Artificial Intelligence and Machine Learning  are no longer science fiction. Looking for a way to connect AI and ML to robots and labs? Then it’s time for you to meet MLOps!

Today, Machine Learning is crutial to producing models capable of solving previously unsolvable problems. For this to succed, the model must be implemented correctly, by a trained team aware of all stages within this process. 

Yes, Machine Learning has joined the Ops family, which contains DevOps, DevSecOps, and DataOps… but putting an ML model into production is not that simple. Only 22% of companies that use Machine Learning have successfully implemented this model.

What is MLOps?

The acronym MLOps stands for Machine Learning Operations. 

MLOps provides best practices for your corporation to work with Artificial Intelligence. 

In other words, MLOps is nothing more than a way to guide the processes of IT teams and data scientists, with the help of various software products and cloud services.

In an even more straightforward way: MLOps are the practices to work with Artificial Intelligence within the business environment successfully. 

You might be wondering why this is so important. Well, Artificial Intelligence has been impacting and modifying business models in different segments worldwide. 

There are many examples in operation, and some are already revolutionizing the way companies position themselves in their markets. 

To get deeper into this subject, check out our guide “Artificial Intelligence: Understanding it’s Role in Transforming Business Models.”

How does Machine Learning Ops work?

To explain, let’s imagine the following situation:

Your corporation has decided to start investing in Machine Learning. It decided to skip a step and already has a team of experts capable of producing the models needed to solve your problems.

Performance metrics are calibrated, dashboards are working, demos are running, and the team is familiar with DevOps. 

Along comes a stakeholder with a common question: how long will it take for us to have a model in production?

You would think “quickly,” right? Wrong. That’s because Machine Learning isn’t just about code. And that’s where the big challenge lies.

Machine Learning = code + data

Codes are possible to control and predict. Data, not so much. Data comes from outside and not from a controlled environment. That is, it can (and will) change all the time. And you have no control over it. 

But you don’t need to abort the mission just yet. That’s where MLOps comes into play! Using the best practices will allow your business to get the most out of Artificial Intelligence and generate value for your business.

Differences between MLOps, DevOps and DataOps

Now that you understand the concept of MLOps, you might have been in doubt when we said that having a team running on the DevOps model wouldn’t necessarily make a difference. 

This happens because the Ops family has models with different applications, despite having the same origins. Let’s define each concept to clarify the differences between them.

• DevOps: Development + Operations

It involves automating IT governance – and it’s even more effective with the application of Agile. DevOps integrates the development and operations area. 

We’re talking about integration practices that favor a more secure and agile development environment. It’s already a widespread concept in the business world.

• DataOps: Agile + DevOps + Statistical Process Control

DataOps brings best practices that will break down barriers and complications between development and analytical operations.

DataOps is a culture and a methodology. It’s an agile development practice that brings DevOps teams together with engineers and data scientists, to support data-centric decisions and strategies.

• MLOps: Machine Learning + DevOps + Data Engineering 

MLOps is modeled on the DevOps discipline: software developers (the Devs) + IT operations (Ops).

MLOps adds data scientists to that equation. They will be responsible for preparing the datasets and creating Artificial Intelligence models to carry out the analysis. 

In addition to scientists, we can also include Machine Learning engineers, who will run the dataset models in an automated way.

Where to start: Five first steps to implement an MLOps model

1. Hybrid Teams

The Agile methodology is already premised on multidisciplinary teams for the success of projects. 

The same is true here: it is implausible that one person can do everything. Therefore, a hybrid team meets the full range of skills needed to make MLOps run successfully. 

2. About the environment

It is essential to consider everything your team will need before starting your project. 

From hardware to software, check what will be needed right from the start.

3. DevOps Knowledge

Understand what DevOps is. The success of one model is not necessarily linked to the other, but having knowledge on the subject will help a lot.

If you’re new to DevOps and Machine Learning, experimentation and production will require more focus. The idea is to separate the two.

4. Prepare your data

It’s essential to capture new data and keep your models up to date. 

There are a lot of good tools online that can help you in this regard.

5. Model Training and Assessment

Define and understand what KPIs and metrics will be used to construct a successful model. If you don’t have any parameters, it will be difficult to establish whether the overall performance of one model, at the expense of another, is good enough.

At the end of the day, what matters is that each team finds the combination of MLOps products, services, and practices that best fit their cases. 

The vast majority of existing tools for this purpose create an automated way to run AI fluidly as part of a corporation’s digital life.

Got any questions about how to start MLOps in your corporation? Our data scientists can help. Get in touch with us, and let’s talk about your challenges!