A short intro to MLOps
Machine Learning Operations, or MLOps for short, is the systematic practice of deploying and managing Machine Learning models. It’s a relatively new trend in the industry, but one that is rapidly gaining popularity among professionals and organizations. In fact, Gartner recently added it to its list of the top 10 data and analytics technology trends for 2021. If you’re a company that uses (or is planning to use) AI, MLOps should matter a lot in your business strategy, seeing as it can help tackle some of the challenges that machine learning projects are currently facing.
What is MLOps?
Whether you’re working in software development or in an industry close to it, you are likely to be familiar with the practice of DevOps. DevOps or Development Operations aims to shorten the development time and increase the quality of software.
DevOps achieves these efficiency goals by eliminating silos so that operations and development engineers work together throughout the software development lifecycle. At the same time, the DevOps way involves automating as many processes as possible.
MLOps follows the same pattern as DevOps, but also adds data engineering and data science to the mix. MLOps is to Machine Learning what DevOps is to infrastructure. MLOps focuses on increasing collaboration between data and operations teams, by automating the machine learning cycle.
Why we need MLOps?
In the past researchers in the field had to contend with both a lack of computing power and the fact that data was not as accessible or as plentiful as it is today. But things began to change as the volume of data started to increase exponentially as well as the computing power necessary to process it.
Moreover, compared to the days of data science past where data scientists pretty much worked alone, nowadays data teams are part of large organizational ecosystems. Navigating large organizations involves a lot more processes, tools and disciplines — from programmers and AI software architects to business analysts and product managers.
In order to provide a return on their investment in AI, companies need and have to learn how to scale data science projects. However, managing ML systems at a scale is no easy task. This is where MLOps comes in.
Benefits of MLOps?
The various disciplines involved in data science projects tend to work in silos. Often times, data scientists, backend engineers as well as production engineers don’t communicate with each other and struggle to collaborate.
In addition, while there are some tools such as Python or Docker which are used by all people working with data, each field has its own specialized solutions such as Jupyter for Data Science or Kubernetes for DevOps. Not to mention field-specific practices as well — one person’s Independent Variable is another person’s feature!
All these different engineering practices and tools can lead to a lot of challenges in communication — differences in terms of stack, or memory or hardware just to name a few. This lack of communication, at both a human and technological level, impacts the bottom line. It generates large delays in deployment, as well as missed opportunities for innovation.
MLOps requires a set of clear processes and documentation for the training and deployment of Machine Learning models. These processes’ explicit nature helps with communication and transparency. This is particularly the case in the diverse and high turnover teams that are quite common in industry.
With its focus on automation, MLOps allows your team to work on core tasks instead of spending time on repetitive ones, such as writing boilerplate code to acquire data, model deployment or infrastructure management.
For example, following the MLOps methodology, you can write an end-to-end, automated machine learning pipeline than can be triggered every time new data is available. This level of automation makes for a process where you can build, test, and deploy continuously. Rather than working on debugging operational faults your data team can work on the substantive task of improving models — which, chances are, is actually what they would much rather be doing!
The bottom line with MLOps is that it provides faster and safer deployment of machine learning models. Applying the MLOps strategy creates a clear set of operations and expectations for each team member. Such a degree of clarity reduces the scope for miscommunication allowing teams to get models to production faster. Additionally, MLOps reduces the number of time-consuming tasks your team has to perform to get data products (such as ML models) out.
This extra speed allows your company to get quicker feedback from the market. Which in turn gives you time to react to changes in market circumstances. And all this in a cost-effective way.
The amount of buzz around MLOps can feel daunting. It is worth remembering that MLOps emerged organically as a set of practices around reproducibility, monitoring, automated deployment and continuous integration, that data scientists and data engineers were already engaging in before there even was a name for this complex of practices. If you’re in data, you or your organization is probably doing a certain amount of MLOps already! Having a name for this “thing” does hint at a more structured process of thinking about machine learning a scale, and this is where it feels like the movement can bring the most value.
To learn more, perhaps the best resource out there is the mlops.community website. We are quite fond of the videos they record at their meetup. Watching Luke Marsden’s opening video is quite a good entrypoint to understanding the field better.
Originally published at https://scie.nz.