What is MLOps?

MLOps is a mix between machine learning and DevOps. It works by streamlining the process of taking machine learning models to production by maintaining and monitoring them after deployment. It does so by borrowing the same principles as DevOps to take a machine learning (ML) product into the DevOps lifecycle. In both, the outcome is of higher quality, and it provides faster patches and releases due to its CI/CD pipeline. This results in higher customer or user satisfaction and benefits the production process.

In this modern computing world, almost everything has started to use ML or Artificial Intelligence (AI) models. This means that more and more ML/AI models are being generated and implemented, creating a need for monitoring and quality control. Here is where MLOps shines. It provides a pipeline process for creating and quality-checking ML/AI models. It does so by running it through rigorous tests that allow us to pinpoint errors and weaknesses in our models. This monitoring, validation, and governance methodology for our processes provides a streamlined and error-proof system for making our models. In turn, increasing the pace of our ML model production.

Now that we know what MLOps entails, we will discuss why we need it.

1. Efficiency: Through the DevOps lifecycle, the product, i.e., the ML model, has a faster development cycle. This is because the testing and monitoring allow for fast identification of issues or potential issues. This, in turn, allows developers to understand better and fix the issues at hand. This efficient process increases overall development while also creating higher-quality models.

2. Scalability: As the number of models rises, they must be monitored to see if they produce the expected results. MLOps helps in this regard as well. Its monitoring system allows us to oversee, control, manage, and monitor thousands of models simultaneously. This ability greatly simplifies our problems and, in turn, allows us to be alerted before an issue occurs.

3. Risk reduction: These thousands of monitored and managed systems are built with some requirements in mind. MLOps can also help us by putting these models through scrutiny and other checks to enable greater transparency and, in turn, create a policy-compliant model that passes all test cases.

Let us now see MLOps's steps in producing its managed models.

We will now further look into what each process indicates.

1. Exploratory data analysis (EDA): Explore and prepare data for machine learning, making it shareable and reproducible.

2. Data preparation and feature engineering: Create refined features from data, making them visible and shareable with data teams.

3. Model training and tuning: Train and improve models using popular open-source libraries or automated machine-learning tools.

4. Model review and governance: Manage model versions, artifacts, and transitions using an open-source MLOps platform like MLflow.

5. Model inference and serving: Deploy models and automate the pre-production pipeline using CI/CD tools.

6. Model deployment and monitoring: Set up model inference and serving, managing model refresh frequency and production-specifics.

7. Automated model retraining: Monitor models in production, creating alerts and automation for corrective actions in case of model drift.

MLOps is an upcoming and evolving process that is more experimental in nature. It allows multiple teams within an enterprise to work together, reducing testing time and providing automated deployment. It allows us to build an end-to-end or custom-built MLOps solution for our models which enhances the reproducibility of machine learning experiments.