Every business is at a different place in their data journey.
For businesses who want to improve and scale their business much faster, there is an emerging profession within the data industry that is already being led by Big Tech and tech startups. Enter Machine Learning Operations (MLOps).
You may already have someone on your team who is working within the parameters of what this role can do for your business, but what if you structured your data team with a professional dedicated to the infrastructure that makes your business run more smoothly?
But before you can employ or even search for someone with such a specific skill set, it’s important to understand who you should be looking for, what they should be able to do, and how their efforts interact with your data team.
What’s driving the increase of MLOps for businesses?
When you know the right terminology to find the right person for the right role, that is when you can be confident in ensuring the infrastructure is being laid by those who understand its intricacies. As an emerging profession, one of the main trends we’ve seen is that of bringing on more Data Specialists in the last few years who can write high-level production code into production. Even pre-pandemic, organizations were hiring PhDs and master’s degree professionals with commercial experience who wrote these high-level algorithms that could identify unique segments of the market.
Regardless of the industry, there was a central question that evolved, which asked, “How do we scale from here”? and “How do we create a data platform that is able to go from 0 to 1”? But asking the question, how do we scale wasn’t the whole question when it came to this area of Machine Learning Operations (MLOps). If you flip the acronym and consider that all businesses have an internal operations department that is responsible for ensuring best practices, then it becomes Operations of Machine Learning. Then, from there, determining best practices to enable your business to scale faster ensures you’re more able to keep up with the competition.
Five Best Practices
MLOps is often labeled incorrectly. This is not a one-size-fits-all role and requires someone with deep knowledge of Python, Engineering, and Computer Science, and has worked in a business that has scaled.
- Deep knowledge of Python.
- Have experience bringing a company to scale.
- Create a machine learning platform that when new data comes in there is a change in the market, a change in product, a change in whatever your type of data is you have the infrastructure to scale something quickly.
- Ensure you have the right machine learning platform, or it will be difficult to get the most out of your Data.
- Make sure your Data is accurate and strong so your MLOps Engineers can get the most out of it.
MLOps Engineers should be excellent with Python, own the engineering side, and understand how all the pieces fit together and be able to create, maintain, and deploy the models. Companies are trying to get there, but so many of the hires for a field this new are first hires. Businesses need someone who understands the intricacies of the engineering of these platforms.
It’s important for businesses to understand what they’re looking for because even if you don’t have anyone with the title of MLOps on staff, you most likely have someone who is doing the role already regardless of title. Your goal, whether you realize it or not, is make your ingest Data faster, so it can be more usable, and you can learn more from it.
Deciding Between MLOps and Machine Learning? This May Help.
There are two sides to every opportunity and MLOps is no different. If you’re interested in seeing how your work directly impacts and affects the business, then MLOps may be the role for you. When you’re this specialized, there is increased visibility, increased monetary value, and though it may not be as creative as other endeavors within the Data team, you’re where the buck stops when it comes to ensuring the algorithms and Data work together to move the business forward.
A quick example: Imagine you are part of a team working to get a book written and in stores. The author is the Data Scientist who researches, writes, and creates. The Data or Software Engineer is the Editor who makes sure everything is clean before it goes out the door. The MLOps Engineer is the Publisher who gets the book into stores so readers can find and read it. A book isn’t much to good to someone if it’s not where they can access it. Same goes for Data. The Data Scientist, Engineers, and MLOps work together to scale the business each in their specific role.
Companies committed to tech whether a tech startup, Big Tech, or a business that knows it needs tech to stay competitive also know that adding MLOps to their Data strategy is an absolute must to stay on the cutting edge.
If you’re interested in MLOps, ML Engineering, Data Engineering, or Data Science just to name a few, Harnham may have a role for you. Being on the cutting edge of emerging Data industry trends is our specialty.
Check out our latest MLOps jobs or contact one of our expert consultants to learn more.
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