MLOps 101: Machine Learning Operations Explained

Machine learning (ML) is fundamentally changing how businesses operate and make decisions. With growing troves of data from more and more connected devices and channels, comes increasing opportunities to uncover deeper insights more efficiently. But as ML is integrated into your business processes, you’ll need streamlined workflows to ensure the speed, accuracy and scalability of these initiatives.

This is where machine learning operations (MLOps) comes into play –providing methods for you to manage and optimize your ML efforts.

What is MLOps?

MLOps, the integration of machine learning and DevOps, is rapidly becoming a cornerstone for businesses seeking to scale their machine learning initiatives.

This practice blends machine learning development with operational processes and best practices – unifying data scientists, DevOps engineers and IT teams and helping them automate and streamline their workflows.

By adopting MLOps, organizations can move machine learning models from development to production efficiently while ensuring they’re continuously maintained and monitored. From simplifying deployments to delivering reliable and scalable solutions, MLOps can help solve complex challenges and deliver real value to customers.

Behind the Rising Demand for MLOps

Developing and deploying machine learning models has traditionally been a slow and complex process. Data scientists often worked alone, building models on their local machines and passing them to IT for deployment. This handover led to delays, inconsistencies and additional expense.

MLOps mitigates these issues by combining AI with DevOps practices to automate the entire machine learning lifecycle, including data preparation, model training, deployment and monitoring.

A key benefit of MLOps is improved collaboration between data scientists and IT teams. With AI tools, they can work together directly, reducing communication gaps and manual handoffs, saving time and ensuring more reliable models.

MLOps also helps organizations scale their machine learning efforts. It allows them to quickly build and manage multiple models simultaneously while minimizing errors and maintaining consistency. AI-powered monitoring tools also provide transparency by tracking model performance in real-time, enabling the quick detection of any issues – while also facilitating easy model retraining with new data.

The Connection Between MLOps and DevOps

MLOps involves collaboration and efficient communication among data engineers, data scientists, operations teams and ML engineers. Together, they manage the ML application lifecycle and iteratively improve products.

Built on solid foundation of DevOps principles, MLOps includes model and data versioning, continuous training, monitoring for issues like data drift and platform automation.

MLOps is also about putting algorithms into practice; helping move machine learning models from research to production. This juncture is critical, as AI and ML projects often get stuck in the research phase.

Implementing MLOps

A typical ML project begins with data acquisition, cleaning and pre-processing followed by feature extraction. Given the iterative nature of the process, it’s essential to have reusable pipelines in place, complete with checkpoints.

The next step involves building the model through experimentation, where tracking parameters, metrics and hyperparameters are essential for optimization. During this phase, it’s crucial to write code and implement version control simultaneously.

After building the model, it’s necessary to deploy it somewhere, typically in a development or staging environment, where it can be validated.

If validation is successful, the model will be deployed to production. At this stage, the model must be closely monitored for performance, data drift, model drift and other issues.

How Do You Begin an MLOps Project?

To successfully implement machine learning solutions like testing ideas, training models and creating workflows, everyone – from data scientists to ML engineers to operations teams – needs to work seamlessly together. Cloud platforms like Amazon SageMaker, Azure ML Studio and Google AI Platform can make it easier for teams to collaborate, build models together and streamline their processes.

Once your platform is in place, you can use DevOps principles to manage version control, continuous testing and performance monitoring. This means regularly checking for issues – like data changes or model performance problems – and providing feedback for improvements.

MLOps is not a strict set of rules; rather, it’s a flexible approach that can be applied to a project’s specific goals and needs. For example, if a project requires the testing of new ideas or the automation of checks before going live, focus on those areas.

The following points illustrate how to get started on a typical machine learning use case using the MLOps approach.

• First, manually handle data acquisition, cleaning and ETL tasks. Then develop small pipelines and containerize them.

• Next, focus on feature engineering. Package your code, write pipelines and containerize the process.

• After feature engineering, proceed to experimentation. Once you’ve selected and streamlined the algorithm, write code to dynamically handle parameters, package it and containerize the process.

• After establishing small pipelines, integrate them. Create workflows to trigger each pipeline sequentially.

• Implement code and model versioning as part of the process.

Remember, MLOps is all about systematic management of the ML application. Start small with manual efforts and gradually automate each step as you become more familiar with your MLOps processes. Eventually, you’ll have an end-to-end solution in place.

Who Can Practice MLOps?

DevOps engineers, ML engineers, software engineers and data scientists all have active roles to play in MLOps projects.

Generally, team members with diverse skillsets should collaborate on the MLOps approach to take projects from initiation to conclusion (as depicted in the graphic below). But an experienced full-stack data scientist may be able to manage MLOps projects with fewer dependencies on additional teams and contributors.

Implement MLOps with Material

Material is here to help you navigate the MLOps approach to developing machine learning products and processes. We specialize in building AI platforms that utilize technologies like computer vision, natural language processing and core ML algorithms to deliver tailored predictive and prescriptive analytical solutions.

Our approach ensures a smooth transition from research to production through MLOps, including automated model retraining. With our end-to-end automation and continuous monitoring, we can help you innovate and achieve tangible business goals with your machine learning initiatives.

To unlock the full potential of MLOps, connect with us today.