Guide to End-to-End Machine Learning Projects

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About the webinar

Over the course of 45 minutes, we will discuss how to efficiently manage machine learning projects. Though machine learning projects may seem similar to any software engineering endeavor, the reality is machine learning projects are onerous, demand high quality work from every person involved, and are sensitive to any tiny mistake.

Skyl end to end machine learning projects

It seems that we cannot go five years without having some massive technology shift that becomes an essential part of our day-to-day lives. So, we will start with a proper definition of machine learning and how it is changing the way businesses analyze information. We will then continue by discussing proper ways to begin machine learning projects, including weighing the feasibility of a project, planning timelines, and the stages of the machine learning workflow once you start your project.

After exploring the stages of the machine learning workflow, we will end the webinar with an example of a completed machine learning project. We will demonstrate how to create a similar project and give you the tools to create your own.

What you will learn

A deeper understanding of the end-to-end machine learning workflow

The tools needed to effectively create, design, and manage machine learning projects

The skills to define your goal, foresee issues, release models, and measure outcomes during the ML project lifecycle

Demo: Skyl Platform for End-End machine learning workflow

Speaker(s) & Panelist(s):

speaker

Bikash Kumar Sharma

Chief Technology Officer at Skyl.ai

CTO & Software Architect with close to 15 years of experience whose passion lies in building great products while enabling others to perform their roles more effectively. Lead at the forefront of innovative projects, building future tech using Machine Learning and Artificial Intelligence.