Machine learning operations (MLOps) involve a set of techniques and principles aimed at the design, development, deployment, and maintenance of machine learning models for production use. The purpose of MLOps is to establish a clear set of guidelines to simplify the complex process of bringing a model into production. You can also learn more about the differences between large language model operations (LLMOps) and MLOps.

MLOps can be seen as the intersection of Machine Learning, DevOps, and Data Engineering:

Source: Wikimedia Commons

Machine Learning focuses on developing artificial intelligence, Data Engineering involves the gathering and processing of large-scale datasets, and DevOps covers the collaboration between engineers and the management of the production process.

What is an MLOps platform?

An MLOps platform provides key functionalities, typically offering an all-in-one solution designed to equip you with the tools necessary to transition from research to production in machine learning.

In this blog post, we compare UbiOps to all-in-one MLOps platforms. In general, we believe UbiOps is better suited for many use cases for three main reasons that we will outline.

An all-in-one solution is maybe not always necessary for experienced teams

All-in-one MLOps platforms have emerged over the past five years, offering comprehensive solutions for companies looking to engage in MLOps. However, for experienced teams and companies, such solutions can sometimes become limiting rather than beneficial for several reasons:

• Inflexibility: All-in-one solutions can limit flexibility by restricting your options to the tools provided by the platform. If the platform’s direction doesn’t suit your needs, your entire MLOps setup can be impacted.
• Integration Challenges: Switching to an all-in-one MLOps platform can be difficult if you already have parts of your MLOps environment in place. Migrating existing workflows can be painful, and learning a new platform may come with a steep learning curve. Compatibility issues with existing tools can also arise, and integration in hybrid or multi-cloud environments can be particularly challenging.
• Bloat: Many all-in-one platforms include a wide range of features to cater to a broad audience, which can lead to bloat. As a result, they become generalists rather than specialists, offering a variety of features without excelling in any particular area.
• Cost: Related to how bloated the platforms can be, you will end up paying more because you will be paying for features you do not particularly need.

Overall, an all-in-one MLOps solution may not be ideal for many use cases due to its potential for limiting flexibility, integration challenges, and unnecessary features.

Too much focus on experimentation and model design 

MLOps platforms often focus too much on experimentation and model design, which are early stages in the MLOps life cycle. This can lead to neglect of the operational and production stages, where stable, practical solutions are essential, especially for resource management and scaling.

The research and experimentation phases require a different approach from deployment and production, each requiring distinct skill sets. All-in-one platforms sometimes overlook these differences, leading to inefficiencies in the later stages of the MLOps life cycle.

Can be costly

All-in-one MLOps platforms can also be costly, often charging for features that may not be necessary for your use case. Since these platforms aim to be generalists, their pricing often includes the development and maintenance costs of features you may never use. Licensing fees and other costs may not align with your actual needs, making the platform unnecessarily expensive.

Why UbiOps is better suited

UbiOps is not an all-in-one or general-purpose MLOps platform. It specializes in the deployment and production stages of the MLOps life cycle. Below is an overview of where UbiOps fits within the MLOps life cycle:

Easy Integration: UbiOps is designed to integrate seamlessly with other tools and platforms, particularly those focused on the earlier stages of the MLOps lifecycle. Its focus on deployment and production ensures it can complement other solutions used for model development, experimentation, and training. Unlike all-in-one platforms, UbiOps is framework-agnostic and encourages integration with any framework, tool, or library. It also supports hybrid, multi-cloud, and on-premise setups, making it cloud-agnostic as well.

Reduced Costs: By focusing on the production and deployment stages, UbiOps provides a streamlined set of features specifically for these purposes, leading to lower overall costs compared to general-purpose platforms. UbiOps is optimized for efficient scaling and production setups, which helps reduce operational expenses.

Workflow Optimization: UbiOps offers tools specifically designed to facilitate the deployment and ongoing maintenance of machine learning models. Its focus on production ensures it is well-suited for managing the complexities of model deployment, monitoring, and management, enhancing the efficiency of workflows.

Resource Orchestration: UbiOps is both framework- and cloud-agnostic, offering significant flexibility and adaptability for medium to large companies. UbiOps can be run on the cloud, hybrid setups, or on-premise, helping to avoid vendor lock-in, which can occur when relying on a single platform.

Scaling and Management Features: UbiOps has features geared toward the deployment and production stages, including flexible pipelines that allow users to manage and control data streams and models. The platform’s operators feature enables users to add logic and perform operations on data streams.

Conclusion

UbiOps’ specialization in the deployment and production stages of the MLOps life cycle makes it more integrable, cost-effective, and optimized compared to many all-in-one platforms. Its targeted approach ensures robust support for taking models from development to production and maintaining them efficiently. If you’re looking for tools for data management and experimentation, UbiOps may not be the right fit, but for production and deployment, it offers significant advantages.