What is a machine learning framework?

This article was originally published at Algorithimia’s website. The company was acquired by DataRobot in 2021. This article may not be entirely up-to-date or refer to products and offerings no longer in existence. Find out more about DataRobot MLOps here.

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.

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: 

• Understand the costs and benefits of both options
• Figure out the technical resources you would need to maintain an ongoing machine learning lifecycle in both circumstances
• Be a champion of the transformational capabilities of enterprise machine learning at your organization

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

Disclaimer: this article was originally published at Algorithimia’s website. The company was acquired by DataRobot in 2021. This article may not be entirely up-to-date or refer to products and offerings no longer in existence.

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 DataRobot, 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.

Connectivity

A flexible machine learning framework connects to all necessary data sources in one secure, central location for reusable, repeatable, and collaborative model management. 

• Manage source code by pushing models into production directly from the code repository
• Control data access by running models close to connectors and data sources for optimal security
• Deploy models from wherever they are with seamless infrastructure management

Deployment

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. 

• Deploy in any language and any format with flexible tooling capabilities 
• Serve models with a git push to a highly scalable API in seconds
• Version models automatically with a framework that compares and updates models while maintaining a dependable version for calls.

Management

Manage MLOps using access controls and governance features that secure and audit the machine learning models you have in production. 

• Split machine learning workflows into reusable, independent parts and pipeline them together with a microservices architecture
• Operate your ML portfolio from one, secure location to prevent work silos with a robust ML management system
• Protect your models with access control
• Usage reporting allows you to gain full visibility into server use, model consumption, and call details to control costs

Scaling

A properly scaled machine learning lifecycle scales on demand, operates at peak performance, and continuously delivers value from one MLOps center.

• Serverless scaling allows you to scale models on demand without latency concerns, providing CPU and GPU support 
• Reduce data security vulnerabilities by access controlling your model management system
• Govern models and test model performance for speed, accuracy, and drift
• Multi-cloud flexibility provides the options to deploy on Algorithmia, the cloud, or on-prem to keep models near data sources