Course Overview
Participants learn about MLOps tools and best practises for installing, assessing, operating, and monitoring production ML systems on Google Cloud in this course. The deployment, testing, monitoring, and automation of ML systems in production are the main goals of the MLS discipline. Professionals in machine learning engineering employ tools to continuously analyse and enhance deployed models. They collaborate with (or are themselves) data scientists who create models to speed up and improve the deployment of the top-performing algorithms.
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Prerequisites
Completed Machine Learning with Google Cloud or have equivalent experience
Audience Profile
Data scientists wanting to create business impact swiftly move from machine learning prototype to production. Software engineers who want to learn about machine learning. ML Engineers interested in utilising Google Cloud.
Learning Objectives
- Choose and implement the essential technologies for effective MLOps.
- Adopt the most effective CI/CD procedures when dealing with ML systems.
- Configure and set up Google Cloud environments for dependable and efficient MLOps.
- Implement workflows for training and inference that are dependable and reproducible.
Content Outline
This module provides an overview of the course.
In this section, we examine machine learning from the standpoint of operations. This entails adopting a holistic viewpoint, starting with the problem's definition and ending with the solution.
This module is optional.
We'll talk about a Google Cloud product called AI Platform Pipelines in this module that makes using Google Cloud Services for MLOps simple, frictionless, and scalable.
In this module, you will gain knowledge on how to manually train, tune, and manually serve a model from the Jupyter notebook on the AI Platform.
Using a Kubeflow pipeline, this module will automate the training and tweaking procedures we previously detailed. After we have defined the many processes as a Kubeflow pipeline, we can start the complete process with a single click rather than manually triggering each step from the Jupyterlab notebook.
We will talk about CI/CD for Kubeflow pipelines in this module. How can we incorporate an automated Kubeflow pipeline into a continuous integration stack now that we know how to design one? The intention is to immediately rebuild pipeline assets after new training code is uploaded to the associated repository.
This module is a recap and summarization of what was covered in the course.
FAQs
A: Machine learning operations offer the methods and tools needed to implement, oversee, control, and regulate machine learning in real-world settings. Scaling the number of machine learning-driven applications in an organisation requires MLOps.
A: Exploratory data analysis (EDA)
Data Prep and Feature Engineering.
Model training and tuning.
Model review and governance.
Model inference and serving.
Model monitoring.
Automated model retraining.
A: ML Engineers build and retrain machine learning models. MLOps Engineers enable the ML Engineers. MLOps Engineers develop and maintain a platform to allow the development and deployment of machine learning models. They typically do that through standardization, automation, and monitoring.
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 and in any time zone.
A: The learners will be enthralled as we engage them in the natural world and industry Oriented 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.