MLOps in a Nutshell
Data is becoming more complex, and so are the approaches designed to process it. Companies have access to more data than ever, but many still struggle to glean the full potential of insights from what they have. Machine learning has stepped in to fill the gap. However, the lifecycle falls apart at the deployment stage more often than not, thanks to heavy reliance on manual processes. MLOps is a methodology designed to solve the challenge of deployment. Here’s how it came about and what you need to know to get started.
What is MLOps?
Before understanding MLOps, let’s back up. The term comes from combining “machine learning” and “DevOps.” And DevOps revolutionized the way developers built, deployed, and iterated software by prioritizing automation and continuous improvements. While different companies express the principles slightly differently, it builds on four basic principles:
- Collaboration and communication
- Continuous improvement and waste minimization
- Build with the end (i.e., user need) in mind
MLOps takes those ideas and builds them into the machine learning lifecycle. Instead of relying on manual processes to drive the lifecycle, MLOps focuses on reducing the number of steps between building and deploying.
The core concepts of MLOps
If you didn’t come into the field first through coding and development, some of the core concepts of MLOps may be foreign. These are critical concepts taken from the DevOps world.
Continuous integration: Merging code changes into a central repository. In DevOps, this triggers automatic validation tests. In MLOps, this concept expands to data and model validation.
- Continuous delivery: Code changes are automatically built, tested, and prepared for production during short iterations. It ensures code can be released automatically at any time.
- Continuous training: Automatically retraining models in production so that the end result is ready to deploy without manual interventions.
- Continuous testing: Evaluating the product at every stage of the lifecycle facilitates continuous delivery.
- Continuous monitoring: Another automatic part of the development and deployment process, monitoring at all stages ensures models perform in the wild and revert to previous iterations in case of failure.
- Reusable infrastructure: Standardizing infrastructure prevents starting over from square one each time a new model is necessary.
- Reproducible environments: Ensuring versioning, fault tolerance, and governance without sacrificing fast, efficient development.
Why use MLOps
When we talk about the machine learning lifecycle, the usual suspects appear—data cleaning, modeling, gathering—but the deployment state often remains elusive. Very few machine learning projects actually make it into production thanks to a combination of:
- Lack of data engineering skills or support if the data science team doesn’t have explicit training in engineering.
- Teams working in silos. Even if there’s a recommended ratio of data scientists to developers to data engineers, the teams sometimes work in silos from each other and business teams.
- Tool and application sprawl.
Companies often struggle to balance conducting data science and machine learning projects to the best of their ability and to remember the business focus. Companies need to close the loop between extracting insights and turning those insights into actionable steps that turn into business value.
MLOps helps bridge that gap by leveraging skills from the IT side and the business side. By fostering a deep sense of collaboration, projects keep business value at the forefront. It also helps keep regulatory considerations at the forefront while ensuring that IT can concentrate on what it does best.
Plus, MLOps ensures a consistent feedback loop once the solution reaches production. Improvements help create better iterations over time with fewer bottlenecks in between. This critical step allows MLOps to put machine learning into production and then scale.
Introducing MLOps to your organization
Beginning MLOps streamlines the machine learning process, but you should ask a few questions before starting.
- What benchmarks will best serve you as you establish MLOps? A key component of MLOps is the concept of continuous improvement. With strategic KPIs, your data science team will understand the direction and goal of each project, and operations will see where (and when) projects need to pivot.
- Who will monitor each component? The collaboration component implies that continuous monitoring happens. MLOps requires a clear chain of responsibility as models are built, deployed, and retrained. In addition, monitoring business value ensures MLOps retains its primary goal: to deliver value for customers.
- What safeguards are in place to ensure compliance? MLOps automates much of the traditional machine learning process, but this does introduce some risk. MLOps should be explainable and open to audits, as well as well-documented. Reproducible environments preserve the state in sensitive cases and ensure version control.
The end goals of MLOps
The purpose of MLOps is streamlining and reducing waste, but what does that look like in practice? Some concrete examples of what MLOps can do for an organization include:
- Reducing both the time and the complexity of putting machine learning models into production. With such a small percentage of machine learning models making it to production, much less scaling, this is a critical goal.
- Enhancing collaboration: Silos are the death of insights. MLOps can foster deeper cooperation between IT and business users.
- Automating previous laborious manual processes in machine learning development and deployment.
- Standardizing the process to ensure compliance with regulations, governance policies, and best practices without increasing time to deployment.
- Increasing the rate of innovation. Focusing on continuous iterations allows teams to get to delivery without taking months.
- Managing the entire machine learning lifestyle through automation.
Building MLOps into your company’s operations
DevOps changed the software development world, and MLOps is doing the same for machine learning. As more companies turn to ML for business initiatives, MLOps could become the go-to methodology for extracting value and keeping things on track.
Author: Elizabeth Wallace
Source: Open Data Science