Machine Learning Operations (MLOps)

Reviewed by Kuntal ChakrabortyCheckmark | Last updated: June 1, 2021

What Does Machine Learning Operations (MLOps) Mean?

Machine learning operations (MLOps) is an approach to managing the entire lifecycle of a machine learning model -- including its training, tuning, everyday use in a production environment, and retirement.

MLOps, which is sometimes referred to as DevOps for ML, seeks to improve communication and collaboration between the data scientists who develop machine learning models and the operations teams who oversee an ML model's use in production. It achieves this by automating as many repetitive tasks as possible and improving the feedback loops used by data scientists, ML engineers, developers and operations teams.

An important goal of MLOps is to help stakeholders balance priorities between using using artificial intelligence (AI) tools to solve business problems and ensuring an ML model's output meets best practices for Explainable AI.


Techopedia Explains Machine Learning Operations (MLOps)

An MLOps rollout requires five important components to be successful:

1. ML Pipelines: ML Pipelines where data gets extracted, transformed, and loaded are essential for successful machine learning operations because data often needs to be transformed into different formats.

2. Monitoring: Machine learning uses iteravie mathematical functions instead of programmed instructions, so continuous monitoring is required to ensure outcomes remain within pre-defined limits.

3. Co-Team Operations: Interconnected teams are needed to bridge the gaps in knowledge and skill between data scientists and DevOps teams.

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.

MLOps was developed with the knowledge that not all data scientists and ML engineers have experience with programming languages and IT operations. The continuous feedback loops that MLOps provides allows employees outside data science to focus solely on what they know best instead of having to stop and learn new skills.

Today, ML plays an essential role in developing artificial intelligent-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.

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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