If you have ever worked with a Machine Learning (ML) model in a production environment, you might have heard of MLOps. The term explains the concept of optimizing the ML lifecycle by bridging the gap between design, model development, and operation processes.

As more teams attempt to create AI solutions for actual use cases, MLOps is now more than just a theoretical idea; it is a hotly debated area of machine learning that is becoming increasingly important. If done correctly, it speeds up the development and deployment of ML solutions for teams all over the world.

MLOps is frequently referred to as DevOps for Machine Learning while reading about the word. Because of this, going back to its roots and drawing comparisons between it and DevOps is the best way to comprehend the MLOps concept.

MLOps vs DevOps

DevOps is an iterative approach to shipping software applications into production. MLOps borrows the same principles to take machine learning models to production. Either Devops or MLOps, the eventual objective is higher quality and control of software applications/ML models.

What is MLOps?

Machine Learning Operations is referred to as MLOps. Therefore, the function of MLOps is to act as a communication link between the operations team overseeing the project and the data scientists who deal with machine learning data.

For the development and improvement of machine learning and AI solutions, MLOps is a helpful methodology. By utilizing continuous integration and deployment (CI/CD) procedures with appropriate monitoring, validation, and governance of ML models, data scientists and machine learning engineers can work together and accelerate the speed of model creation and production by using an MLOps approach.

The key MLOps principles are:

• Versioning – keeping track of the versions of data, ML model, code around it, etc.
• Testing – testing and validating an ML model to check whether it is working in the development environment.
• Automation – trying to automate as many ML lifecycle processes as possible.
• Reproducibility – we want to get identical results given the same input.
• Deployment – deploying the model into production.
• Monitoring – checking the model’s performance on real-world data.

What are the benefits of MLOps?

The primary benefits of MLOps are efficiency, scalability, and risk reduction. 

• Efficiency: MLOps allows data teams to achieve faster model development, deliver higher quality ML models, and faster deployment and production. 
• Scalability: Thousands of models may be supervised, controlled, managed, and monitored for continuous integration, continuous delivery, and continuous deployment, thanks to MLOps’ extensive scalability and management capabilities. MLOps, in particular, makes ML pipelines reproducible, enables closer coordination between data teams, lessens friction between DevOps and IT, and speeds up release velocity.
• Risk reduction: Machine learning models often need regulatory scrutiny and drift-check, and MLOps enables greater transparency and faster response to such requests and ensures greater compliance with an organization’s or industry’s policies.

Data pipeline for ML operations

One significant difference between DevOps and MLOps is that ML services require data–and lots of it. In order to be suitable for ML model training, most data has to be cleaned, verified, and tagged. Much of this can be done in a stepwise fashion, as a data pipeline, where unclean data enters the pipeline, and then the training, validating, and testing data exits the pipeline.

The data pipeline of a project involves several key steps:

• Data collection: 

Whether you source your data in-house, open-source, or from a third-party data provider, it’s important to set up a process where you can continuously collect data, as needed. You’ll not only need a lot of data at the start of the ML development lifecycle but also for retraining purposes at the end. Having a consistent, reliable source for new data is paramount to success.

• Data cleansing: 

This involves removing any unwanted or irrelevant data or cleaning up messy data. In some cases, it may be as simple as converting data into the format you need, such as a CSV file. Some steps of this may be automatable.

• Data annotation: 

Labeling your data is one of the most time-consuming, difficult, but crucial, phases of the ML lifecycle. Companies that try to take this step internally frequently struggle with resources and take too long. Other approaches give a wider range of annotators the chance to participate, such as hiring freelancers or crowdsourcing. Many businesses decide to collaborate with external data providers, who can give access to vast annotator communities, platforms, and tools for any annotating need. Depending on your use case and your need for quality, some steps in the annotation process may potentially be automated.

After the data has been cleaned, validated, and tagged, you can begin training the ML model to categorize, predict, or infer whatever it is that you want the model to do. Training, validation, and hold-out testing datasets are created out of the tagged data. The model architecture and hyperparameters are optimized many times using the training and validation data. Once that is finished, you test the algorithm on the hold-out test data one last time to check if it performs enough on the fresh data you need to release.

Setting up a continuous data pipeline is an important step in MLOps implementation. It’s helpful to think of it as a loop, because you’ll often realize you need additional data later in the build process, and you don’t want to have to start from scratch to find it and prepare it.

Conclusion

MLOps help ensure that deployed models are well maintained, performing as expected, and not having any adverse effects on the business. This role is crucial in protecting the business from risks due to models that drift over time, or that are deployed but unmaintained or unmonitored.

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