Most courses in Artificial Intelligence and Machine Learning (ML) teach us how to learn techniques to build ML models for sample datasets such as the MNIST database. However, it turns out that deploying a ML solution that works in practice is much harder than simply training a specific ML model on freely distributed datasets.
This video is a 15 minute introduction to my upcoming course on how to design a ML solution from scratch and deliver it in production. Drawing from my experience, I will walk you through all the steps that you need to take to build a working ML solution. We will consider an interesting problem to solve, that of training a Vector robot to recognize another Vector robot in its proximity. We will then go about identifying the steps to accomplish a solution which can work efficiently and also be managed in production. We will use some common ML software and services: specifically Roboflow and ModelDB.
If you are interested in taking the full course, or in other educational material and tutorials I develop, please consider subscribing at LearnWithARobot.com
Негізгі бет Chapter 1: Introduction to MLOps: Taking Machine Learning from Scratch to Production
Пікірлер