During the industrial revolution, the rise of physical machines required organizations to systematize, forming factories, assembly lines, and everything we know about automated manufacturing.
During the first tech boom, Agile systems helped organizations operationalize the product lifecycle, paving the way for continuous innovation by clearing waste and automating processes for creation. DevOps further optimized the production lifecycle and introduced a new element, that of big data.
With more businesses now turning to machine learning insights, we’re on the cusp of another wave of operationalization. Welcome to MLOps.
[Related article: The 2019 Data Science Dictionary – Key Terms You Need to Know]
What Is MLOps?
MLOps is communication between data scientists and the operations or production team. It’s deeply collaborative in nature, designed to eliminate waste, automate as much as possible, and produce richer, more consistent insights with machine learning. ML can be a game changer for a business, but without some form of systemization, it can devolve into a science experiment.
MLOps brings business interest back to the forefront of your ML operations. Data scientists work through the lens of organizational interest with clear direction and measurable benchmarks. It’s the best of both worlds.
Why Should My Organization Adopt MLOps?
Contrary to what you may think, MLOps allows your data scientists freedom to do what they do best – find answers. Take business decisions off their plates, and they can build and deploy models that get your insights more quickly.
Think about it. You didn’t hire your data team to understand the ins and outs of your industry. You didn’t hire them to keep up with regulation. You hired them for their skills in information gleaning. Remove the barriers and let them find your answers.
MLOps follows a similar pattern to DevOps. The practices that drive a seamless integration between your development cycle and your overall operations process can also transform how your organization handles big data. Just like DevOps shortens production life cycles by creating better products with each iteration, MLOps drives insights you can trust and put into play more quickly.
[Related article: 5 Mistakes You’re Making With DataOps]
What Problems Will MLOps solve?
Data should always have a business focus. Operationalization helps to close the loop between gaining insight and turning that insight into actionable business value. A simple premise but not so simple of an execution.
Adopting an MLOps approach could help your organization in the following ways:
- Your operations team has the business knowledge, and your data science team understands the data. In between? A wide gulf of mismatched expertise. MLOps combines the expertise of both camps for more efficient ML that leverages both sets of skills.
- As ML becomes more common, the regulatory side of operations is a critical function. Run afoul of regulatory bodies, and it won’t matter how much insight you’ve gleaned. MLOps puts your operations team at the forefront of new regulations and best practices. They can take ownership of regulatory processes while your data team concentrates on deploying creative models.
- The bottleneck that results from complicated, non-intuitive algorithms eases with a better division of expertise and bigger collaboration from operations and data teams. MLOps tightens the loop.
So How Do We Introduce MLOps?
There’s a good chance your ML is optimizing a particular business application, but that particular application requires multiple programs and dependencies. MLOps streamlines deploying those programs and knowing which to put into production.
Consider a few things before you build MLOps into your applications. Bringing ML into production does require your organization settle a few things before your model can officially be known as MLOps.
- What are the benchmarks? – Your KPIs should be clear and measurable so that everyone is on board. Data science teams understand what’s at stake and operations personnel understand how to use insights to move forward or pivot.
- Who is monitoring? – ML uses non-intuitive mathematical functions. The black box requires constant monitoring to ensure you’re operating within regulation and that programs are returning quality information. You may have to retrain data periodically, and determining how and when to do so needs critical collaboration between the teams involved. With an operational system in place, there shouldn’t be any confusion.
- How are you ensuring compliance? – If GDPR doesn’t strike fear in your soul, maybe you weren’t hit that hard with its initial introduction. Deploying ML, however, might have you afoul of European regulations or a slew of other compliance systems designed to protect customers in the age of big data. MLOps should have a comprehensive plan for governance to ensure your programs are auditable and to assist with explainability.
Getting Started With MLOps
Deploying machine learning effectively means more than running numbers or leaving your data scientists on their own to figure out compliance and business insight. It’s crucial to take responsibility for production level ML so your operations team knows how to approach this new era of data and your data team is fully supported to do what they do best. Looking ahead to operations ensures you’re not only ahead of the machine learning curve, but your adoption is smooth and immediately insightful.