Operationalize and scale machine learning to maximize business value with MLOps-as-a-Service

Businesses today are looking to proactively streamline organizational challenges, including communication, collaboration, and coordination with well-established practices. And while building a machine learning model that works well to solve these challenges is being done, it only solves half of the problem. With MLOps, enterprises can ensure agility and speed, a cornerstone in today’s digital world, while streamlining challenges.

Working with data is one thing, but deploying a machine learning model to production can be another. Often, Data Scientists build a great model that can be beneficial for institutions, but deploying the model and utilizing it can pose significant challenges that many fail to solve. At the same time, Data Engineers and ML Engineers are always looking for new ways to deploy their machine learning models to production.

Working with data is one thing, but deploying a machine learning model to production can be another. Often, Data Scientists build a great model that can be beneficial for institutions, but deploying the model and utilizing it can pose significant challenges that many fail to solve. At the same time, Data Engineers and ML Engineers are always looking for new ways to deploy their machine learning models to production.

Industry Challenges that MLOps Can Help Solve

Management of deployment pipelines

Manually performing deployment steps are both
time-consuming and labor-intensive

Data Quality

Many AI applications will be obsolete if not trained
with broader variations of data sets

Data Volume

If the underlying resources are not flexible enough
to add more storage and processing power, the
model’s usability declines

The CriticalRiver
Solution

Our unique offerings to address these business challenges include

Automated CI/CD Pipelines: CI/CD pipelines that automate replicating new environments like staging and production are necessary because of the ever-changing data and the iterative nature of machine learning projects.

Multi-Cloud Support: To deploy models to any cloud-agnostic environment without reengineering their pipelines, enterprises need the ability to store models in any cloud or in-house registry. Integrated MLOps should be able to deploy to any cloud, on-premises, or hybrid environment based on the infrastructure of your choice.

Automated Scaling: As well as provisioning different resource types (CPU, GPU, or TPU), deployment pipelines should be capable of auto-scaling. The system should also provide real-time insights and recommendations for complex model deployment scenarios, such as A/B deployments, shadow rollouts, and phased rollouts, which have become a typical pattern in data-driven applications.

Let’s Start Something New

    You can also email us directly at contact@criticalriver.com