Tecton

Tecton

Computer Software, 995 Market St, San Francisco, California, 94103, United States, 51-200 Employees

tecton.ai

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Who is TECTON

Founded by the team that created the Uber Michelangelo platform, Tecton provides an enterprise-ready feature platform to make world-class #machinelearning accessible to every company. Mac...

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  • 995 Market St, San Francisco, California, 94103, United States Headquarters: 995 Market St, San Francisco, California, 94103, United States
  • 2019 Date Founded: 2019
  • 51-200 Employees: 51-200
  • dollar-icon Revenue: $50 Million to $100 Million
  • tech-icon Active Tech Stack: See technologies

industries-icon Industry: Computer Software

SIC SIC Code: 6531

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Frequently Asked Questions Regarding Tecton

Answer: Tecton's headquarters are located at 995 Market St, San Francisco, California, 94103, United States

Answer: Tecton's official website is https://tecton.ai

Answer: Tecton's revenue is $50 Million to $100 Million

Answer: Tecton's SIC: 6531

Answer: Tecton has 51-200 employees

Answer: Tecton is in Computer Software

Answer: Tecton contact info: Phone number: Website: https://tecton.ai

Answer: Founded by the team that created the Uber Michelangelo platform, Tecton provides an enterprise-ready feature platform to make world-class #machinelearning accessible to every company. Machine learning creates new opportunities to generate more value than ever before from data. Companies can now build ML-driven applications to automate decisions at machine speed, deliver magical customer experiences, and re-invent business processes. But ML models will only ever be as good as the data that is fed to them. Today, its incredibly hard to build and manage ML data. Most companies dont have access to the advanced ML data infrastructure that is used by the Internet giants. So ML teams spend the majority of their time building custom features and bespoke data pipelines, and most models never make it to production. We believe that companies need a new kind of data platform built for the unique requirements of ML. Our goal is to enable ML teams to build great features, serve them to production quickly and reliably, and do it at scale. By getting the data layer for ML right, companies can get better models to production faster to drive real business outcomes.

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