What is MLOps? A beginner’s guide to machine learning operations

The MLOps market is growing fast, with expectations to jump from USD 3.4 billion in 2024 to over USD 17.4 billion by 2030, and a growth rate of 31.1% per year. As more and more companies turn to AI and ML to stay competitive, MLOps will be key in ensuring these technologies work efficiently.

In this article, you will discover what is MLOps, which industries already benefit from it, the top tools and frameworks to use, how to apply MLOps to your projects, and much more.

What is MLOps?

MLOps meaning refers to machine learning operations, a set of tools that help handle and automate the entire lifecycle of machine learning models. It combines ML with DevOps (development operations), and just like DevOps changed software development, MLOps is doing the same for machine learning. It makes managing all the different steps of an ML project more organized and helps teams create, test, and launch models smoothly. MLOps is quickly becoming a must-have in the AI world because more and more companies are adopting ML tools to solve challenges.  

For example, let's imagine a virtual reality (VR) game studio that uses AI to create immersive environments. If players begin to report that certain scenes feel repetitive or less engaging, MLOps lets the team of developers track player interactions and feedback in real time. They can gather data on which areas players explore the most and where they lose interest. With this information, the team can quickly change the game design, update scenarios, or introduce new ones.

Which fields use MLOps?

Industries like video gaming, entertainment, healthcare, and finance are benefiting from MLOps. For example, let's look at video games, which frequently use algorithms to influence gameplay based on a player's performance in specific tasks (like completing a level without losing a life). Similarly, platforms like Hulu or Apple Music use algorithms to suggest films or tracks to users, tailoring recommendations based on their past choices. MLOps solutions they use effectively deliver tailored experiences that resonate with users.

The MLOps lifecycle: from development to deployment

Data collection

It all starts with data collection. It is where you gather the information needed to train your model. Data can come from different sources, like databases, APIs, or user interactions. The goal is to collect various and relevant data to enable your model to learn.

Data preparation

Data preparation involves:

• Cleaning up the data.

• Removing duplicates.

• Fixing missing values.

• Ensuring everything is in the proper format.

This step is crucial because the quality of your data directly impacts how well your model will perform.

Feature engineering

It is the step where you select or create the key elements to support your model in making better predictions. You can create new variables, scale features, or encode categorical data to enhance the model's learning process.

Model development

With clean data and meaningful features ready, it's time to develop the model. It involves choosing suitable algorithms, training the model, and fine-tuning the settings to optimize its performance. During this phase, data scientists often experiment with different approaches to find the best fit for the problem at hand.

Model evaluation

It usually involves splitting your data into training and testing sets to see how the model performs on unseen data. Assessing the model helps identify any areas for improvement and ensures it's ready for use.

Deployment

When you're satisfied with the model's performance, it's time for the final step—deployment. It means integrating the model into a production setting to enable predictions to be made in real time. MLOps tools can help automate this process and make it easier to update the model as new data comes in.

Popular MLOps tools and frameworks

MLOps platforms help teams quickly update models as new data comes in. Here are the most useful options for beginners:

MLflow

MLflow is an open-source platform that makes managing your machine learning experiments easier. It helps you keep track of all the models you build, the parameters you use, and the results you get from your tests. It's user-friendly, so you can easily track your work and compare different models. It is an excellent place to begin if you're starting and want a tool to help organize your experiments.

Cloud platforms: Azure ML and Google Vertex AI

AI Cloud platforms like Azure Machine Learning and Google Vertex AI are great solutions if you don't want to worry about setting up infrastructure. They offer end-to-end MLOps capabilities, from data processing to model deployment, and simplify getting started with MLOps, especially for beginners who want a reliable solution.

TensorFlow Extended (TFX)

TensorFlow Extended (TFX) is built for production-level machine learning pipelines, especially if you're already using TensorFlow. It gives you a complete suite of tools to manage the entire lifecycle of a model, from data processing to deployment. It's perfect when you're ready to scale up and move your models into applications.  

KubeFlow

KubeFlow is designed for teams that want to run their machine-learning models in the cloud. It’s ideal for scaling up projects, especially when working with high volumes of models or large datasets. While it can be a bit hard for beginners, it's a great choice if you're looking to grow your project in the cloud. KubeFlow makes automating tasks like training and deploying your models easier across different cloud environments.

DVC (Data Version Control)

DVC is like Git for machine learning. It helps you keep track of your datasets, models, and the code you use to train them. DVC ensures that every experiment you run is reproducible, so you never have to worry about losing your work or forgetting what changed in each experiment. DVC is a fantastic tool to ensure everything is versioned and organized if you're working with complex models or extensive datasets.

Common challenges when implementing MLOps

Understanding MLOps  

One of the biggest challenges when first diving into MLOps is understanding what it really involves. MLOps combines machine learning, DevOps, and data science, and their overlap makes it easy to get confused. It's not just about deploying models—there's a whole ecosystem of best practices that help keep everything running smoothly.  

Start by breaking it into smaller pieces. Familiarize yourself with the core concepts of MLOps and learn how it integrates machine learning models with your development and operational processes. Go at your own pace—take your time and learn as you go.

Managing data across projects

Data management can be hard if you don't have suitable systems. When working with machine learning models, keeping track of the datasets is crucial. Data must be tracked, which can be tough when working with large datasets.

Consider using tools like DVC to help you manage and version control your data just like you would with your code. It would make collaborating with others easier and ensure consistency across your projects.

Lack of automation

One of the main benefits of MLOps is automation, but many beginners skip this step because they feel they can manage things manually. The problem is that manually handling tasks like model retraining or data preprocessing takes time and is prone to errors.  

The best option is to automate your workflows wherever possible. The more you automate, the more time you can spend improving your models and processes.

Getting started with your first MLOps project

Clarify your project goal

Before going to the technical part, clearly define what problem you want to solve with AI. It could be anything from predicting sales numbers to classifying images.  

Prepare data

Machine learning models need data to learn. Gather and clean your data (remove duplicates, fix missing values, etc.) to ensure it's ready for training. If you're working on a small project, you can use publicly available datasets or your data if it's accessible.

Choose a machine-learning model

Select a simple model that suits your needs. For beginners, models like linear regression for prediction tasks or decision trees for classification are excellent starting points. Don't concentrate on choosing the "perfect" model—focus on learning how to manage it.

Set up your development environment

Install the tools you'll need for your project. Popular tools for MLOps include Python (with libraries like TensorFlow or Scikit-learn), Jupyter notebooks for experimentation, and Git for version cont.  

Train your model

Use the data you've prepared to train your machine learning model. It is where you'll experiment with your model's settings (hyperparameters) to improve performance. When your model is trained, test it on new data to check how well it performs.

Automate with pipelines

Create a basic MLOps pipeline to automate parts of the workflow. It could include automating the data preparation, model training, and testing stages. You can use tools like MLflow or Kubeflow to help set up your pipeline.

The future of MLOps and why it matters

AI adoption grows across industries—from healthcare and finance to retail and logistics, so the demand for seamless, reliable, and efficient machine learning operations will only increase.  

In the coming years, we can expect a stronger focus on automation, scalability, and collaboration within MLOps pipelines. Currently, many tasks involved in managing machine learning models are still done manually, which can be time-consuming and prone to error.  

In the future, MLOps solutions will automate more of these tasks, like monitoring model performance and updating models with new data. As more industries rely on machine learning, the role of the MLOps engineer will become more critical because someone has to make sure that everything runs smoothly.  

As we move forward, MLOps tools will take over more tasks, help reduce mistakes and save time.