MLOps—or machine learning (ML) and operations—is the equivalent of DevOps but with a significant difference. DevOps concerns itself with the working model of ML. It focuses on the actual software delivery cycle, working to close the gap between development and IT teams so they build, test, and release software faster and more reliably. MLOps aims to achieve the same results—in a data science and ML context.
As the team at Google Cloud says, “the real challenge isn't building an ML model. The challenge is building an integrated ML system and continuously operate it in production.”
For a successful ML model, several processes must be in place and continuously work well together, resembling the continuous, flawless, and high-quality delivery of an assembly line that produces expert results without fail.
Here are nine reasons why it is essential for developer productivity.
1. MLOps make the ML process faster.
Since the ML process involves countless steps—from design to development, testing, and delivery—engineers need a function that cuts through the manual sluggishness and expedites the cycle. Without MLOps, the process is time-consuming, especially if the model was upgraded through different ML frameworks. Communication between other teams would also require diverse sign-offs and tedious back-and-forths, dragging out an already slow process to months if not years.
2. MLOps automates the ML process.
A regular ML process would be highly manual, with code written from scratch with each use case. There would also be numerous bottlenecks, resulting in software getting stuck at any stage in the process and work stopped indefinitely. Software may never make it to the finish line. ML platforms that help you with MLOps can help you avoid bottlenecks by keeping all versions of the work documented, stored, and shared. Stakeholders set KPI benchmarks, and the project flows on to completion.
3. MLOps creates repeatable workflows.
MLOps allows custom-built steps to be reused, leveraged, and built on, not just by the author, but also by other data scientists from your team and organization. Just as DevOps shortens the production life cycles by improving products with each iteration, MLOps drives insights by shortening the life cycle between the ML training and development stage.
4. MLOps make the ML process error-resilient.
The ML manual process is drastically error-prone with issues like a training-serving skew. The lack of coordination between the operations and data science teams leads to unexpected online and offline performance differences. Data scientists who work on ML need to know that the result matches their trained model in a real-time setting. For that, they need to have a streamlined CI/CD (continuous integration/continuous delivery) process, where there is a constant loop-back device between dev and ops, so engineers can improve the model and rapidly deploy.
Such an error-prone process is fundamental in a workplace environment where you get new engineers all the time. A managed approach, achieved through MLOps, stops software from getting lost and keeps your team on the same page.
5. MLOps prevent fatigue.
A manual ML process turns your energetic, promising crew of data scientists into frustrated and underutilized engineers who feel they're spinning in an endless Sissyphusian circle. MLOps does away with data drift, giving operations and data scientists the creativity and motivation to continue their work. You’re more likely to get promising insights and actionable results.
6. MLOps reduce bias.
Sometimes, MLOps can guard against certain biases in their algorithms that, if undetected and corrected, can harm under-represented people in fields such as health care, criminal justice, and hiring. Overlooked biases in marketed software can also dent the company’s reputation and expose the company to legal scrutiny.
7. MLOps lead to actionable business value.
Close the training-to-operations loop faster, and you turn ML insights into actionable results. Each stage of the process seamlessly connects with and flows into the next, workers of different teams collaborate, bottlenecks disappear, leading to productive outcomes.
8. MLOps helps you with regulatory compliance.
The regular ML process is held accountable to a slew of government compliance and ethical obligations on data security, machine ethics, and data governance.
MLOps frees your data team to focus on what they do best: creating and designing software, while MLOps allows your operations team to concentrate on the ins and outs of management and regulations.
9. MLOps facilitates team communication.
Each team has its particular talents. Without MLOps, your operations teams would be unable to communicate with your data engineers, data scientists, software engineers, and vice versa, resulting in wasted human potential. There would be wasted software potential, too, with promising software designs and solutions held up in the deployment or some earlier stage, rendering them ineffective.
Why do we need MLOps? Here’s what the engineers at Google Cloud say: With the long history of production ML services at Google, we've learned that there can be many pitfalls in operating ML-based systems in production.
A platform for MLOps helps you shorten the system development life cycle and ensure that high-quality software is being developed continuously and delivered and maintained in production. Done well and consistently followed through, MLOps can be a game-changer for your company in that it eliminates waste, automates the ML cycle, and produces richer, more consistent insights.
Leah Zitter, Ph.D., has a master’s in philosophy, epistemology, and logic and a Ph.D. in research psychology.
There is so much more to discuss, so connect with us and share your Google Cloud story. You might get featured in the next installment! Get in touch with Content Manager Sabina Bhasin at firstname.lastname@example.org if you’re interested.
Rather chat with your peers? Join our C2C Connect chat rooms!