MLOps or Machine learning operations describe the best practices for businesses to run machine learning projects in production successfully. All it needs is help from an expanding miscellany of software products and cloud services.
MLOps play a very crucial role in the development and operation of machine learning products and ecosystems. Machine learning operations is the practice of efficiently developing, testing, deploying, and maintaining machine learning (ML) applications in production environments. MLOps is used to automate and monitor the machine learning lifecycle of a product or a project that enables seamless collaboration across teams. It is highly efficient in getting great results in a lower period of time.
The potential of MLOps is still not fully utilized. One of the key challenges in data science is that the team often works like a granary, cut off from the DevOps teams, and use manual development processes, which is used to manually convert project into production-ready ML pipelines. This requires separate teams of ML engineers, data engineers, DevOps engineers, and developers to invest more time and resources than required.
Data science logic is refactored and converted into production-based frameworks. The data science and engineering teams need to package the code, address scalability, tune for performance and automate. These tasks are typically manual and can take a huge amount of time. If there is a change, the whole cycle is repeated again. This causes wastage of resources, time, and management efforts.
MLOps are required for continuous delivery and automation pipelines in an organization specializing in ML-based products. Understanding the core concepts of MLOps is very useful to
MLOps is a growing area that is still in its early stages but will definitely gain momentum in the future. The best practices and DevOps practices should be employed for the time being. The main goal of MLOps is to use ML models more efficiently to solve business problems in a fast way.
MLOps also poses many new challenges with its implementation like project management as data scientists must be skilled to work on an integrated role. Other challenges include communication, collaboration difficulties, logging, and monitoring.
Uses and Benefits of Machine Learning Operations
- Model quality is improved
- Models must be rewritten in different languages for deployment.
- Reduction of model backlog waiting to be deployed.
- Reduction of time taken for troubleshooting problems in ML model deployment.
- A standardized process for elevating models from development to production.
- High productivity as time wastage of data engineers and data scientists is reduced.
- Very reliable and auditable due to better versioning and quick deployment
NVIDIA is offering new courses on MLOps whose details can be seen here . It is a great course on learning Machine Learning models in the production environment.
Future of MLOps
It provides a system for monitoring all the models in production, irrespective of their deployed state. It can help in optimizing the output of ML projects by reducing the project release cycle, better versioning of code, bias reduction etc. With the incorporation of Machine Leaning Operations, developers can achieve many goals in the development of data science projects.