James Levine’s Post

Tired of tedious #LLM annotation processes? Seeking to streamline your machine learning workflow? Try data annotation with MEGAnno, where human expertise meets #LLM capabilities for seamless, high-quality annotations! Many existing data annotation tools focus on the annotator, enabling them to annotate data and manage annotation activities. Instead, MEGAnno is an open-source data annotation tool that puts #ML practitioners first. It enables you to bootstrap annotation tasks and manage the continual evolution of annotations through the machine learning lifecycle. Key Features of MEGAnno: 🔧 Enhanced Data Scientist-Centered Annotation: a tailored experience for data scientists, facilitating direct annotation management within Jupyter notebooks. This feature enables the utilization of existing Python functions and our suite of built-in power tools, optimizing the annotation process. 🔍 Human Verification and Label Exploration: Seamlessly incorporate both human and LLM data labels with verification workflows and integration to popular LLMs. This enables LLM agents to label data first while humans focus on verifying a subset of potentially problematic LLM labels. ℹ Unified Data Management: MEGAnno integrates a robust back-end service acting as a single source of truth, managing the evolution of annotation information throughout the lifecycle. 🧐 Comprehensive Data Exploration and Selection: MEGAnno provides powerful tools to explore datasets and choose the most suitable data for labeling. Incorporating features like active learning, it optimizes the labeling process, ensuring efficient prioritization of tasks. 🔄 Informed Decision-Making Through Label Distribution Analysis: MEGAnno enables users to analyze label distribution and annotator behavior, facilitating informed decisions for subsequent labeling batches. To address the diverse challenges in LLM annotation workflows, MEGAnno prioritizes: ✅ Convenience: Automating annotation workflows, customizable model configurations, and robust error handling. 🔍 Selectivity: Empowering users to selectively verify annotation candidates based on confidence scores and metadata. 🔄 Reuse: Facilitating the storage and reuse of LLM agents, labels, and metadata for future projects. Try Out Our Demo or use MEGAnno locally! Visit https://megagon.ai/meganno for more info and documentation. #AI #MachineLearning #DataAnnotation #phd #datascience #MEGAnno #LLM #LLMs

We're thrilled to announce the launch of MEGAnno, an open-source data annotation tool designed with the needs of ML practitioners firmly in mind. Unlike traditional annotation tools that view data labeling as a standalone step, MEGAnno supports a broader, iterative ML workflow, including data exploration and model development, and can be seamlessly integrated within a Jupyter Notebook. Try MEGAnno Annotation 👉 https://lnkd.in/gcr6peXG Key Features of MEGAnno: 💥 Bootstrap Annotation Tasks: Jumpstart your projects with ease. 💥 Sophisticated Data Exploration: Programmatically search and utilize automated suggestion functions to navigate your data efficiently. 💥 Dynamic Labeling Schema Updates: Adapt your labeling schema as your project grows and evolves. 💥 Centralized Annotation Management: A back-end service acts as your project's single source of truth, managing the progression of your annotation workflows. 💥 Organized Label Management: Effortlessly manage, sort, and review labels to streamline your processes and reduce the time spent on data annotation. 💥 Integration with LLMs: Leverage the power of Large Language Models (LLMs) for generating and verifying data labels, blending the best of human and AI capabilities. MEGAnno fits perfectly into existing ML development environments, including Jupyter Notebooks, making it an indispensable tool for your toolkit. 🛠️📊 Explore MEGAnno and enhance your machine learning projects. Together, we can streamline your data annotation process for better efficiency and effectiveness. Try MEGAnno: https://lnkd.in/gzNCbkpC #AI #DataScience #Annotation #LLM #MachineLearning #data

AI Workflow Tools: Weka (Free) vs. Dataiku (Paid) 🖥️ Weka (Free): A collection of machine learning algorithms for data mining tasks, with a GUI for easy use. Pros: Free, user-friendly GUI, extensive algorithms. Cons: Limited scalability, less suited for large datasets. Dataiku (Paid): An advanced data science and machine learning platform that enables enterprises to build and deploy AI solutions at scale. Pros: Scalable, collaborative environment, strong support. Cons: Expensive, requires setup. #AIWorkflow #MachineLearning #FreeVsPaid

DataWalk’s unique knowledge graph grounds LLMs!! DataWalk’s graph AI platform unifies all the unstructured and structured data from across your organization’s many disparate systems and makes it available in a clean, normalized knowledge graph. The DataWalk knowledge graph provides a unique combination of remarkable flexibility to connect new data, exceptional performance for graph algorithms, and effective handling of vast amounts of data, all without coding.

