Sensational MLOps Services

What is MLOps?

MLOps is a collection of tools and methodologies that aim to productionize and operationalize machine learning development.

If you ask three data scientists to implement a machine learning (ML) solution you will receive 3 different methodologies, stacks, and operational viabilities. MLOps processes attempt to standardize and unify the development of projects to enhance security, governance, and compliance. MLOps technologies automate repetitive tasks like training a production model or deploying solutions into production.

MLOps is more than just a set of technologies. It doesn’t matter if you’re building your MLOps process on Azure, AWS or GCP. The goals are the same.

MLOps is more than a CI/CD process. It’s a combination of tools and ways of working, ideologies that derive from DevOps, that are unique to each business.

Our MLOps services help to guide you through your MLOps journey.

MLOps is not a single platform.

There are many products that claim to be MLOps. But productionizing machine learning (ML) and reinforcement leaning (RL) models is much more than being able to serve a model on an endpoint. Running successful, resilient, scalable ML and RL takes time and requires significant expertise.

There are products that suggest that if you combine model training and model serving, you have an MLOps system. But that misses the point of the value of implementing MLOps. The whole point is to improve the quality of service of a model. And this includes items such as auditing and cyber security, which are often neglected by vendors.

In fact, true MLOps involves a whole range of other development tasks that are just as important:

How Does MLOps Help?

MLOps describes the operational framework, unique to your organization, that maximizes the quality and usefulness of data science.

The phases of an MLOps framework are often described in terms of the machine learning (ML) development lifecycle. But this occludes the big picture and obfuscates the nitty-gritty details. We need a better way of describing the value of MLOps.

In our experience, at a high level, the value of our MLOps services can be attributed to three categories:

How to ML Deployment Pipelines Relate to MLOps?

ML deployment pipelines are necessary to provide robust, repeatable procedures for managing your models.

They are one of the most important parts of moving a trained model to a place where it can be consumed by downstream applications or users. This means that in many of our MLOps development projects this is one of the first areas our MLOps experts tackle.

But remember that it only forms a small part of your overall MLOps strategy. Other phases that fall under the MLOps banner, like training, monitoring, provenance, and data versioning can be just as important.

Ultimately its importance depends on your unique circumstances and ML workload. Winder.AI are experienced MLOps consultants that can help you make the right decision.

Talk to Sales