November 2020
Beginner to intermediate
54 pages
1h 17m
English
Content preview from What Is MLOps?
Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.
Chapter 1. Introduction to MLOps and the AI Life Cycle
In the wake of the global health crisis of 2020, the question of scaling AI in the enterprise has never been more pressing. As many industries try to cope with the instability of a changing landscape, data science, machine learning (ML), and AI have moved from experimental initiatives to necessities.
Despite the growing need for AI to bring a newfound agility to a post-pandemic world, businesses still struggle to pivot their operations around these technologies precisely because it’s not simply a matter of technology; processes and people are also critically important. This report will introduce the data science, ML, and AI project life cycle so that readers can understand what (and who) drives these projects before covering MLOps (short for machine learning operations), a process that brings the required agility and allows for massive scaling of AI initiatives across the enterprise.
Why Are AI Projects So Complex to Execute?
It’s important to understand the challenges that AI projects present in order to properly address and overcome them with good MLOps practices. So, why are AI projects so complex, and why do so many organizations struggle to execute them (even those that succeed in other complex processes and in software development)?
There are two fundamental reasons for this.
Business Needs (and Data) Are Not Static
Not only is data constantly changing, but business needs shift as well. Results of ML models (i.e., ...
Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.
Read now
Unlock full access
More than 5,000 organizations count on O’Reilly
O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.