Defining MLOps as Simply As Possible — “What is MLOps?” — Part 2
Hey everyone, in this series I’ll be diving into the “What” and “Why” of MLOps.
This is Part 2 of the multi-part series that currently includes (& will include):
- Part 1: Introducing the series: “What is MLOps?”
- Part 3: Why ML Ops Matters: Taking the ‘Oh Sh*t’ out of MLOops
- Part 4: Goals of MLOps as Themes (Substack)
- Part 5: Software and ML Before MLOps (Substack)
- Part 6: The Challenges of Scaling ML As Software (Substack)
Links will be updated as the sections are published. Sections tagged with “Subtack” will be available in their entirety at my substack for free.
Note: This is going to be one of the shortest parts pf the series because 📰 newsflash: I don’t think the definitions are that complicated 📰.
Execution of that definition though… woah baby, that’s where the devil’s in the details.
There Are Many Definitions…
MLOps, or Machine Learning Operations, is becoming the new buzzword that has people scratching their heads confused and going “Do we really need another confusing job title in data that basically sounds made up?”.
First we had Data Scientists, which were then followed by Machine Learning Engineers and Data Engineers, and now we have MLOps Engineers and DataOps.
And just like how everyone had a different definition of Data Science (and now Data Engineering), you’ll find as many definitions of MLOps as there are people.
For example, a sample of definitions out there include:
“A set of practices that aims to depoy and maintain machine learning pipelines in production reliably and efficiently” (source: Wikipedia)
“DevOps is behavior, just like data science. Think about the principles of both DevOps and data science. In both cases, DevOps and data science are methodologies for evaluating the world, not necessarily unique job titles.” (Book: Practical MLOps)
“At its core, MLOps is the standardization and streamlining of machine learning life cycle management” (Book: Introducing MLOps)
“Discipline trying to make the development and deployment of ML systems systematics” (Video: Data-Centric AI)
“MLOps refers to the set of practices and tools to deploy and reliably maintain machine learning systems in production. In short, MLOps is the medium by which ML enters and exists in the real world.” (Blog: MLOps is a Mess)
“Ops in MLOps comes from DevOps, short for Developments and Operations. To operationalize something means to bring it into production, which includes deploying, monitoring, and maintaining it. MLOps is a set of tools and best practices for bringing machine learning into production.” (Blog: MLOps)
“In another word, MLops = DataOps + ModelOps + DevOps.” (Blog: Why Do You Need It?)
And the venn diagrams… let’s not forget those (From Left -> Right, Top -> Down):
Web Frameworks for Data Scientists, and Why You Should Care.
Or the pseudo equations, let’s not forget those either:
Part 1: An Overview of DataOps For Computer Vision
…They’re All Kind Of Saying The Same Thing
However different the definitions might sound on the surface, they’re hitting at the same sentiment.
A fair encapsulation then is:
- MLOps is the practice of productionizing machine learning artifacts in a scalable and reliable manner, where “artifacts” can include projects, applications, services, and pipelines.
- MLOps is the application and adaptation of Software Engineering and DevOps principles and practices to ML artifacts.
It then follows that:
- An MLOps System or Platform is a collection of tooling and processes that enables the systematic development and productionization of machine learning artifacts.
- An MLOps Team is a collection of individuals focused on the design, development, and maintenance of the MLOps System (or Platform).
- An MLOps Tool is technology (in the form of either a library, framework, service, or platform) that addresses a specific stage of machine learning artifact lifecycle.
Great, Now Why Should I Care? Also I Have More Questions. This was super short.
Awesome! Because all the other parts of this series are, like, hella long.
Why should a company, organization, or even you as an individual contributor care?
We’re going to div deper into the reasons why in the next part (Part 3: Why ML Ops Matters: Taking the ‘Oh Sh*t’ out of MLOops) but it comes down to:
- Mitigating Risk and Bias
- Alleviating Market Shortages of Talent and Bolstering Existing Talent
- Unlocking Competitive Innovation Through Reducing Toil
So if you’re interested in being able to pitch MLOps initiatives, part 3 will be most relevant.
Series List
This is Part 2 of the multi-part series that currently includes (& will include):
- Part 1: Introducing the series: “What is MLOps?”
- Part 3: Why ML Ops Matters: Taking the ‘Oh Sh*t’ out of MLOops
- Part 4: Goals of MLOps as Themes (Substack)
- Part 5: Software and ML Before MLOps (Substack)
- Part 6: The Challenges of Scaling ML As Software (Substack)
Links will be updated as the sections are published. Sections tagged with “Subtack” will be available in their entirety at my substack for free.
About Me
My name is Mikiko and I currently work as a Sr MLOps Engineer at Mailchimp.
Before pivotting to MLOps I’ve also worked as a Data Scientist, Data Analyst, and Growth Hacker at various companies in the Bay Area.
If you’re interested in my various career leaps and breakthroughs, check out these series:
- 👩🏻💻 Miki’s 🔥Hot-Takes🔥 on MLE Interviews: Types of Roles & Interview Prep — Part 1 & Part 2 — Where I talk about getting my role (which was initial ML Engineering before the team converted to MLOps)
- ✂️Breaking into Data Science- From Hair Salon to Data Scientist 🔍 — Part 1, Part 2, Part 3, Part 4 — Where I go into exhaustive detail about preparing for getting a job as a data scientist.
If you’re interested in learning more about MLOps, production ML, and distributed systems + cloud development, I also publish:
- Videos on Youtube at The MLOps Engineer
- Technical walkthroughs on Substack
- Snark and saltiness on Twitter
- Professional sounding/career oriented stuff on LinkedIn
- Code gists and TODOs on Github
And I take support and patronage in the form of coffees ☕!
