DataRobot is the leader in Value-Driven AI – a unique and collaborative approach to AI that combines our open AI platform, deep AI expertise and broad use-case implementation to improve how customers run, grow and optimize their business. The DataRobot AI Platform is the only complete AI lifecycle platform that interoperates with your existing investments in data, applications and business processes, and can be deployed on-prem or in any cloud environment. DataRobot and our partners have a decade of world-class AI expertise collaborating with AI teams (data scientists, business and IT), removing common blockers and developing best practices to successfully navigate projects that result in faster time to value, increased revenue and reduced costs. DataRobot customers include 40% of the Fortune 50, 8 of top 10 US banks, 7 of the top 10 pharmaceutical companies, 7 of the top 10 telcos, 5 of top 10 global manufacturers.
Posts by DataRobot
Data scientists drive business outcomes. Many implement machine learning and artificial intelligence to tackle challenges in the age of Big Data. Learn more.
Analysts at the Business Application Research Center (BARC) surveyed 248 companies from a variety of industries to learn what companies are doing with DataOps and MLOps today. Learn more about their findings.
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You need to know where your deployed models are, what they do, the data they use, the results they produce, and who relies upon their results. That requires a good model governance framework.
At the confluence of cloud computing, geospatial data analytics, and machine learning we are able to unlock new patterns and meaning within geospatial data structures.
Go from predictions to real-world results by augmenting business decisions with AI.
It feels like a lot of AI consulting these days is like the technology itself, more promise than payoff. In her book The Business of Consulting, Elaine Blech shares a joke about consulting’s reputation, where a consultant is asked the time by a client. The consultant in turn asks for the client’s watch and says, “Before I give you my…
ML operations management platforms are essential to getting models into production and keeping them there. A model or pipeline that is not in production is one that cannot provide any value (or limited value) to the business. But while they have very high operational benefits, they are not value-add from a business point of view.
According to Gartner, 51% of enterprises have started their AI journey, but just 10% of ML solutions get deployed. Learn why in the article.