Videos
Welcome to our Video Library, where you can access all the insightful videos. Our collection covers a range of topics, from foundational concepts to advanced strategies, providing you with a comprehensive understanding of the latest trends and innovations from past Devday, Conferences, etc.
1:12:47 Container Security: Understanding the Security Landscape
November 14, 2024
00:05:26 Sahaj Software Corporate Video
October 22, 2024
00:03:03 Client Voice: Technology innovations to transform Talon Outdoor and the Out of Home industry
October 22, 2024
00:04:13 Client Voice: A new ISA platform for Vemo Education to address the US student loan crisis
October 22, 2024
00:03:44 Client Voice: Disrupting OOH media with game-changing solutions
October 22, 2024
00:16:00 The Beginner’s Guide to All Things Sahaj
October 22, 2024
00:07:10 Sahaj – Lending Sixth Sense to a Business with Data Science
October 22, 2024
00:04:59 Sahaj X Citizens GBR – Securing the Great Barrier Reef with Engineering and Collaboration
October 22, 2024
00:26:24 A Playbook For Kickstarting Your Data Journey
October 18, 2024
01:51:40 Dev Day | Understanding the internal workings of databases
October 18, 2024
01:51:14 Uncover the mysteries of IAC
October 18, 2024
01:29:49 The Power of Right Abstractions – The Story of HTTP
October 18, 2024
01:00:43 Testing in Flutter
October 18, 2024
02:32:43 Strangulating infrastructure to containers
October 18, 2024
01:10:09 Starting your journey as Developer
October 18, 2024
00:37:11 Solving real world enterprise problems using Blockchain
October 18, 2024
01:06:50 Shipping LLM Addressing Production Challenges
October 18, 2024
01:29:49 Open language resources and strategies for building speech systems in under-resourced languages
October 18, 2024
01:27:16 MLOps, Data Drift and Concept Drift in Production
October 18, 2024
01:04:29 Mental models used in nation-scale digital certificate platform
October 18, 2024
00:59:49 LLMs in action: Strategies for crafting solutions
October 18, 2024
00:28:46 Decentralized Finance: Business Opportunities and Trend III
October 18, 2024
01:19:04 JVM GC Tuning in containerised environment
October 18, 2024
01:15:20 Intro to Apache Spark 2.x & running a Spark cluster in the cloud
October 18, 2024
00:57:31 From VMs to containers and back again
October 18, 2024
01:07:15 From Concept to Code: Building a Q&A Bot Live with LLM
October 18, 2024
01:18:42 LLM Unleashed: Deep dive into fundamental concepts of LLM
October 18, 2024
00:47:06 Clean Code 101
October 18, 2024
00:37:07 Decentralized Finance: Business Opportunities and Trends IV
October 18, 2024
01:05:19 Decentralized Finance: Business Opportunities and Trends II
October 18, 2024
00:43:03 Decentralized Finance: Business Opportunities and Trends I
October 18, 2024
01:09:42 Classifying Indian addresses for the e-commerce domain
October 18, 2024
01:16:06 Building a search engine for interactive search
October 18, 2024
01:21:56 Automatic Speech Processing for Voice AI
October 18, 2024
00:59:50 LLM Solutions Navigating the landscape
October 18, 2024
Shipping LLM Addressing Production Challenges
By Venkatesh, Suman
October 18, 2024
Summary- How do we balance the need for domain expertise in critical applications (e.g., kidney dialysis) with the requirement of providing a user-friendly interface that's accessible to non-experts? What strategies can be employed to design an application that caters to both extremes, from life-critical domains to more general customer service scenarios?
- Can you elaborate on the concept of "tooling around observability" for these types of use cases? How do existing monitoring tools and ML pipelines help in tracking user queries and improving context relevance? What specific metrics or KPIs should be used to measure the success of a model in generating relevant questions, especially when dealing with varying levels of domain expertise?
- How can we incorporate feedback mechanisms into our system to continuously improve the performance of our models and adapt to changing user queries and contexts?
- What role do you see natural language processing (NLP) and machine learning (ML) playing in developing more effective question-answering systems that can handle diverse domains and use cases?
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