If you work in the space of data, analytics, and technology, odds are you have heard some (if not all) of the following phrases:
Artificial intelligence. Proof of concept. Scaling. Neural networks. Pipelines. Cloud computing. Agile. Production.
The one connecting theme that all of those concepts have in common is Machine Learning Operations, otherwise known as MLOps. As data science continues to grow, it’s extremely important to understand MLOps and why it matters in your organization.Â
Here is a simple example to help you understand MLOps in action:
Imagine that you made an amazing batch of chocolate chip cookies. You spent time perfecting the recipe in your kitchen and you were able to get some local bakeries to agree to sell your cookies in their stores. While that is exciting, it’s a little nerve wracking because your kitchen is only able to produce so many cookies in a given day.Â
In order to meet your production needs, you have to open a small bakery, get some industrial baking equipment, and hire on a staff. Once you get your shop running, fulfilling the orders seems less daunting and everything is working like a well-oiled machine and your cookies taste just as good as they did when you made them in small batches.
What is MLOps?
MLOps aren’t too different. MLOps are a way of working between data engineers, data scientists, and IT managers to take a good idea and turn it into an analytical tool that can be used across the organization. MLOps are the process of taking a data science approach and working with the appropriate teams to get it integrated into the right IT systems to fit the needs and size of the organization. Instead of cookies, it’s taking a good model that a data scientist created and integrating the idea with larger data sets and IT systems.
Why Is MLOps Important?
In a bakery, the person servicing the machinery and the baker have very different skills. I would not want a baker to fix a piece of equipment and I would not want the mechanic to make the cookies. They should be focused on the parts of the bakery that they are skilled at.Â
MLOps provides open communication between the multiple teams that are impacted by a project, but allows them to focus on what they are skilled at.
It all starts with letting data scientists get creative in finding modeling applications within a business. They will involve data engineers to help them navigate and massage data to get the right inputs for the model. The data scientists will focus more on the modeling techniques and their implications. Once the results look promising, then they’ll involve other IT teams.Â
Involving the IT teams makes sure that the model will run at the right time with the right amount of resources without messing up any other programs that are running. Having constant and open communication between these teams allows each to focus on what they are good at, often resulting in more synergies throughout the process.
How Do You Implement MLOps?
Are you now interested in bringing in MLOps to your organization? Great! Here are a few things to think about while getting started:
- What are the immediate and long term technology goals of the organization?
- Who in my organization is responsible for data engineering, data science, and IT?
- Can we build a culture of open communication?
- What business units would be interested in leveraging data science and technology to help them with their day-to-day operations?
