MLOps, short for Machine Learning Operations, is an emerging practice that brings together the disciplines of machine learning and DevOps to streamline the entire lifecycle of machine learning models, from development to deployment and beyond. One of the key aspects of MLOps is the use of automation to improve the efficiency, reliability, and quality of machine learning pipelines. In this tutorial, we will explore how to use Continuous Integration (CI), Continuous Delivery (CD), and Continuous Testing (CT) to automate the deployment of machine learning models. Before we dive into the details of MLOps automation, let's briefly explain the three key concepts that underpin it: MLOps automation typically involves a series of steps that automate the entire machine learning pipeline, from data preparation to model deployment. To automate this process, we can use a combination of CI/CD/CT tools and techniques.

MLOps, or DevOps for machine learning, is a practice that aims to bring the collaboration and automation practices of DevOps to the development and deployment of machine learning models. It aims to improve the speed and reliability of model development and deployment, as well as to make the process more reproducible and maintainable. By following these steps and using tools and practices from the DevOps philosophy, organizations can improve the speed and reliability of their machine learning model development and deployment processes, and better manage the complexity and scale of their machine learning systems. By testing and validating the model continuously throughout the development process, data scientists can ensure that the model is accurate, reliable, and effective when it is deployed to production.

MLOps is defined as certain practices that ensure the deployment and longevity of ML systems by performing the necessary maintenance for updated versions. Due to its potential benefits MLOps market has grown rapidly: According to Deloitte, the market will be worth $4 billion in 2025, predicting a nearly 12-fold increase in MLOps market size since 2019. Despite all the benefits ML brings to various business processes, companies are struggling to deploy ML techniques to enhance their efficiency. Therefore, we list some of the best practices for implementing MLOps to your business problems. A clear business objective is critical to the deployment of successful MLOps.

It is now well recognized, across multiple industries, that predictive modeling and machine learning may provide tremendous value for organizations that leverage these techniques as an integral part of their business model. Many organizations spanning both the public and private sectors have adopted a data-driven business strategy where insights derived from either comprehensive data analyses or the application of highly complex machine learning algorithms are used to influence key business or operational decisions. Although there are many organizations leveraging machine learning at scale, and the variety of use cases abound at a high level, the overall machine learning life cycle has a common structure among all organizations irrespective of the specific use case or application. Specifically, for any organization leveraging data science at scale, the machine learning life cycle is defined by four key components: Model Development, Model Deployment, Model Monitoring, and Model Governance (see Figure 1). Most data scientists are well versed in the model development part of the machine learning life cycle and have a high degree of familiarity with complex data queries (e.g., SQL), data wrangling, feature engineering, and algorithm training.

Challenges arise as the production of machine learning models scale up to an enterprise level. MLOps plays a role in mitigating some of the challenges like handling scalability, automation, reducing dependencies, and streamlining decision making. Simply put, MLOps is like the cousin of DevOps. It's a set of practices that unify the process of ML development and operation. This article serves as a general guide for someone looking to develop their next machine learning pipeline, delivering summaries of topics that will introduce topics of MLOps.