Machine Learning Operations, or MLOps is a relatively new concept.
Starting back in 2014 there were rumbling of the challenges connected with deploying machine learning models into production. This started to lead to machine learning platform start-ups like DataRobot and H2o.ai gaining a decent amount of traction and funding.
Somewhere around 2018 the term MLOps started getting thrown around these platforms and the term stuck.
So what is the goal of MLOps. The goal of MLOps is to help simplify the management, logistics, and deployment of machine learning models between operations teams and machine learning researchers.
From a naive perspective it is just DevOps applied to the field of machine learning.
But, MLOps actually needs to manage a lot more than what DevOps usually manages.
Like DevOps, MLOps manages automated deployment, configuration, monitoring, resource management and testing and debugging.
Also similar to DevOps, many of these MLOps platforms aren’t free.
So before investing in MLOps, managers need to ask themselves a few questions.
One of those being, how will the tool or discipline improve their team or strategy.
Here are some reasons why your team may benefit from MLOps.
Simplify Complex Deployments Like DevOps
Software in itself is complex and difficult to develop, manage, maintain. This is one of the main reasons DevOps became very popular.
Machine learning does not only require a decent amount of software engineering, but it also requires a lot of other best practices in order to manage data, validate models, feature storing etc.
This is where MLOps comes in. Lots of MLOps tools help to manage this pipeline. They help software and machine learning engineers move away from manually using one off scripts to prepare the data, train it, validate it, deploy it to ops, and then have ops manage the machine learning services.
Managing The ML LifeCycle
As part of simplifying complex deployments some MLOps platforms look to automate key steps that are unique to the machine learning model lifecycle.
For example, the training portion of your model isn’t a normal step in your Devops process. But it’s important to ensure the result that the model outputs is the expected result.
For those who are familiar with machine learning models, you will understand that depending on multiple factors such as feature engineering and your training data set the output of the model might not be consistent. Your goal might be to use new data to retrain the model in order to allow the model to adjust. This means you will also need to also revalidate the model and the data. To ensure that the models output is still reasonable as well as the data is not poor quality or skewed.
In the perfect MLOps world, this whole process will be automated. Allowing for easy deployment and testing.
Scaling Machine Learning Applications
Another benefit of MLOps is improved management of machine learning models at scale. Having a centralized system that logs, tracks metrics and helps team maintain thousands of models is necessary as companies become more data driven.
Just putting models out there without any form of centralized system geared towards not just logging code but how the models react in the wild is important for many reasons. For example, what if the model starts accidentally producing a negative output where customers or end-users are having a bad experience.
But your company is using hundreds of models and the output that end-users are experiencing could be based on one of hundreds of said models. How would you know where the problem is quickly?
A big part of code and machine learning is not building but maintaining and debugging.
This is somewhat connected to activity monitoring. However, monitoring at scale is just one of the “at scale” benefits of having a centralized MLOps platform offers.
Active Monitoring And Tracking
Just deploying a model and assuming it will always work as expected in a bad idea. One great example of this was Google in 2008 had worked on developing a model that they claimed could “nowcast” the flu based on people’s searches.
This actually proved a very effective method for predicting flu outbreaks early on. However, a few years after doing a decent job and helping map flu outbreaks the model failed miserably.
Google’s flu tool in 2012–2013 as well as 2011–212 overestimated the prevalence of flu by more than 50%. This is not abnormal when you develop models and put them out into the wild. Models may have been designed poorly or may have overfit their training data. In turn this leads to models that act differently in production compared to how they acted in development.
This is why machine learning engineers shouldn’t just develop a model and deploy it.
It’s important that they are also monitoring their models output. When our team discussed this with Andy Dang of WhyLabs.ai he discussed the importance of even basic monitoring. Even monitoring things like CPU usage, error rates and recovery time, can provide a lot of information for the operational team.
However, this means creating systems that do just that. That’s where a lot of products like WhyLabs are attempting to fill in the gap as far as making sure your model does not become stale or starts performing improperly.
Why Use MLOps?
MLOps can provide a lot of benefits for teams that are looking to integrate machine learning models into their software. It can help simplify your deployment process, improve the actually maintenance and operation as well as improve the ability for your model to scale.
Developing machine learning models is complex enough. Spending time manually deploying and managing a model in production isn’t scalable. Thus, looking for tools and machine learning platforms that can help your team spend less time maintaining and more time dealing with real operations issues can save your company both in costs as well as in possible newspaper headlines.
With that, we wish you luck in your machine learning projects!
If you are interested in reading more about data science or data engineering, then read the articles below.
What Are ETLs And Why Your Team Should Use It?
4 SQL Tips For Data Scientists
What Are The Benefits Of Cloud Data Warehousing And Why You Should Migrate
5 Great Libraries To Manage Big Data With Python
1 thought on “Why Your Team Should Consider Using MLOps To Manage Their Machine Learning Models”
Comments are closed.