Building AI Agents and Vector Search

Moving beyond OpenAI
Guy Eshet
Guy Eshet
Product Manager at Qwak
Gad Benram
Gad Benram
Founder & CTO at TensorOps
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Join Gad Benram, CEO at TensorOps and Guy Eshet, Product manager at Qwak, to learn how data teams and organizations are building AI based search solutions at scale. During this session, we’ll see how to simplify AI and ML development for efficient and cost-effective embedding models and LLM deployments. We’ll guide you through the possibilities for building advanced AI agents and implementing vector search capabilities for your own data.

Additionally, we’ll go through the following topics:

  • Using LLMs for enhanced language processing and understanding
  • Deploying embedding models for advanced information retrieval
  • Building, deploying and monitoring AI agents in production

Whether you are a data scientist, AI developer, or product manager, this webinar will provide you with valuable insights and practical knowledge to enhance your AI projects.

Join Gad Benram, CEO at TensorOps and Guy Eshet, Product manager at Qwak, to learn how data teams and organizations are building AI based search solutions at scale. During this session, we’ll see how to simplify AI and ML development for efficient and cost-effective embedding models and LLM deployments. We’ll guide you through the possibilities for building advanced AI agents and implementing vector search capabilities for your own data.

Additionally, we’ll go through the following topics:

  • Using LLMs for enhanced language processing and understanding
  • Deploying embedding models for advanced information retrieval
  • Building, deploying and monitoring AI agents in production

Whether you are a data scientist, AI developer, or product manager, this webinar will provide you with valuable insights and practical knowledge to enhance your AI projects.

Qwak optimizes ML Model Production

“We ditched our in-house ML platform for Qwak. I wish we had found them sooner.”
Upside
“Qwak streamlines ML development from prototype to production, freeing us from infrastructure concerns and maximizing our focus on business value.”
Notion
“People ask me how I managed to deploy so many models while onboarding a new team within a year. My answer is: Qwak.”
OpenWeb
“With Qwak, our ML team efficiently manages and deploys various models, both batch and real-time. The addition of an observability and Vector DB layer has been a game-changer, allowing us to confidently bring 10 models into production. Qwak's robust and streamlined approach has significantly enhanced our operational efficiency.”
Happening (Superbet)