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The “data-scientist” job posting is quietly disappearing from corporate career pages. In its place, you’ll find a title that didn’t exist three years ago: GenAI Data Scientist. A search on LinkedIn Jobs as of today returns around 18,000 open roles that explicitly demand “LLM fine-tuning”, “prompt evaluation”, or “synthetic data generation”, as one of the skills for the position. AI-related job postings have grown at an average annual rate of nearly 29% over the last 15 years, which outpaces the 11% annual growth rate of job postings in the general economy. The message is blunt: employers still need people who…

I’ve spent the last eight years working with AI, learning the ins and outs of building and applying AI solutions in business. After making countless mistakes, I created my own method for building and applying the technology. That was fine and dandy until the fall of 2022, when ChatGPT was released and gave a sudden rise in the usefulness and adoption of generative AI. For my consulting business TodAI, that meant a lot of new projects involving generative AI and a lot of learning. After several projects, I’ve identified places where generative models are clearly distinct from other AI when…

Framework switching is expensive. It means rewriting code, retraining teams, and explaining decisions to stakeholders. Yet our recent interviews revealed that multiple development teams have made exactly this move – abandoning established tools like LangChain and Microsoft Semantic Kernel for Google’s relatively new Agent Development Kit. What’s driving these switches? We conducted over a dozen in-depth interviews with developers and engineering teams who have hands-on experience with ADK: from solo developers building personal projects to enterprise teams selecting frameworks for client deployments. We’ve distilled their experiences into eight key factors that consistently emerged across our conversations. Read on to see…

Image by Author | ChatGPT   Machine learning has powerful applications across various domains, but effectively deploying machine learning models in real-world scenarios often necessitates the use of a web framework. Django, a high-level web framework for Python, is particularly popular for creating scalable and secure web applications. When paired with libraries like scikit-learn, Django enables developers to serve machine learning model inference via APIs and also lets you build intuitive web interfaces for user interaction with these models. In this tutorial, you will learn how to build a simple Django application that serves predictions from a machine learning model.…

LLMs like those from Google and OpenAI have shown incredible abilities. But their power comes at a cost. These massive models are slow, expensive to run, and difficult to deploy on everyday devices. This is where LLM compression techniques come in. These methods shrink models, making them faster and more accessible without a major loss in performance. This guide explores four key techniques: model quantization, model pruning methods, knowledge distillation in LLMs, and Low-Rank Adaptation (LoRA), complete with hands-on code examples. Why Do We Need LLM Compression? Before diving into the “how,” let’s understand the “why.” Compressing LLMs offers clear…