[New on our blog] Building High-Performing Computer Vision Models with Encord and neptune.ai by Stephen Oladele TL;DR → Iteratively improving data quality and conducting experiments are vital in developing computer vision models. → Encord is a data-centric platform that enables teams to curate visual datasets to improve data and model quality → neptune.ai is the MLOps stack component for experiment tracking that provides a central place to log, analyze, and compare ML experiments. → Together, Encord and neptune.ai cover the entire computer vision modeling process from data curation to delivering the final model to production. — (link to the full article in the comments) #ml #machinelearning #mlops

Being able to quantify the value of AI assistants is still mostly a manual estimation exercise. Coding tools are a little easier when they have built in usage metrics. But outcome impact is difficult to articulate what change as a direct result of AI only.

Eric Lagally and Jared N. Plumb recently engaged with a great crowd at PyCon to discuss how AI is transforming the software development and data analytics education landscape. It was a fascinating discussion filled with valuable insights into the impact of AI on the industry. Thank you, Eric and Jared, for sharing your expertise with us! #AI #softwaredevelopment #dataanalytics #PyCon

🚀 Exciting news! We’re thrilled to unveil our new LLMs in Production database – a groundbreaking resource featuring 300+ real-world LLM implementation case studies! Over the past few weeks, our team has been digging deep into technical insights, architectural strategies, and production lessons shared by organizations actively using LLMs. This isn’t your typical hype – it’s a practical, no-nonsense guide from the frontlines of AI deployment. Here’s what’s inside: - Architectural blueprints: From scrappy startups to established enterprises. - In-depth implementations: Tailored for various industries. - Lessons learned: Honest accounts of successes and failures. - Actionable solutions: Cost-saving tactics, monitoring methods, scaling tips, and more. We created this because, while LLM demos are everywhere, actionable implementation knowledge is rare. Each case study is distilled (with a little help from Claude!) to focus on critical details – deployment strategies, engineering trade-offs, and technical decision-making. The best part? The database is searchable, filterable, and free – built for the community, by the community. Explore by industry, tech stack, or unique patterns, and even contribute your insights! Check it out: https://lnkd.in/gYcq6Qtp Got your own case study? Share it here: https://lnkd.in/gmeX8Dbm A huge shoutout to Alex S. for leading this project and to Evidently AI’s MLOps collection for inspiring us to dive deeper into the LLM production ecosystem. #LLMOps #MachineLearning #Engineering #GenerativeAI #AI 📌 P.S. Stay tuned! We’ve already started publishing a series on the patterns and insights uncovered during this journey. Two articles are live now – more to come!

𝗧𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗠𝗟 𝗧𝗼𝗼𝗹𝘀 𝗗𝗼𝗲𝘀𝗻'𝘁 𝗪𝗼𝗿𝗸 𝗳𝗼𝗿 𝗚𝗲𝗻𝗔𝗜 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 Recently, I’ve been reflecting on whether building GenAI applications differs significantly from traditional ML applications. Building LLM-based applications feels both novel and nostalgic. This made me wonder: do we really need a new set of GenAI tools, or can we rely on the existing tools designed for traditional ML? After some thought, I’m leaning towards the idea that we do need a new set of tools. This is mainly due to the introduction of new concepts, RAG and prompt engineering, which heavily influence the importance of the tools we use. For example, while VectorDB has always existed, it now plays a crucial role in the development of GenAI applications. Full thoughts here: https://lnkd.in/gVpj-DZD #genai #ml #llm #mlops