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LLMs Differentiation Problem Adding to this structural challenge is a concerning trend: the rapid convergence of large language model (LLM) capabilities. In other words, while every new LLM boasts impressive performance based on standard benchmarks, a truly significant shift in the underlying model architecture is not taking place.
Once the model exceeds 7 billion parameters, it is generally referred to as a large language model (LLM). The core “skill” (if you might call it that) of an LLM is its ability to predict the most likely next word in an incomplete block of text. But ChatGPT is not the only LLM out there.
Engineers need to build and orchestrate the data pipelines, juggle the different processing needs for each data source, manage the compute infrastructure, build reliable serving infrastructure for inference, and more. Together, Tecton and SageMaker abstract away the engineering needed for production, real-time AI applications.
Adaptive RAG Systems with Knowledge Graphs: Building Smarter LLM Pipelines David vonThenen, Senior AI/ML Engineer at DigitalOcean Unlock the full potential of Retrieval-Augmented Generation by embedding adaptive reasoning with knowledge graphs.
The First Python Course Designed for AIDevelopment from scratch! This week, we are excited to announce our most requested course, Python Primer for Generative AI designed to help you learn Python specifically for LLMs, how an AIengineer would. Then feel free to jump into our advanced LLM program!
Autonomous AI agents arent just an emerging research areatheyre rapidly becoming foundational in modern AIdevelopment. At ODSC East 2025 from May 13th to 15th in Boston, a full track of sessions is dedicated to helping data scientists, engineers, and business leaders build a deeper understanding of agentic AI.
Good morning, AI enthusiasts! Ever since we launched our From Beginner to Advanced LLMDeveloper course, many of you have asked for a solid Python foundation to get started. Im excited to introduce Python Primer for Generative AI a course designed to help you learn Python the way an AIengineer would.
This problem often stems from inadequate user value, underwhelming performance, and an absence of robust best practices for building and deploying LLM tools as part of the AIdevelopment lifecycle. LLMs, while accelerating some processes, introduce complexities that require new tools and methodologies.
AI systems like LaMDA and GPT-3 excel at generating human-quality text, accomplishing specific tasks, translating languages as needed, and creating different kinds of creative content. On a smaller scale, some organizations are reallocating gen AI budgets towards headcount savings, particularly in customer service.
Developed by a collaborative effort of researchers, MiniChain stands out as a beacon of simplicity amidst the intricate frameworks prevalent in this domain. With a modest footprint, this library encapsulates the essence of prompt chaining, allowing developers to weave complicated chains of LLM interactions effortlessly.
Generative AI — in the form of large language model (LLM) applications like ChatGPT, image generators such as Stable Diffusion and Adobe Firefly, and game rendering techniques like NVIDIA DLSS 3 Frame Generation — is rapidly ushering in a new era of computing for productivity, content creation, gaming and more.
The AI agent classified and summarized GenAI-related content from Reddit, using a structured pipeline with utility functions for API interactions, web scraping, and LLM-based reasoning. The session emphasized the accessibility of AIdevelopment and the increasing efficiency of AI-assisted software engineering.
Time is running out to get your pass to the can’t-miss technical AI conference of the year. Our incredible lineup of speakers includes world-class experts in AIengineering, AI for robotics, LLMs, machine learning, and much more. Register here before we sell out!
Offering unlimited image generation, the AI is designed to work with more than 20 of the world’s most popular AIengines. One of those AIengines — also known as Large Language Models — is its own Ninja-LLM 3.0, which is built on AIdeveloped by Facebook parent Meta.
Evals for Supercharging Your AIAgents Aditya Palnitkar | Staff Software Engineer |Meta Testing and monitoring LLMs are often overlookedbut theyre critical to improving performance and development speed. Walk away with practical tools, curated Jupyter notebooks, and a roadmap for building robust LLM evaluation pipelines.
Large Language Models & RAG TrackMaster LLMs & Retrieval-Augmented Generation Large language models (LLMs) and retrieval-augmented generation (RAG) have become foundational to AIdevelopment. Whats Next in AI TrackExplore the Cutting-Edge Stay ahead of the curve with insights into the future of AI.
Hilpisch, CEO | The Python Quants & The AI Machine | Adjunct Professor of Computational Finance Reinforcement learning and related algorithms, such as Deep Q-Learning (DQL), have led to major breakthroughs in different fields. Behind a chat interface, they can chat with users almost like a real human would.
Implement specialized RAG pipelines and multi-agent systems using LangGraph to create adaptive AI solutions capable of reasoning across diversetasks. These workshops equip developers, data scientists, and AIengineers with actionable insights to scale effectively, cut costs, and maintain system integrity.
LangChain Many AIengineers have a love-hate relationship with the open-source framework LangChain , and I know several who have ripped it out of their product, only to later decide to put it back in. This might sound like table stakes, but you’d be surprised how often this is overlooked in the AIdevelopment space.
This funding milestone, which brings the companys total funding to $14 million, coincides with the launch of its flagship tool, Experiments an industry-first solution designed to make large language model (LLM) testing more accessible, collaborative, and efficient across organizations. Gentrace makes LLM evaluation a collaborative process.
Youll also be entered for a chance to win awesome prizes, including tickets to ODSC East 2025 or the month-long AI BuildersSummit. Building an AI Financial Analyst with Multi-Agent Systems Heres how to construct your own AI financial analyst dream team by using multi-agent systems, and how to ensure its working correctly.
Across 5 hands-on sessions, attendees learned about fine-tuning SLMs, mixture of memory experts, and LLM model selection. AIDevelopment Lifecycle Learnings of What Changed withLLMs These are the key lessons and best practices for creating successful, high-performing LLM-based solutions as part of the AIdevelopment lifecycle.
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