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Vamshi Ambati, PhD

Building Something Big | 2x Founder | 3x Exits | @Predera, @Latentstructure, @Base, @PayPal, @Carnegie Mellon, @IIIT, @ISB

Vamshi Ambati, PhD is an accomplished data scientist and entrepreneur in the Bay Area. He is the Founder and CEO of PredEra, an AI product and solutions startup, which specializes in data science, text mining, machine learning, personalization, and machine translation. He is also an adjunct faculty member at IIIT Hyderabad and teaches at the Indian School of Business, Hyderabad.

Vamshi's educational background includes a BTech in Computer Science from the International Institute of Information Technology, a Ph.D. in Computer Science, and a Master's in Software Engineering from Carnegie Mellon University. His research interests cover a wide range of topics encompassing machine learning, natural language processing, software engineering, digital libraries, machine translation, and information retrieval.

Prior to founding PredEra, Vamshi served as the Founder and Principal at LatentStructure LLC, Director of Data Science & Engineering at Base CRM (acquired by Zendesk), lead data scientist at PayPal/eBay Inc., and research scientist at AT&T Interactive. He has also served as the founder and CTO of CrowdEagle and a research intern at AT&T's Shannon Labs.

Overall, Vamshi's professional journey has been focused on developing innovative solutions in artificial intelligence, machine learning, and natural language processing. He has extensive experience in leading and managing research teams, working with large datasets, and building end-to-end AI-powered products.

Highlights

Aug 30 · linkedin

Llama models (OSS) are closing the accuracy gap with Closed-AI models, and paving changes in the GenAI business landscape. My interpretations from the llama blog - https://lnkd.in/g5ST4gvk

a) Hosting OSS models efficiently is becoming interesting as an ecosystem, as can be seen from these mind boggling stats:

  • Downloads: 350M lifetime on Hugging Face, with a remarkable 10x year-over-year growth and 20M downloads in the past month.
  • Cloud token volume: Surged to 2x between May and July compared to CSPs and an impressive 10x increase from January to July among the largest CSPs.
  • Integration partners: A significant 5x rise since Llama 3.1 release.

b) Enterprise adoption of Llama models will continue to rise and is a good sign of growing trust in using open-source models for customer facing apps:

  • AT&T: Achieved a 33% enhancement in search-related responses in customer service, leading to cost reductions and quicker issue resolution.
  • Accenture: Expecting a substantial 70% boost in productivity and 20-30% improvement in the quality of ESG report creation.
  • Goldman Sachs: Leveraging document information extraction efficiently.
  • DoorDash: Streamlining daily tasks for software engineers, such as automating pull request reviews.
  • Nomura: Utilizing code generation, log analysis, and document processing.
  • Shopify: Enhancing product metadata with an impressive 40-60M inferences per day.
  • Zoom: Offering an AI companion service.
Mar 27 · linkedin

Exciting news ahead :-)

I am beyond thrilled to announce a remarkable milestone in the life of Predera. We have been acquired by an innovative early-stage entity and will not be continuing in our current form.  Later when we can say more about what we’re working on new, we will let you know.

Thanks to my amazing co-founder Nazeer H Shaik without whom, this day would not be possible! I am deeply grateful to my family for their unwavering support and patience with my frequent unavailability over the past seven years. My heartfelt thanks to my advisors, mentors, and friends who have provided guidance, encouragement, and support, championing our success throughout this time. (You know who you are!)

Finally, a big thank you to our customers, colleagues, followers, well-wishers and friends of Predera - for your support, faith, and enthusiasm over the years. We have valued you!

Cheers!

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Vamshi Ambati, PhD
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Location

San Francisco Bay Area