Earlier this week, I attended Build Bengaluru 2024, an event by Snowflake focused on generative AI, data engineering, and their ecosystem. What drew me in was the agenda—it promised to deliver insights into concepts like Retrieval-Augmented Generation (RAG) and AI app development, areas I’ve been deeply interested in exploring for my projects.
General Impressions
The event kicked off with a bustling crowd, which honestly surprised me. Even though I had pre-registered, there was still a queue at the registration desk. But the atmosphere made up for the wait—it was buzzing with energy and curiosity.
Key Sessions Attended
Session 1: Data Power to Chatbot Power—Engineering the Perfect RAG Snowball
Speaker: Daniel Myers
Key Highlights:
- An in-depth explanation of Retrieval-Augmented Generation (RAG) with Cortex Search and its enterprise applications.
- Features like Hybrid Retrieval, Cortex Analyst, and Cortex Chat API were thoroughly discussed.
Personal Takeaway:
This session was incredibly impactful as it aligns directly with my ongoing projects and future plans, particularly in exploring RAG implementations. Stay tuned for a dedicated blog post where I dive deeper into this topic and its applications!
Session 2: Building Enterprise-Grade AI Apps with Snowflake Native Apps Using Snowpark Container Services
Speaker: Harish Chintakunta
Key Highlights:
- Showcased how Snowflake Native Apps simplify AI app development and deployment.
- Explained seamless workflows through integration with Git, REST APIs, Python, and Snowflake CLI.
- Emphasized security with in-platform app execution.
Session 3: Harnessing Generative AI with Snowflake and AWS
Speakers: Avinash Venkatagiri and Bharath Suresh
Key Highlights:
- Insights into Amazon Bedrock’s diverse foundation models and their integration with Snowflake for contextualized AI applications.
- Demonstrated the capabilities of the AWS Generative AI Stack for scalable AI model development and deployment.
Insights for My Projects
Another highlight was learning about Snowflake Native Notebooks. The ability to train models directly and leverage Snowflake’s integrations, like Streamlit, felt like a game-changer. It simplifies the process of experimenting with models and speeds up development—a feature I’m excited to explore further.
Reflections
While I didn’t interact much with other attendees, the event itself offered plenty of value. Snowflake’s focus on generative AI and data apps feels relevant for anyone working in the AI/ML space.
If you’re considering attending Build next year, I’d highly recommend it. Whether you’re looking to deepen your technical knowledge or discover new tools for your workflow, it’s an excellent opportunity to stay ahead in the fast-evolving world of AI and data engineering.
Stay tuned for a separate deep dive into the "Data Power to Chatbot Power" session—it deserves a post of its own!
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