Mihir Shah

Using generative AI, NVIDIA operations built an AI Planner agent, developed on NVIDIA Inference Microservices (NIM). The agent leverages LLM, NeMo Retriever and CuOpt NIM to reduce re-planning time from hours to just seconds. https://nvda.ws/4bIfS2T

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Thrilled to share that kipi.bi has partnered with Dataiku to enhance our data science, artificial intelligence, and machine learning solutions on Snowflake! Tapping into the combined platform and services of Snowflake and Dataiku, we’re empowering organizations to extract actionable insights from their data like never before. Discover more about this strategic partnership here: #DataPartnership #Analytics #Dataiku #Snowflake

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The Analytics India Magazine has a piece on an Indian startup "building a private Perplexity for enterprise" https://lnkd.in/ec-_xt8x. #generativeai #perplexity #startup #artificialingelligence #startup #perplexity #analyticsindiamagazine

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I am speaking at #DES2024. If you guys are interested please attend my session. It would be about how we can use the Embeddings for creating semantic search, custom #RAGs and how do we store them in #VectorDbs for faster Retrieval. I will be giving some practical examples of how we are using them in our products at #Bizom.

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The hunt for a pool of data scientists might be holding your digital transformation back. A new breed of leader is emerging: the AI Enabler. Here’s what you need to know about building your own class of AI Enablers: http://spklr.io/6040qSon #AI #GenAI #DigitalTransformation

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A product from an idea - text to diagrams. We have done this in #capgemini internally and I had posted about it (https://lnkd.in/gySaSK-r) https://www.eraser.io/ai #llms #genai #generatieveai #capgemini #llm #opensourceai #capgeminiindia #ai #artificialintelligence #software #architecture

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Navigating the journey from pilot projects to scalable production in #GenAI is filled with challenges and will require a strategic and disciplined approach. A rough estimate is that only 10% of the pilot projects scale in large enterprises. Focus needs to be on business value, establishing a standard GenAI #platform, change management, and cost management. A recent #McKinsey article talks about steps required to scale. I appreciate the article's emphasis on the need for a GenAI platform that integrates within the enterprise tech stack. It's not just about #LLMs. More focus is needed on such architecture and evolving patterns. The tech stack illustration effectively highlights how various capabilities – like orchestration, semantic search, data extraction, guardrails, and prompt engineering - come together to create a robust GenAI solution. The future expansion of the agentic workflow will further enhance this architecture. Link to the article in comments.

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It was a good conversation with Priya Dialani for the Analytics Insight® podcast, which delved into the complexities of #Crypto and #stablecoin adoption and its correlation with the #regulatory landscape in #India. Link - https://lnkd.in/g-SqhE3r #Regulation #cryptocurrencytrading #cryptonews #cryptoadoption

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This is a must read Forbes article by Gary Drenik which highlights on Data Quality For Good AI Outcomes. The piece talks about common data quality challenges faced by enterprises and features insights from Srinivas Atreya, Consulting Data Scientist at Cigniti Technologies. https://lnkd.in/gQ5QZwJY #ai #dataquality

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Recently had the pleasure of interviewing Kai Waehner, Confluent’s Global Field CTO for Dataquest at the Kafka Summit Bangalore. We discussed how Confluent is pushing the boundaries of streaming analytics and data management. Our conversation highlighted Confluent's commitment to robust data flow, seamless collaboration, and enhanced security. Exciting times ahead for real-time data! #DataAnalytics #Streaming #KafkaSummit #Confluent #datastreaming #kafka #ApacheFlink

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Wanted to share my husband Arjun Srinivasan's efforts in democratizing data science and AI for students and early career professionals through this masterclass. Key learnings include: - Foundational building blocks of data science - Real world applications of data science and measuring ROI - Future landscape of data science, AI and Machine Learning https://lnkd.in/gFxf55HS

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As we explore #generativeAI and #largelanguagemodels , key patterns are surfacing in the development of applications and their deployment to #production. Check out our recent article shedding light on these emerging trends. Reshma Godse Persistent Systems #seebeyondriseabove https://lnkd.in/gUxqmKwD

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Data: The Lifeblood Of AI. #AI Needs Data More Than Data Needs AI #ItheumV2 https://lnkd.in/gFXFCvNC

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🤖 GenAI transformed the way AI Engineering was evolving. The key change is that there is no model-building process for most use cases like Machine Learning Engineering. 💻 Model building process is replaced by identifying the right technology vendor and partners. 👩🏽‍💼 The cost of hiring the best AI talent is shifter to upskill your Engineering with GenAI API and frameworks. 🎯 The missing part between security and data is that RAG systems show all the weaknesses of a minimal secure data system. What we need is SRAG or Secure RAG for GenAI. #genai #ai #aiml #generativeai #llm https://lnkd.in/dqUk4V5j

