Model development and experimentation is part of any machine learning lifecycle. However, without careful planning, keeping track of experiments can become tedious and challenging; especially given the number of configurations we typically deal with. MLflow is a machine learning lifecycle framework that allows ML engineers and teams to keep track of their experiments. In PART 1 of the series, we are going to focus on the first two steps -- tracking experiments and sharing code. PART 2 will be dedicated to model packaging, while PART 3 will show how the concepts outlined in the previous parts can be used in a React web application. For now, let's try to understand what MLflow is, and what it can do for us!

Of course, that is a gross over-simplification. As more models are being deployed in production, the importance of MLOps has naturally grown. There is an increasing focus on the seamless design and functioning of ML models within the overall product. Model Development can't be done in a silo given the consequences it may have on the product and business. We need an ML lifecycle that is attuned to the realities of ML-assisted products and MLOps.

Everyone has been talking about MLOps for over a year now. I looked around for how the lifecycle and processes have evolved. The discipline of seeking insight from data has been around for 25 years. Back then, it was known as data mining. In this article, I present a survey of the ML lifecycle process and conclude with my take on it.

There are no standard practices for building and managing machine learning (ML) applications. As a result, machine learning projects are not well organized, lack reproducibility, and are prone to complete failure in the long run. We need a model that helps us maintain quality, sustainability, robustness, and cost management throughout the ML life cycle. The Cross-Industry Standard Process for the development of Machine Learning applications with Quality assurance methodology (CRISP-ML(Q)) is an upgraded version of CRISP-DM to ensure quality ML products. These phases require constant iteration and exploration for building better solutions.

A technique for creating policies, norms, and best practices for machine learning models is known as "machine learning operations" or "MLOps." MLOps aims to guarantee the whole lifecycle of ML development -- from conception to deployment -- is meticulously documented and managed for the best results instead of investing a lot of time and resources in it without a strategy. MLOps aims to codify best practices to improve the quality and security of ML models while making machine learning development more scalable for ML operators and developers. MLOps provides developers, data scientists, and operations teams with a framework for cooperating and, as a result, producing the most potent ML models. Some refer to MLOps as "DevOps for machine learning" since it successfully applies DevOps methods to a more specialized field of technological development.

This is PART 2 of our 3 PART series on the machine learning lifecycle platform MLflow. In PART 1, we've had a look at: In this guide, we are going to have a look at MLflow Models. With Models, we can package machine learning/deep learning models for deployment in a wide array of environments. Note that this guide assumes that you've read PART 1. So make sure to check out the first article in the series before reading on!

In this article, we'll get introduced to MLOps. We'll learn what MLOps is, the Data Science Lifecycle, the Machine Learning Lifecycle, multiple challenges we face with Machine Learning and then get to understand the importance of MLOps. Finally, we'll make a brief comparison of MLOps to DevOps and learn about various principles of MLOps along with specific benefits and business values of MLOps for businesses and organizations. Machine Learning Operation shortly known as MLOps focuses on empowering data scientists and application developers to help bring ML models to production. The MLOps makes it faster for experimentation and in the development of machine learning models. Moreover, faster deployment of models into production can be made.

MLOps is the new terminology defining the operational work needed to push machine learning projects from research mode to production. While Software Engineering involves DevOps for operationalizing Software Applications, MLOps encompass the processes and tools to manage end-to-end Machine Learning lifecycle. Machine Learning defines the models' hypothesis learning relationships among independent(input) variables and predicting target(output) variables. Machine Learning projects involve different roles and responsibilities starting from the Data Engineering team collecting, processing, and transforming data, Data Scientists experimenting with algorithms and datasets, and the MLOps team focusing on moving the trained models to production. Machine Learning Lifecycle represents the complete end-to-end lifecycle of machine learning projects from research mode to production.

Building a machine learning model is an iterative process. For a successful deployment, most of the steps are repeated several times to achieve optimal results. The model must be maintained after deployment and adapted to changing environment. Let's look at the details of the lifecycle of a machine learning model. The machine learning lifecycle is the process of developing, deploying, and managing a machine learning model for a specific application.

A successful machine learning (ML) project is about a lot more than just model development and deployment. Machine learning is about the full lifecycle of data. It consists of a complex set of steps and a variety of skills, required to achieve actionable outcomes and deliver business value. The level of complexity involved in the ML lifecycle is part of the reason why good practices and fully integrated tools are in their infancy, even in the present day. Other reasons include a lack of skills, poor scalability of models, and a lack of automation as data scientists often come from several different backgrounds and do not always follow best coding and DevOps practices. Furthermore, data scientists and engineers usually work in silos which results in poor collaboration across the teams.