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.

What Is MLOps Not?

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:

• Authentication and Authorization
• Operational maintenance, ownership, and support
• Disaster recovery
• Monitoring and alerting
• Automated testing (both data and model)
• Auditing
• Schema management
• Provenance
• Scalability (including to zero)
• Model artifact lineage and metadata
• And many more…

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:

• Governance allows organizations to manage and control risk throughout the ML development lifecycle. From audit trails that show which models are used to evidence that a model has been signed off for production deployment. Banks are good at this form of MLOps because they have regulatory requirements that force them to do so. But organizations everywhere can leverage the same techniques to reduce risk.
• Provenance is often described as the ability to track a lineage, from a deployable artifact to the data it originated from. But delivering provenance provides robust, repeatable pipelines. Provenance promotes DevOps and GitOps; proven cloud-native techniques. And provenance provides uniformity - you’ll find that common patterns are reused, and operational simplifications because of the uniformity.
• Operational automation helps reduce the toil involved with running ML models in production. This idea is less general and more specific. Precisely what you automate and how you do it depends on various non-functional requirements, like the size of your team, or how popular the services are. But the benefits are universal. If you can automate a dangerous or tedious part of the process, this reduces the risk of mistakes, enforces compliance and reduces the operational burden on engineers and data scientists

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.

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