engineers
Machine learning scientists, data scientists, and data analysts
Data engineers

Learning Objectives

Give an overview of the Vertex AI Platform and how it can be used to easily create, train, and deploy AutoML machine learning models without writing a single line of code.
How should machine learning be implemented on Google Cloud?
Utilize the resources and environment of the Google Cloud Platform to do ML Articulate Responsible AI best practises

Content Outline

Introduction to Course and Series

This module gives an overview of the course series and the Google experts teaching it.

What It Means to be AI-First

You investigate developing a data strategy around machine learning in this module.

How Google Does ML

The organisational expertise Google has accumulated over the years is shared in this module.

Machine Learning Development with Vertex AI

Every machine learning project begins with a goal, whether it's a commercial use case, an academic use case, or a problem you're attempting to solve. In the "proof of concept" or "experimentation" phase, this module examines the procedure for deciding if the model is prepared for production.

Machine Learning Development with Vertex Notebooks

This module explores managed and user-managed notebooks for machine learning development in Vertex AI.

Best Practices for Implementing Machine Learning on Vertex AI

This module discusses best practices for various machine learning processes in Vertex AI.

Responsible AI Development

This module explains why ML systems aren't fair by default and several considerations you should make as you include ML into your products.

Summary

This module is a summary of the How Google Does Machine Learning course.

Q: How Google uses Machine Learning?

A: Google is the master of all. It takes advantage of machine learning algorithms and provides customers with a valuable and personalized experience. Machine learning is already embedded in services like Gmail, Google Search, and Google Maps.

"Machine learning is a core, transformative way by which we're rethinking how we're doing everything. We are thoughtfully applying it across all our products, whether search, ads, YouTube or Play. And we're in the early days, but you will see us systematically apply machine learning in all these areas."

Q: How does Google use AI and machine learning?

A: Ranking web pages, movies, and other content in search results is the most popular application of AI in search engines. Google (and other search engines) use sophisticated AI to decide how to categorise material.

Q: What skills do users need for machine learning?

A: Machine learning engineers must have good skills in computer science and programming, mathematics and statistics, data science, deep learning, and problem-solving.

Q: What are the popular algorithms of machine learning?

A: Top 5 machine learning algorithms for beginners are listed below:-

Linear regression. …
Logical regression. …
Classification and regression trees. …
K-nearest neighbor (KNN) …
Naïve Bayes.

Q: How much RAM is need for machine learning?

A: For Deep learning applications, it is suggested to have a minimum of 16GB of memory; regarding the Clock, The higher, the better. It ideally signifies the Speed—Access Time, but a minimum of 2400 MHz is advised.

Q: How has google explained the working of machine learning?

A: The concept of "gradient descent" or "gradient learning" is a key component of the majority of machine learning, according to Google's Corrado. This indicates that the system makes the necessary small adjustments repeatedly until the situation is corrected. Machines must spend a lot of time learning how to do each step

Q: How do you ensure the quality of the training program?

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 on board 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.

Q: What is the infrastructure required to attend your 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.

Q: What if I miss a class on a particular day?

A: We 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.

Q: How will I be accessing the labs?

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 and in any time zone.

Q: What kind of projects are included as a part of training?

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.

FAQs