Ambassador post originally published on the InfraCloud blog by Atulpriya Sharma, CNCF Ambassador, Sr. Developer Advocate, InfraCloud Technologies

Machine Learning (ML) has revolutionized various industries by enabling data-driven decision-making along with the automation of certain tasks. For instance, many banking institutions deploy advanced machine-learning models to detect fraudulent transactions. These models need to evolve constantly otherwise there will be a steep rise in false positives.

However, deploying new machine learning models in production can be challenging. Training the model on production data, deploying it, and maintaining it isn’t easy. Many a time, machine learning models in production fail to adapt to the changing data and environment. And doing all of it manually isn’t efficient.

MLOps or Machine Learning Operations helps solve such challenges by automating the complete lifecycle of machine learning development and deployment. In this post, we will talk about MLOps, how it works, its benefits and challenges, and various tools that you can use.

But before we dig into what MLOps is, let us take a step back and look at the challenges with the existing model or working with machine learning models.

Machine Learning Development: Existing Challenges

Though we have been advancing in the Artificial Intelligence (AI) and machine learning space, the evolution is slow-paced when it comes to processes associated with it. Many organizations today deploy machine learning models for various use cases, however, most of these processes are manual that bring in multiple challenges.

These are a few challenges that hamper the development and deployment of machine learning models. To overcome these challenges, MLOps was introduced which automates and streamlines various processes.

What is Machine Learning?

Machine learning as we all know is a branch of Artificial Intelligence that enables computers to learn & make predictions without actually programming them.

Machine learning is used across different industries. From providing you with a recommendation of a movie to watch (YouTube/Netflix) to understanding your voice and turning on a fan (Alexa), machine learning is used widely in our daily lives. Even industry sectors like banking use it to detect fraudulent activity on credit cards and deploy chatbots to make customer service faster. These are just a handful of applications of machine learning in real life.

Types of Machine Learning

Just like we humans learn in different ways, there are means by which algorithms learn as well. Below are the three main forms of machine learning. There are three types of machine learning:

Machine Learning Models

At the heart of machine learning are machine learning models. Algorithms that are trained on datasets and optimized to make accurate predictions on new data. The output of this process is a machine-learning model.

Based on the way these models predict, there are different types of machine learning models:

Linear Regression. Courtesy: Spiceworks.
Classification model. Courtesy: Spiceworks.
Clustering models. Courtesy: GeeksForGeeks

Neural network model. Courtesy: Otexts.

What is MLOps?

MLOps or Machine Learning Operations provides a framework for creating a consistent and reproducible machine learning pipeline. It helps streamline the process of developing, deploying, and managing machine learning models. MLOps is the practice of applying DevOps principles and practices to machine learning workflows.

MLOps. Courtesy: mlops

With the help of MLOps, data scientists, engineers, and operations teams can collaborate more effectively – leading to better workflow management, increased productivity, and faster time to market. It’s the way forward and in fact, a report by IDC says that by 2024, 60% of enterprises will have their MLOps processes operationalized using MLOps capabilities.

MLOps Workflow

MLOps pipelines refer to the end-to-end processes and tools used to manage, deploy, monitor, and maintain machine learning models in production environments. A robust MLOps pipeline ensures that machine learning models can be updated, scaled, and maintained efficiently, while also meeting business requirements for accuracy, reliability, and speed.

MLOps pipelines typically include steps such as data ingestion, data preprocessing, model training, model testing, model deployment, and model monitoring.

MLOps Pipeline. Courtesy: iqt.

#1 Data preparation and management

The first phase in any machine learning process is the collection of data. Without clean and accurate data, the models are useless. Hence, data preparation & management is a crucial phase in MLOps workflow.

It involves collecting, cleaning, transforming, and managing data so that we have the correct data in place to train the ML models. The end goal is to have data that is complete and accurate.

Let us deep dive into the finer details of the sub-processes involved in the data preparation and management phase:

#2 Model training and validation

Once you have the data ready, you can feed it to your machine-learning models to train them. This is where you ensure that your models are trained well, and validated to ensure they perform accurately in production.

Steps involved in model training and validation include

#3 Model deployment

After the model is trained and the performance is validated, it’s time to deploy it to production. The model deployment phase involves taking the model that has been trained, tested, and validated in earlier phases and making it available for use by other applications or systems.

The process of deployment involves several steps as follows:

#4 Continuous model monitoring & retraining

Machine learning models aren’t something like deploy and forget. One needs to constantly monitor the model’s performance and accuracy. Since the real world can change which might affect the efficiency and accuracy of the model.

Various sub-processes involved in model monitoring are

There are tools available for each of the processes mentioned above. You have the option to choose from proprietary tools to open source ones. To make your lives easier, here’s an Awesome MLOps repository that has a list of open source MLOps tools categorized for each step.

We saw the various processes involved in a typical MLOps workflow. Each of these stages helps streamline the process of model deployment. Adopting MLOps can be challenging, but the benefits are substantial in unlocking the potential of machine learning for driving innovation and business outcomes.

Benefits of MLOps

By now, we know that MLOps helps streamline the machine learning development process. However, it also comes with a suite of benefits.

Some advantages of implementing MLOps are:


In the age when generative AI is booming, the future of machine learning is bright. It has immense potential to deliver enormous business value. However, it cannot happen just by building newer machine learning models. You need a comprehensive end-to-end process that takes care of the entire machine learning lifecycle from data preparation to model deployment and monitoring. By adopting MLOps, organizations can deliver value faster, optimize costs as well as improve efficiency.

By now you would have some understanding of what MLOps is and its benefits. Keep an eye on our upcoming blog posts where we’ll talk about MLOps on Kubernetes and associated tools.

That’s about it for this blog post, feel free to reach out to Atul for any suggestions or queries regarding this blog post.