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Machine Learning Operations (MLOps)

Machine learning operations (MLOPs) is a set of practices that combines developing and maintaining machine learning (ML) seamlessly. The goal is to establish reliable communication and collaboration between data scientists and machine learning operations professionals in order to properly manage and shorten an artificial intelligence (AI) product’s lifecycle. The three main components of MLOps are machine learning, DevOps (IT), and data engineering.

It achieves that by implementing automation as often as possible, settling on the balance between improving product quality and meeting business and market requirements.

MLOps works on the same principles that run DevOps. In addition to the software developers (Devs) and IT operations teams, MLOps includes data scientists and ML engineers. The result is a continuous production loop that starts with collecting data and modeling in the MLOps segment.

The workflow then proceeds to the devs, where they handle the product’s verification and packaging before sending it to the IT operations team to release, configure, and monitor the result. The loop continues as the feedback is used to plan and create a new update to the machine, going back to data experts.

MLOps is able to produce noticeable results because it bridges the gap between data scientists and ML engineers, and devs and IT teams. MLOps was developed with the knowledge that not all data scientists and ML engineers are experienced in programming languages and IT operations. But instead of older models, where every section in ML development is independent, MLOps creates a continuous feedback loop between the three departments, enabling a faster development cycle and higher product quality, all whilst allowing professionals to focus solely on what they know best instead of having to learn skills on the opposite end of the spectrum.

ML plays an essential role in developing AI-reliant applications. As the proper use of ML helps AI applications grow and evolve semi-automatically, MLOps became an essential part of automating the entire process from start to finish, allowing companies to make the most of their resources. Realistically, without the ability to automate the growth and the deployment process, AI cannot be used.

MLOps has five standard practices that are essential to a successful implementation:

1. ML Pipelines: Pipelines are the structure where data gets extracted, transformed, and loaded. They are essential in MLOps because data needs to be constantly transformed into different shapes and formats.

2. Monitoring: Because machine learning operates using mathematical functions and not a clear set of instructions, continuous monitoring ensures everything is going as planned.

3. Co-Team Operations: To bridge the gaps in knowledge and skill between data scientists and DevOps teams, interconnected teams are needed. Working in the same team or in co-teams allows data scientists and developers to communicate better and achieve their common goals.

4. Versioning: In ML, in addition to versioning code, other elements also need to be tracked and altered, such as training data, meta-information, and model versions.

5. Validation: Tests need to be performed both on the end-product and on its separate elements during development. In MLOps, most tests need to be statistical in nature instead of goal-oriented, as most individual elements of an MLOps lifecycle cannot produce complete results.

Still, only around 15% of businesses reported using MLOps and AI in their regular operations in 2020. MLOps has a high failure rate when it is not implemented properly, with the most common reason is poor staff communication and lack of compatibility between departments. . Other factors include difficulty to scale, complex monitoring and management procedures, automation and diagnostics issues, and low reproducibility of models and results. To combat some of these problems, new businesses are developing to help ensure the successful implementation of MLOps.

But a properly-designed and implemented MLOps structure can take over the production cycle as a monitoring and automation system from the early stages of app development to compliance and updates. Depending on how it gets implemented, MLOps can be of use to data scientists, software developers, compliance teams, data engineers, ML researchers, and business leaders.

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