Video: the LLM 2.0 Revolution
What if you could build a secure, scalable RAG+LLM system – no GPU, no latency, no hallucinations? In this session, Vincent Granville shares how to engineer high-performance, agentic multi-LLMs from scratch using Python. Learn how to rethink everything from token chunking to sub-LLM selection to create AI systems that are explainable, efficient, and designed for […]
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Stay Ahead of AI Risks – Free Live Session for Tech Leaders
Exclusive working session about trustworthy AI, for senior tech leaders. View PowerPoint presentation, here. AI isn’t slowing down, but poorly planned AI adoption will slow you down. Hallucinations, security risks, bloated compute costs, and “black box” outputs are already tripping up top teams, burning budgets, and eroding trust. That’s why this session blends three things you […]
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Scaling, Optimization & Cost Reduction for LLM/RAG & Enterprise AI
Live session with Vincent Granville, Chief AI Architect and Co-founder at BondingAI. Scaling databases is a tricky balance. Teams need speed and reliability, but costs keep rising. From runaway infrastructure bills to overprovisioned clusters and slow queries, companies often spend more without seeing better performance. Join for a practical session on how to reduce database […]
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Podcast: Creating Custom LLMs from Scratch
Despite GPT, Claude, Gemini, LLama and the other host of LLMs that we have access to, a variety of organizations are still exploring their options when it comes to custom LLMs. Logging in to ChatGPT is easy enough, and so is creating a ‘custom’ openAI GPT, but what does it take to create a truly […]
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NoGAN: Ultrafast Data Synthesizer – My Talk at ODSC San Francisco
My talk at the ODSC Conference, San Francisco, October 2023. Includes Notebook demonstration, using our open-source Python libraries. View or download the PowerPoint presentation, here. I discuss NoGAN, an alternative to standard tabular data synthetization. It runs 1000x faster than GAN, consistently delivering better results according to the most sophisticated evaluation metric, implemented here for […]
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NoGAN: New Generation of Synthetic Data (Video)
My talk at the Generative AI Conference, London, September 2023. View or download the PowerPoint presentation, here. I introduce a new, NoGAN alternative to standard tabular data synthetization. It is designed to run faster by several orders of magnitude, compared to training generative adversarial networks (GAN). In addition, the quality of the generated data is […]
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Podcast: Synthetic Data and Generative AI – Importance, Misconceptions and Applications
Fireside chat: Synthetic data – importance, misconceptions and applications In this video, Vincent talks about how synthetic data can be leveraged across various industries to enhance predictions and test blackbox systems leading to more fairness and transparency in AI. Hosted by Victor Chima, co-founder at Learncrunch.com. Topics discussed include: To learn more about synthetic data […]
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Video: Introduction to Machine Learning
This 30 minutes video features my interview about the upcoming course “Intuitive Machine Learning and Explainable AI”, based on my new book with the same title. The course is described here, and the book is available here. Course participants get a free copy of the book. Both the course and the book include a solid […]
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Explainable AI, Blackboxes and Synthetic Data
In this 45 minutes podcast hosted by Ben Cole, Executive Editor at TechTarget, Vincent Granville, founder of MLTechniques.com and Executive ML Scientist, discusses the following topics: This podcast comes with two attachments (videos) that further illustrate the problems discussed: In this Q&A, Vincent offers solutions to problems such as creating rich and meaningful synthetic data, […]
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Synthetic Data in Machine Learning: What, Why, How?
In this episode, Nicolai Baldin (CEO) and Simon Swan (Machine Learning Lead) of Synthesized are welcoming the founder of Data Science Central and MLTechniques.com Vincent Granville to discuss synthetic data generation, share secrets about Machine Learning on synthetic data, key challenges with synthetic data, and using generative models to solve issues related to fairness and […]
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