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# ZenML
# Flyte
# Metaflow

ZenML VS Flyte VS Metaflow

This blog compares three popular machine learning workflow orchestration tools: ZenML, Flyte, and Metaflow. It explores their features, use cases, and strengths, helping data scientists and engineers choose the best option for building and managing efficient ML pipelines.
Ankur Tyagi
Ankur Tyagi ยท Jan 15th, 2025
Popular topics
# LLMs
# RAG
# Generative AI
# AI Agents
# AI
# LatticeFlow
# Evaluations
# Machine learning
# SAS
# Vector Search
# Synthetic Data
# AWS
# LlamaIndex
# In-Stealth
# Prosus
# AI Models
# LLM Evaluation
# Hybrid reranking
# GenAI
# Intuit
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Steven Jieli Wu
Steven Jieli Wu ยท Jan 8th, 2025
This blog explores Retrieval-Augmented Generation (RAG), a technique that enhances large language models (LLMs) by incorporating external data to provide more accurate, up-to-date, and context-specific responses. The author, Steven Wu, shares insights from a year of building RAG-based applications and explains the concept in a simple analogy for beginners.
# RAG
# LLMs
# AI
Gleb Lukicov
Gleb Lukicov ยท Jan 8th, 2025
In this blog post, Dr Gleb Lukicov, discusses the setup for fast local development and scalable remote deployment of ML projects using pipelines, particularly when working with LLMs. Gleb shares how to implement local testing using Kubeflow Pipelines to shorten the development cycle using Docker cache, multi-stage builds, uv and dynamic user credentials injection. The full end-to-end implementation is available as a self-contained repository, a companion to this blog post, that also includes some infrastructure goodies like GitHub CI/CD & pre-commit config for linting & testing, local scripts with typer, project dependency management with uv, and static checking with mypy.
# LLMs
# ML Pipelines
# Cloud Experiments
Christina Garcia
Christina Garcia ยท Jan 7th, 2025
Part 1 of YouGot.usโ€™ AI Agent study, conducted during the AI Agent in Production Event by Prosus and MLOps.Community, covers the definition of AI Agents, users, and use cases, as well as agent adoption.
# AI Agents
# Generative AI
# YouGot.us
Sophia Rowland
Sophia Rowland ยท Dec 10th, 2024
This article highlights SAS Viya as an all-in-one solution for MLOps, offering tools for version control, model registry, orchestration, monitoring, and deployment. It emphasizes features like experiment tracking, compute resource management, and responsible AI practices, aiming to simplify workflows while ensuring transparency and human oversight.
# SAS Viya
# MLOps
# SAS
Brij Mohan Singh
Travis Thompson
Ritwika Chowdhury
Brij Mohan Singh, Travis Thompson & Ritwika Chowdhury ยท Dec 10th, 2024
This blog underscores the need for robust governance frameworks to balance the efficiency of autonomous AI agents with their associated risks. To mitigate risks and enable safe deployment across domains, a modular and transparent approach, like the use of DDPs, is recommended.
# Governance for AI Agents
# Data Developer Platforms
# DDP
Demetrios Brinkmann
Demetrios Brinkmann ยท Nov 21st, 2024
Token prices are falling, but the cost of meaningful answers is rising due to increased system complexity. Advanced tasks now require multiple LLM calls for reasoning, planning, and refinement, driving up operational costs. We do not live in the one LLM call world anymore.
# Token prices
# LLM
# ROI
COMPL-AI, developed by LatticeFlow AI in collaboration with ETH Zurich and INSAIT, offers an open-source framework for evaluating generative AI modelsโ€™ compliance with regulatory standards like the EU AI Act. By translating high-level regulatory requirements into measurable technical standards, COMPL-AI enables AI developers to benchmark large language models (LLMs) for compliance in areas such as safety, transparency, and ethical considerations. This compliance-centered tool supports integration with HuggingFace models and provides detailed reports, making it accessible for stakeholders to ensure model accountability and address societal impacts effectively.
# LLM
# COMPL-AI
# LatticeFlow
Effective collaboration is crucial for building scalable ML and AI solutions in a rapidly evolving data engineering landscape. YouGot.us, in collaboration with MLOps.community, conducted a survey of over 200 participants in September 2024, revealing key challenges and practices in ML and data pipeline development.
# Effective collaboration
# Survey
# MLOps Community
# You.com
Sergio Ferragut
Sergio Ferragut ยท Oct 28th, 2024
Tecton introduced new GenAI capabilities (in private preview) in the 1.0 release of its SDK that makes it much easier to productionize RAG applications. This post shows how the SDK can enable AI teams to use the SDK to build hyper-personalized chatbots via prompt enrichment and automated knowledge assembly.
# LLMs
# Real-Time
# Tecton
Vishakha Gupta
Vishakha Gupta ยท Sep 10th, 2024
The ability to semantically search for a concept, summarize a response, and point to relevant links is exactly why large language model (LLM) and retrieval augmented generation (RAG) methods have become so popular. Our LangChain-based implementation uses ApertureDB under the covers as the vector store/retriever for high-performance look-up of documents that are semantically similar to the userโ€™s query. Now we can look at the questions that resulted in insufficient or incorrect responses and introduce helpful and accurate information where it belongs. Ultimately, if we can help our users find guidance easily, then it's a win for everyone.
# Vector Database
# RAG
# Usability
# ApertureDB
Popular
Extending AI: From Industry to Innovation
Sophia Rowland, David Weik & Demetrios Brinkmann