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The Future of AI Agents
·OnlineOnlineExploring open source profitability and small language models – Aditya Gautam
Aditya Gautam has built his career at the intersection of AI research, large-scale deployment, and public discourse. With experience at Google, Meta, and leading academic conferences, he works on large language models, AI agents, and responsible AI at scale.
In this episode, Aditya will explore the debates around open-source AI, the economics behind LLMs, and the barriers enterprises face when adopting AI agents. He also shares his perspective on the rise of small language models and what these shifts mean for the future of AI.
We plan to cover:
- Open-source AI: democratization and risks
- The economics of LLMs and the challenge of profitability
- Why enterprises struggle to adopt AI agents in practice
- The role of small language models in efficiency and cost reduction
- Emerging trends in AI research and deployment
About the Guest
Aditya Gautam is an AI expert contributing to the advancement of the field through industrial innovation, academic research, and public discourse. At the core of his work, he designs and builds systems in the domain of Large Language Models (LLMs) and AI Agents. He has successfully led several high-impact applied AI initiatives at Meta, focusing on both enhancing core ranking and recommendation algorithms and successfully architecting, developing, and productionizing state-of-the-art GenAI systems at massive scale.
As an active voice in the Generative AI community, Aditya actively disseminates his expertise on the global stage. He was featured among the best speakers at the Databricks Data + AI Summit 2025 for his talk on "Optimize Cost and User Value Through Model Routing AI Agent." He has published research on LLMs in Information Retrieval and Multi-Agent Systems, and his thought leadership has been featured in major media articles, industry interviews, and AI podcasts. He is a frequent speaker and panelist at premier events and conferences such as Analytics Vidhya. His influence in the academic community is demonstrated by his service as a respected peer reviewer for top-tier venues like NeurIPS, ICML, and AAAI. Aditya holds a Master’s degree from Carnegie Mellon University.
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From APIs to Warehouses: AI-Assisted Data Ingestion with dlt
·OnlineOnlineThis hands-on workshop focuses on building reliable data ingestion pipelines to data warehouses (for example, Snowflake) using dlt (data load tool), enhanced with LLMs, the dlt dashboard, and dlt MCP.
You’ll work through the key building blocks of a production-ready ingestion setup, including:
- Extracting data from APIs, files, and databases
- Normalizing data into consistent schemas
- Writing data to a data warehouse (e.g. Snowflake)
- Using LLMs to accelerate dlt pipeline development
- Validating data and schema changes using the dlt dashboard and dlt MCP
The session is fully practical and code-driven. By the end of the workshop, you’ll understand how to design maintainable, scalable ingestion pipelines and use AI and validation tools to build them faster and with confidence.
## About the Speaker
Aashish Nair is a Data Engineer at dltHub and the creator of the famous dlt deployment course, where he teaches best practices for running dlt pipelines in production.
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This event is sponsored by dltHub23 attendees
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