The article discusses the increasing complexity of Kubernetes and suggests that Silicon Valley is exploring alternative technologies for container orchestration, citing a benchmark showing a stripped-down stack outperforming Kubernetes.
An article discussing the role of data orchestrators in managing complex data workflows, their evolution, and various tools available for orchestration.
Portkey AI Gateway allows application developers to easily integrate generative AI models, seamlessly switch among models, and add features like conditional routing without changing application code.
Run:ai offers a platform to accelerate AI development, optimize GPU utilization, and manage AI workloads. It is designed for GPUs, offers CLI & GUI interfaces, and supports various AI tools & frameworks.
Apache Airflow's latest update, version 2.10, introduces hybrid execution and enhanced data lineage for more efficient and trustworthy data orchestration, especially for AI workloads.
The future of iOS apps might be services that just tie into Apple Intelligence, with little to no interface of their own.
This article explains Retrieval Augmented Generation (RAG), a method to reduce the risk of hallucinations in Large Language Models (LLMs) by limiting the context in which they generate answers. RAG is demonstrated using txtai, an open-source embeddings database for semantic search, LLM orchestration, and language model workflows.
This article provides an introduction to Mlflow, an open-source platform for end-to-end machine learning lifecycle management. The article focuses on using MLflow as an orchestrator for machine learning pipelines, explaining the importance of managing complex pipelines in machine learning projects.
Ragna is an open source RAG orchestration framework.
With an intuitive API for quick experimentation and built-in tools for creating production-ready application, you can quickly leverage Large Language Models (LLMs) for your work.
With an intuitive API for quick experimentation and built-in tools for creating production-ready application, you can quickly leverage Large Language Models (LLMs) for your work.
pip install 'ragna builtin » ' # Install ragna with all extensions
ragna config # Initialize configuration
ragna ui # Launch the web app
ragna config # Initialize configuration
ragna ui # Launch the web app
