Building AI Agents & Vector Search
Moving Beyond OpenAI

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.     

Gad Benram
Guy Eshet

Qwak optimizes ML Model Production for
ML driven organizations.

Here is what our customers have to say about us:

"From the get go, it was clear that Qwak understand our needs and requirements. The simplicity of the implementation was impressive.
Automatic deployment and continuous training were crucial to allow us to scale. Qwak gave us a type of "Jenkins" for machine learning."

Shaked Zychlinksi Head of Recommendations Research

"Using Qwak allowed us to focus on creating a business impact rather than spending valuable time on our infrastructure setup.

At JLL our development is very time sensitive. As a result of implementing Qwak, we improved our execution time by 4.5X."

or hiltch
Orr Hiltch, Vice President of Engineering

"Qwak streamlines our machine learning development all the way from prototype to production, freeing us from infrastructure concerns and maximizing our focus on business value delivery."

Edward Zhou,
Software Engineer