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Super pumped to see such intense competition in Gartner's MQ for the Data Science & ML platforms. There has never been a better time to be a data scientist or AI engineer. The barriers to entry have been considerably lowered with AutoML, GenAI code copilots, one-click deployments, end2end evals, and simplified MLOps. Below I tried to show the progress vendors have made since the last Jan 2021 Gartner MQ, pre-GenAI days. 𝗚𝗼𝗼𝗴𝗹𝗲:  𝗔𝗵𝗮! - The only major cloud vendor that makes it's own frontier LLMs - Gemini. They invest heavily in Gemini to protect their other mega businesses. Great job balancing both GenAI and AI/ML. Our data scientists love the remarkable 500+ updates to Vertex AI in 2023. 𝗨𝗵 𝗢𝗵! - Data governance & lineafge is fragmented across the ML pipeline. 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁:  𝗔𝗵𝗮! - Great range of frontier models from OpenAI and open-source small models (eg. their Phi3) alongside AI ML. Azure AI Studio + Azure ML, gives our team full flexibility of AutoML, evals etc. Investments in massive compute for OpenAI trickles down to developers. 𝗨𝗵-𝗢𝗵! - Notoriuous for going all in and then sunsetting products. Need to understand the total cost of all licenses etc, particularly co-pilots. 𝗔𝗪𝗦:  𝗔𝗵𝗮! - Ton of options across the AI lifecycle. Anthropic was a great bet! Our AI team loves full control, super scalable components, security, lots of integrations, modular services. AWS has invested a lot in AI infrastructure eg. SageMaker HyperPod for efficient LLM training.  𝗨𝗵-𝗢𝗵! - Bedrock is still missing some features like Azure's Prompt Flow. Options get overwhelming for beginners. Titan LLMs are closed. 𝗗𝗮𝘁𝗮𝗯𝗿𝗶𝗰𝗸𝘀:  𝗔𝗵𝗮! - A very unifed data-first approach to simplifying Data & ML workflows. Nailed data governance & lineage for ML training. MossaicML was a brilliant acquisition. 𝗨𝗵-𝗢𝗵! - Product upgrades are a bit messy. Need to optimize model deployment flexibility and tuning LLMs w/ enterprise data. 𝗗𝗮𝘁𝗮𝗶𝗸𝘂:  𝗔𝗵𝗮! - Great technical teams & business collaboration. Visual flow compliments pro-code. Great with large datasets and complex projects.  𝗨𝗵-𝗢𝗵! - The user community is not big enough. On-prem deployment requires a lot. Gets expensive quickly. 𝗜𝗕𝗠:  𝗔𝗵𝗮! - Ideal for enterprises with hybrid clouds (on-prem, private, public). Has focused heavily on openess (watsonx.ai) and responsible AI governance (watsonx.gov). Granite LLMs are ranked high by Forrester, are fully open-sourced and very transparent with the training data. InstructLab helps enterprises collaboratively train LLMs with priorietary client data.  𝗨𝗵-𝗢𝗵! - Need more pre-built integrations with services provided by client's cloud vendor. Need an integrated LLM app builder. What are your thoughts on these vendors? Any surprising omissions or inclusions? #AI #MachineLearning #DataScience #Gartner -- 🔔 Pls connect, comment, repost ♻️ Subscribe to my free ‘AI with Whiskey’ newsletter https://lnkd.in/gK4zrSYv

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In our industry, there are data "so-called experts" and hands-on data folks who solve complex issues and bring insights quickly. Naveen belongs to the latter group. When he pitched the idea of ChatWithCensus leveraging LLM-based technology, I immediately decided to join forces. After months of hard work, debates, and perseverance, ChatWithCensus is live. So, what does it do? ChatWithCensus simplifies engagement with ABS data. Users can ask questions in plain language, and our custom-built NLU engine converts these queries into SQL for rapid data retrieval.  Because it is simple and doesn't require complex loading or SQL coding, we hope this becomes the go-to tool for smaller research organisations. A huge thank you to those who made ChatWithCensus possible: - Naveen Y., my partner in crime, Thanks, mate! - Pere Martra, thank you for stepping in when we faced our first hurdle. - Stephen Johansen your mentorship and generosity are invaluable. - Belal Hossain, we wouldn't be here if it weren't for your late-night coding. - Asher Hussain Thank you for SSL magic! Again, thank you! - Prof. Jing and PhD student Haoyang Li, for their insights. Finally, I would appreciate it if you try it out and share your feedback: https://chatwithcensus.ai/

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Wow, what an incredible experience! While AI mega-conferences are everywhere, it was refreshing to join Nicole Kaufman, Tisson Mathew, Matt Marshall, and many other AI practitioners for an intimate discussion on real-world AI adoption. Many are delivering on board-level mandates to invest their firm's money and, more importantly, their teams' time in AI. It is also noteworthy how consistently conversations focus on return on investment--gone are the days when AI was purely an R&D effort!  However, those leading AI transformations must address a long list of challenges before an attractive return on investment can be realized. One that I have yet to hear discussed much is the behavioral design challenge à la IDEO frog etc. Often, teams need to adapt how they work before the full potential of your AI-powered system can be realized.   Marketing campaigns, for example, are not launched the instant that copy is created! If you want eye-popping marketing efficiency gains, your AI-powered system must be intentionally designed to help your teams adapt how they work. The good news is that your teams can do the behavioral design work while they address the data, technology, and governance work. In fact, it ought to be done before much of the data, technology, and governance work is undertaken. Thanks to the VentureBeat team for pulling together a great group for this leg of the AI Impact Tour. Thanks to Edge & Node for luring us all to the Presidio on a fantastic, sunny day. #ModernStrategy #EnterpriseAI #AI #DataCentric #Design #FutureTrends

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Knowledge Graphs (KGs) are positioned at the center of Gartner's 2024 Impact Radar as a critical enabler for emerging technologies. They enhance data discovery, classification, and organization, improving the effectiveness of GenAI tools like Large Language Models (LLMs). KGs help enterprises connect and contextualize vast amounts of data, enabling more accurate insights and decision-making processes. This technology is particularly valuable for improving AI accuracy, supporting knowledge-intensive workflows, and increasing the explainability of AI models. Bottom Line is: You can't do AI without an ingestion engine, AKA the Knowledge Graph. KGs prime your data, ensuring it is clean, deduped and accurate. This is the necessary precursor to employing any ML modeling or creating any algorithms designed to generate useful outputs and actionable insights from your data. #knowledgegraphs #graphdatabases #AI #ML #LLMs #dataunification #EquitusAI #EquitusEnterprise

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