Choosing the Right Machine Learning Operations (MLOps) Platform

This guide will be helpful to you if you are:

What is MLOps?

MLOps is shorthand for machine learning operations, a set of best practices for organizations to build, test, validate, and deploy ML models successfully. It encompasses the entire development cycle for machine learning models from data collection to deployment to production.

MLOps tools and practices help guide the creation and quality of ML and AI while allowing engineers to collaborate efficiently and increase the pace of model development and production. It provides the framework for continuous integration and continuous deployment (CI/CD) practices that allow for controlled experimentation to train models with the proper monitoring, validation, and governance required.

What is the difference between DevOps and MLOps?

While following a similar trajectory, DevOps focuses on the development, testing, and operational components involved in software development. Its principal goals include the automation of processes, continuous delivery of products, and feedback loops to continue iteration. Rather than quarterly or annual updates, DevOps sets the table for continuous delivery whenever enhancements or refinements are ready for release.

By comparison, MLOps streamlines the machine learning lifecycle from start to finish to bridge the gap between design, modeling, and operations. Commonly in machine learning development, model development and operations are separate processes handled by separate teams. This requires a handover with manual steps that can introduce human error and produce long cycle times.

With MLOps, the process of data collection, preprocessing, model training, evaluating, and deployment are unified in a single workflow to facilitate collaboration and communication. Similar to how the DevOps process works for software, MLOps facilitates a seamless workflow for how production models for ML and AI are developed, deployed, and optimized.

What is an MLOps platform?

An MLOps platform is a tool that provides data scientists and engineers with a collaborative environment to manage the entire MLOps lifecycle. It can ease stages of the ML development process by providing management, collaboration, and optimization features relating to operational aspects of the machine learning lifecycle to accelerate and improve the development process. MLOps enables shorter cycle times even for complex machine learning projects that require complex steps.

In a traditional data science environment, data scientists typically have a single objective and search for a single solution for complex problems. With MLOps, multiple solutions are applied, which requires multiple adjustments to the approach. An MLOps platform significantly speeds this iteration and experimentation cycle to reach a stable state for production.

What is machine learning lifecycle management?

MLOps management allows you to manage the entire process across the AI lifecycle for production, including:

Once data is ready, the next phase begins, including:

And finally:

Because MLOps features a (CI/CD) process similar to DevOps, at the end of one cycle, the iteration and refinement process loops back to the beginning to improve future performance and deep learning models.

The rapid growth of the AI and machine learning market have spawned a plethora of startups. AI and ML were already a $1.41 billion industry at the start of 2021 and expected to grow to $20 billion by 2025.

With such explosive growth, data scientists have the choice of using best-of-breed solutions or end-to-end platforms but it’s become increasingly difficult to know which startups or platforms to use and whether they will integrate with your systems.

Best-of-Breed versus End-to-End Platforms

The lifecycle of machine learning solutions is very complex, which means you might sacrifice some functionality with an end-to-end platform. However, using best-of-breed solutions means you have to integrate multiple platforms and systems. Will they all work together seamlessly? It can take a lot of experimentation and expense to figure out.

While a best-of-breed solution may offer some advanced functionality, you also have to be worried about what happens if one of these solutions you piece together no longer functions or the startup behind it goes out of business. Will your entire tech stack still function? Can you plug in a replacement? Will the replacement integrate seamlessly with the rest of your tech stack?

With an end-to-end platform, you know that everything will work together. End-to-end platforms can be helpful for organizations that want to tie their MLOps to a single vendor for ease of implementation. The drawback of working with end-to-end products, however, is that they can lock you into that vendor’s process and tools.

Ultimately, however, whether to use multiple best-of-breed solutions or a comprehensive MLOps platform is a decision every organization will have to make for itself.

Which Platform Works Best for Your Use Case?

Which platform works best will depend on your use case. You need different capabilities depending on the use case you envision. For example, testing a proof of concept requires data preparation, feature engineering, model prototyping, and validation using experimentation and data processing. However, if you require frequent retraining, such as is used in fraud detection, you need ML pipelines to connect additional steps like data extraction and preprocessing in addition to model training.

When evaluating MLOps platforms, you should first define your use case to ensure any platform you are evaluating has the right features to support your needs.

About CI/CD for Machine Learning

Continuous integration and continuous deployment CI/CD can be applied to any software development/deployment lifecycle, including ML. Because machine learning models only provide value once they reach production, employing CI/CD can reduce the time it takes to see a return on your machine learning investment. You get your product to market more quickly and provide incremental refinements.

With an end-to-end MLOps platform, you can streamline your CI/CD process.

Best Practices

Machine learning is iterative and complex by nature, but the complexity isn’t limited to the data science behind the technology. Efficient ML deployment requires efficient processes, teamwork, and communication. Successful machine learning teams need to be highly functional when it comes to key components, such as:

Data science can drive your business forward in multiple ways by leveraging people, processes, and technology.

Organizations that invest in MLOps and other data science initiatives see significant gains.

An investment in MLOps should lead to demonstrable improvements. When discussing ML solutions with a provider, ask for case studies that demonstrate ROI and impact along with references from current clients that can talk about workflow and ease-of-use.

You should also ask targeted questions to ensure you understand the benefits and limitations of any MLOps provider across these areas.

Model Metadata Storage and Management

Data and Pipeline Versioning

Hyperparameter Tuning

Orchestration and Workflow Pipelines

Model Deployment

Production Model Monitoring

Comet provides a cloud-based or self-hosted meta machine learning platform so data scientists can track, compare, explain, and optimize experiments and models. This allows you to support your development strategy whether you’re working on-premises servers or using a private cloud or hybrid solution.

Comet allows you to manage and optimize models across the entire ML lifecycle from experiment tracking to monitoring models in production. This accelerates development so you can drive business value faster and meet the demands of enterprise teams by deploying machine learning at scale.

Fast Integration

Comet allows for fast integration by adding just a few lines of code to your script or notebook to start tracking experiments. It works whenever you run your code with any machine learning library and for any machine learning task.

Compare Experiments

With Comet, you can easily compare experiments to compare the differences in model performance. You can compare:

Monitor, Alert, and Debug

You can monitor your models in every step from training to production. If something doesn’t perform properly, you get alerts so you can debug your models.

Workspace, Reporting, and Visualization

You also get transparency into your entire lifecycle allowing collaboration by data scientists, data team members, and business stakeholders. You can also build custom interactive visualizations with real-time displays.