What is a machine learning framework?
A machine learning framework is an interface that allows developers to build and deploy machine learning models faster and easier. A tool like this allows enterprises to scale their machine learning efforts securely while maintaining a healthy ML lifecycle.
Machine learning frameworks have become standard practice in recent years. They provide democratization in the development of ML algorithms while also speeding up the process. Enterprise-level organizations are largely realizing the need to launch machine learning frameworks in their own ML endeavors.
So, what is a machine learning framework?
A machine learning framework is an interface that allows developers to build and deploy machine learning models faster and easier. A tool like this allows enterprises to scale their machine learning efforts securely while maintaining a healthy ML lifecycle. Enterprises have the option to build their own custom machine learning framework or to buy a framework, like Algorithmia, that can be adapted upon for their needs.
Building or buying a machine learning framework
According to Gartner, 85 percent of all machine learning projects fail, and most organizations that are actively developing machine learning capabilities are struggling to get a return on investment. This is because infrastructural requirements, developer resources, time, and the costs of building a machine learning framework in-house are greater than what organizations expect.
Enterprises can minimize the time to value for their machine learning projects by purchasing an off-the-shelf framework that fits into their existing workflow. This allows the organization to gain competitive advantages from their ML projects sooner and therefore benefit from them longer.
When considering whether to build or buy a machine learning framework, it’s important to:
To dive deeper into building vs buying a machine learning framework, download our whitepaper, building versus buying an ML management platform.
Features of Algorithmia’s machine learning framework
Algorithmia’s machine learning framework allows enterprises to deploy, manage, and scale their machine learning portfolio. Algorithmia is the fastest route to deployment, and makes it easy to securely govern machine learning operations with a healthy ML lifecycle.
With Algorithmia, you can connect your data and pre-trained models, deploy and serve as APIs, manage your models and monitor performance, and secure your machine learning portfolio as it scales.
A flexible machine learning framework connects to all necessary data sources in one secure, central location for reusable, repeatable, and collaborative model management.
Machine learning models only achieve value once they reach production. Efficient deployment capabilities reduce the time it takes your organization to get a return on your ML investment.
Manage MLOps using access controls and governance features that secure and audit the machine learning models you have in production.
A properly scaled machine learning lifecycle scales on demand, operates at peak performance, and continuously delivers value from one MLOps center.