What Is MLOps technology?

Machine learning (ML) is the ability of a computer system to adapt without human input, by using algorithms and statistical models to analyze and draw inferences from patterns in data. Machine learning systems can help businesses forecast market trends, personalize advertisements, and approve customer financing, among countless other use cases. But what happens when the algorithm is wrong?

Left to their own devices, algorithms can advance in a way that leads to poor investments, discriminatory lending practices, and even car accidents (in the case of self-driving cars). While oftentimes impressive, machine learning models can make incorrect decisions based on data that leads to detrimental actions.

Incorporating model monitoring as part of a comprehensive machine learning operations (MLOps) framework will not only save your company money by correcting algorithms, it will also help you conduct business more ethically. In this post, we break down everything you need to know about MLOps.

What is MLOps?

MLOps is a set of practices that aims to deploy and maintain machine learning models in production reliably and efficiently. MLOps is an act of collaboration between operations professionals, data scientists, and ML/AI engineers to automate the deployment of machine learning while improving quality and simplifying the management process. MLOps puts a system in place so you can implement, monitor, and improve your machine learning to ensure that it supports your strategic goals. 

For highly regulated industries like banking, insurance, and healthcare, MLOps also places emphasis on complying with strict regulations through a model governance framework.   

MLOps lifecycle

The MLOps lifecycle is broken down into nine parts. Here is how each step contributes to the overall goals of MLOps:

1. Problem Definition: Identify which problems you want your AI to solve.  
2. Data Collection: Gather relevant, unbiased data for model training.
3. Data Processing and Storage: Sort your data into distinct batches for more efficient processing. 
4. Metrics Definition: Set benchmarks to determine whether or not your AI is solving the problems it is being designed to.
5. Data Exploration and Analysis: Choose the modeling techniques to start with that will make the most of your data.
6. Feature Extraction and Engineering: Determine relevant data points and ensure they are updated regularly.
7. Model Training and Offline Evaluation: Test different models to see which ones yield the best results.
8. Model Integration and Deployment: Implement effective models into the product using a cloud system to allow access by the end-user.
9. Model Release and Monitoring: Observe the model’s performance and identify opportunities for improvement and retraining.

The MLOps lifecycle relies on continuous improvement to function properly. Build feedback into your systems to catch inaccuracies before they lead to major issues, such as data drift or model bias.    

What is an MLOps platform?

An MLOps platform is the control center to help data scientists and operations professionals implement, monitor, and improve machine learning processes. Effective MLOps platforms provide you with a 360 degree view of your machine learning lifecycle, so your team can take informed actions. 

What is the difference between MLOps and DevOps?

The key difference when comparing MLOps vs DevOps is that MLOps focuses on improving machine learning, while DevOps is geared toward software development and performance. So, what is DevOps, and how do its core principles relate to those of MLOps? Development operations (DevOps) teams are typically composed of software engineers that create and improve code for periodic releases. 

In contrast, MLOps teams involve collaboration between operations professionals and data scientists. MLOps also tends to be more experimental than DevOps; models are tweaked as they operate and machine learning is constantly performing tests to retrain and improve effectiveness. 

Why is MLOps important?

MLOps is a key component of explainable AI, which exists with the principle that understanding your AI allows you to change it for the better. To illustrate how important this is, it’s easier to look at a case that lacked effective MLOps. 

An MLOps example gone wrong was reported in 2018 by NPR. This interview discusses how banking algorithms for approving home and auto loans ended up discriminating against minorities, despite having no information related to each applicant’s race or ethnicity. Instead, factors like zip code and a lower tendency to shop around resulted in higher rates for African-American and Latino borrowers. Discrimination is not only against regulations, it also harms an institution’s public perception. 

With an effective MLOps framework in place, these banks could have identified that their algorithm was weighing certain factors incorrectly, skewing results. If the problem had been discovered early on, they could have adjusted their inputs, improved their algorithm’s effectiveness, and avoided bad press and disgruntled clients.     

MLOps can also improve your business by increasing workflow efficiency, reducing bottlenecks, and ensuring compliance with the latest regulations.

Where do I start with MLOps?

When it comes to machine learning, it can be tempting to set it and forget it. However, trusting algorithms to guide your business doesn’t always end well. In order to ensure that your machine learning is consistently working in your favor, you need model monitoring tools to monitor your AI models and correct inaccuracies—before they encourage detrimental decision making.

All too often, ML models are a black box; all the information is inside, but no one can access it. The key to improving your machine learning is understanding it. Try Fiddler for free to see how we can help you optimize your machine learning models with our AI observability platform.