Tecton
Computer Software, 995 Market St, San Francisco, California, 94103, United States, 51-200 Employees
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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- Headquarters: 995 Market St, San Francisco, California, 94103, United States
- Date Founded: 2019
- Employees: 51-200
- Revenue: $50 Million to $100 Million
- Active Tech Stack: See technologies
Industry: Computer Software
SIC Code: 6531
Tecton Org Chart and Mapping
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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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