Machine learning operations
MLOps provides end-to-end capabilities for deploying, managing, governing, and securing machine learning and other probabilistic models in production.
MLOps is a framework for managing machine learning and applying DevOps principles to accelerate the development, testing, and deployment of AI/ML models. Its goal is to help organizations conduct continuous integration, development, and delivery at scale of AI/ML models.
The machine learning lifecycle allows for the rapid and large-scale delivery of models.
The goal of a machine learning operations process is to provide structure for your machine learning work in a way that is both productive and repeatable. Even with collaboration across several departments, MLOps ensures that models are delivered as quickly and effectively as possible.
Despite the fact that ML projects only provide value after models have been validated in production, organizations frequently underestimate the complexity and difficulties of moving ML to production. Historically, more than 85% of ML models are produced but never deployed, and delivery periods are measured in months when they should be measured in hours.
The Advantages of Having an Organizational MLOps Strategy
Getting Value From Models More Efficiently
Automate the deployment of Machine Learning models to significantly decrease the time it takes to produce from weeks to minutes. Then keep learning and improving so you may scale efficiently.
To improve productivity, establish clear roles and minimize time and roadblocks by integrating with current workflows and tools. To make timely judgments, have immediate access to monitor and report on ongoing projects.
To lower costs dramatically, systematically manage compute resources across models to achieve company objectives and cost-performance metrics. On-premises, in the cloud, or in a hybrid setup may be used.
How to get MLOps up and running in your company
Making MLOps Work With Your Existing Tech Stack
The implementation of ML requires the existence of a variety of other technologies. The systems you develop must interoperate with your existing corporate IT, platform choices, pipeline approach, and monitoring solutions. Check out our MLOps platform and management framework to learn about and evaluate all of your key connections between algorithms and applications.
Don’t Start From Scratch
Putting it another way, building an ML platform from the ground up will take years and cost many times more in cash. When you consider the expense of personnel, lost opportunities, and errors made along the road, it’s easy to see why creating your own ML software is so time-consuming and costly. Look for a commercial MLOps platform or service.
Go For Flexibility
Don’t put limits on your data science teams’ capabilities by operating procedures that are already in place. Look for an MLOps procedure that allows for variation and scale, and works with the infrastructure and data production environments you already have. Your MLOps approach should aid rather than impede your current operations.
Reduce delivery time
Make sure that the method you’re using to run your ML projects allows you to scale and deliver models in production quickly. A strong MLOps program will allow your organization more time to apply its discoveries to improve business growth.
Alter your policies when needed
Set up your model to include only successful outcomes. For example, set the resolution for an experiment at a specific threshold (20% is typical). In a production environment, audit both your manufacturing procedures and the performance of your models. Take a moment to reconsider your approach, infrastructure, and ROI estimates at each sprint, keeping stakeholders informed to support AI Governance requirements.
Datatron Can Make Your MLOps Framework Drive ROI
Book a demo today to learn how Datatron can help AI/ML ops teams become more efficient, streamline management of ML models and realize greater business value.