Beyond Tech Hype: A Practical Guide to Harnessing LLMs for Positive Change

In this contributed article, Dr. Ivan Yamshchikov who leads the Data Advocates team at Toloka believes that whether it’s breaking down language silos, aiding education in underserved regions, or facilitating cross-cultural communication, LLMs have altered the way we interact with information –enhance human well-being by improving healthcare, education, and social services.

Video Highlights: Open-Source LLM Libraries and Techniques — with Dr. Sebastian Raschka

In this video presentation, our good friend Jon Krohn, Co-Founder and Chief Data Scientist at the machine learning company Nebula, sits down with industry luminary Sebastian Raschka to discuss his latest book, Machine Learning Q and AI, the open-source libraries developed by Lightning AI, how to exploit the greatest opportunities for LLM development, and what’s on the horizon for LLMs.

The Essential Role of Clean Data in Unleashing the Power of AI 

In this contributed article, Stephanie Wong, Director of Data and Technology Consulting at DataGPT, highlights how in the fast-paced world of business, the pursuit of immediate growth can often overshadow the essential task of maintaining clean, consolidated data sets. With AI technology, the importance of data hygiene becomes even more apparent, as language models heavily rely on it.

Kinetica Delivers Real-Time Vector Similarity Search

Kinetica, the real-time GPU-accelerated database for analytics and generative AI, unveiled at NVIDIA GTC its real-time vector similarity search engine that can ingest vector embeddings 5X faster than the previous market leader, based on the popular VectorDBBench benchmark.

The Five Step Playbook to Move GenAI into Production

In this contributed article, Josh Reini, Developer Relations Data Scientist, TruEra, discusses how gaining the required confidence to deploy GenAI apps at scale can be challenging, and structured evaluation has gained recognition as a key requirement on the path from science experiment to customer value. Evaluation frameworks can play a critical role in this journey by allowing developers to run experiments faster and gain systematic validation for production readiness. Connecting such an evaluation framework with a scaled observability platform brings confidence in production. This article explores five practical steps to move LLM applications from early prototypes to scaled, production applications.

Fine-Tune Your LLMs or Face AI Failure

In this contributed artticle, Dr. Muddu Sudhakar, CEO and Co-founder of Aisera, focuses on the downsides of general-purpose Gen AI platforms and why enterprises can derive more value from a fine-tuned model approach.

Video Highlights: The 3 Steps of LLM Training with Lisa Cohen

In this video presentation, our good friend Jon Krohn, Co-Founder and Chief Data Scientist at the machine learning company Nebula, is joined by Lisa Cohen, Google’s Director of Data Science and Engineering, to discuss the capabilities of the cutting-edge Gemini Ultra LLM and how it stands toe-to-toe with GPT-4.

Overcoming the Technical and Design Hurdles for Proactive AI Systems

In this contributed article, George Davis, founder and CEO of Frame AI, howlights how we find ourselves at an early, crucial stage in the AI R&D lifecycle. Excitement over AI’s potential is dragging it into commercial development well before reliable engineering practices have been established. Architectural patterns like RAG are essential in moving from theoretical models to deployable solutions.

Video Highlights: A Code-Specialized LLM Will Realize AGI — with Jason Warner

In this video presentation, our good friend Jon Krohn, Co-Founder and Chief Data Scientist at the machine learning company Nebula, is joined by poolside co-founder and CEO Jason Warner who sheds light on how code-specialized LLMs could vastly outperform generalized counterparts like GPT-4.

Securing GenAI in the Enterprise

Opaque Systems released a new whitepaper titled “Securing GenAI in the Enterprise.” Enterprises are chomping at the bit to use GenAI to their benefit but they are stuck. Data privacy is the number one factor that stalls GenAI initiatives. Concerns about data leaks, malicious use, and ever-changing regulations loom over the exciting world of Generative AI (GenAI), specifically large language models (LLMs).