The Fastest Path to Production Ready Machine Learning and AI Models.
According to Gartner, more than half of data science projects are not fully deployed today. This is a frustration to the data science team, who has worked hard to create models which impact the business. To short circuit this trend, Data Science teams need a repeatable way to prepare models for production from the start. FastScore integrates with all modern analytics languages and IDEs, providing a simple, uniform path to activate each model.
According to Gartner, data science teams often lack formal communication channels with their IT counterparts, making model deployment a misunderstood process. Communication channels can be established with both organizational and technology support. FastScore provides tooling supporting both the Data Science and IT team needs, enabling a clear, defined path to production.
Watch this webinar where our lead data scientist and product manager talk through this critical relationship.
Create confidence for data scientists when handing models off to IT and reduce troubleshooting frustration.
There is a lot of research showing the business benefits from adoption machine learning and AI broadly across any organization. But while it’s easy to say we will leverage our data with open source solutions, it’s not as simple to implement scalable repeatable capability in the organization. Adopting tools and processes that solve for broad technical adoption, and prevent lock in for the long term. Watch this webinar which talks about the fundamental organization and technical needs for building ML capability across the enterprise.
Much of the incredible advancement in the application of ML and AI to solving business problems comes from the modern compute and open source software world of today. Teams need to be free to choose the latest packages and languages to solve problems, without incurring large technical debt for downstream processes like production deployment. FastScore brings to market some key abstractions enabling true freedom of choice of the data science team, simultaneously preventing unscalable diversity on the IT side of the house.
Read this blog post from our CTO that outlines a core FastScore abstractions for models, and how they help teams co-exist in today’s fast paced, open source world.