What are MLOps and how can they be useful to your business?
If you work in the tech world, you’re likely to have come across DevOps which is a software engineering practice, aiming to unify software development and operations. Its primary benefit is the elimination of time-consuming tasks from the development process. Through the automated testing of websites and apps, developers get instant feedback and integration, allowing them to make code changes much more quickly.
When DevOps first began to be adopted by businesses a few years ago, it was likened to the formation of factories and assembly lines during the industrial revolution in the way that it streamlined production and brought about greater efficiency. And now we’re coming into a new level of intelligent automation - enter MLOps.
What are MLOps?
As you can probably guess from the name, MLOps is essentially DevOps for machine learning. It’s the collaborative relationship between data scientists and the operations or production teams. It is designed not only to improve automation with the aim of streamlining repeatable machine learning processes, but also to provide deeper, more consistent, and more useful insights from ML.
What problems can MLOps solve?
MLOps have the potential to solve a number of problems that businesses are currently grappling with.
1. Lack of communication between machine learning and Ops teams
In many software houses, data scientists and operations professionals continue to work in silos, with a significant gulf between the two, and ongoing communication problems. MLOps combines the expertise of the two groups and leverages both skillsets, resulting in much more efficient processes.
2. Failing to keep up with new regulations and best practice
As machine learning becomes more common, there are new regulations surrounding the operational side and penalties can be invoked for failure to comply. MLOps places your Ops team at the forefront of such regulations, allowing them to focus on compliance, while leaving data scientists to do their job.
3. Bottlenecks in deployment
While many companies realise the benefits of incorporating machine learning into their digital products to both solve business problems and to deliver better outcomes for the end user, they are often met with problems when it comes to putting ML solutions into production. Too often, even the most innovative solutions proposed by ML developers reach a bottleneck when it comes to deployment. Here’s where MLOps comes in, removing tedious blockers.
4. Misused developer skills
It’s important to make the distinction between the capabilities of a data scientist and a software engineer. While the former works predominantly on data transformation and complex algorithms, the latter is focussed on the creation of the products which host these processes - namely websites and applications. MLOps effectively brings these two groups together to ensure that everyone is working to their strengths in order to embed machine learning effectively into apps and services.
Why is this important? These days, machine learning algorithms often grow far beyond their initial use, and therefore require highly complex operations systems - meaning that close collaboration is paramount.
So how do you implement MLOps?
So you’re probably now wondering what is involved when it comes to implementing MLOps into your production path? There are several ways of approaching this, but generally speaking the following stages should be considered: Collaborate, Build, Test, Release, and Monitor. Let’s go through each in a bit more detail below.
As described above, this is all about ensuring effective collaboration between developers, data scientists and the ops team. One additional factor to consider is that your ML team should be skilled in some of the basic software development skills including code modularization, testing and versioning in order to have a strong understanding of how their work will sit within the broader product.
2. Build using pipelines:
When it comes to machine learning, any form of build involves pipelines formed of extractions, transformations and loads - all of which form an essential element of data management. As data transformation is a key part of ML, pipelines are essential and should be built into your processes.
Any projects involving machine learning require a far greater level of monitoring than those without. Why? It’s because you need to ensure that you’re constantly operating within regulations and that quality information is being returned by your systems. For this reason, data may also need to be periodically retrained.
As explained by Geniussee: “In a traditional software world you need only versioning code because all behavior is determined by it. In ML things are a little different. In addition to the familiar versioning code, we also need to track model versions, the data used to train it, and some meta-information like training hyperparameters.”
Looping back to point 3 above, MLOps always needs to factor in continuous testing, both of the data and the models themselves, for reasons of efficiency and compliance. This means that each new version should be subject to scrupulous testing.
The importance of clear KPIs
As a final point, it is always advisable to have clear KPIs understood by all of your teams. Your Machine Learning teams need to have a strong grasp of the end goal, while your data team should use the knowledge gleaned from their testing and validation to propel the project in the right direction.
As with successful DevOps, the prime goal of MLOps initiatives is to abolish silos, improve cross-team collaboration and ultimately lead to more efficient processes. As data science continues to deepen and expand into new fields, there has never been a better time to embrace machine learning while at the same time ensuring that you have the best possible process with which to deliver it from the very outset.
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