Kubernetes
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AI Remote Hiring Trends Dataset
Read Full Article: AI Remote Hiring Trends Dataset
A new dataset has been created to streamline the process of identifying AI-related remote job opportunities by automating the collection of job postings. The dataset captures 92 positions from December 19, 2025, to January 3, 2026, highlighting key skills such as AI, RAG, ML, AWS, Python, SQL, Kubernetes, and LLM. The output is available in CSV and JSON formats, along with a one-page summary of insights. The creator is open to feedback on enhancing skill tagging and location normalization and is willing to share a sample of the data and the script's structure with interested individuals. This matters because it provides a more efficient way for job seekers and employers to navigate the rapidly evolving AI job market.
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Streamline ML Serving with Infrastructure Boilerplate
Read Full Article: Streamline ML Serving with Infrastructure Boilerplate
An MLOps engineer has developed a comprehensive infrastructure boilerplate for model serving, designed to streamline the transition from a trained model to a production API. The stack includes tools like MLflow for model registry, FastAPI for inference API, and a combination of PostgreSQL, Redis, and MinIO for data handling, all orchestrated through Kubernetes with Docker Desktop K8s. Key features include ensemble predictions, hot model reloading, and stage-based deployment, enabling efficient model versioning and production-grade health probes. The setup offers a quick deployment process with a 5-minute setup via Docker and a one-command Kubernetes deployment, aiming to address common pain points in ML deployment workflows. This matters because it simplifies and accelerates the deployment of machine learning models into production environments, which is often a complex and time-consuming process.
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Autoscaling RAG Components on Kubernetes
Read Full Article: Autoscaling RAG Components on KubernetesRetrieval-augmented generation (RAG) systems enhance the accuracy of AI agents by using a knowledge base to provide context to large language models (LLMs). The NVIDIA RAG Blueprint facilitates RAG deployment in enterprise settings, offering modular components for ingestion, vectorization, retrieval, and generation, along with options for metadata filtering and multimodal embedding. RAG workloads can be unpredictable, requiring autoscaling to manage resource allocation efficiently during peak and off-peak times. By leveraging Kubernetes Horizontal Pod Autoscaling (HPA), organizations can autoscale NVIDIA NIM microservices like Nemotron LLM, Rerank, and Embed based on custom metrics, ensuring performance meets service level agreements (SLAs) even during demand surges. Understanding and implementing autoscaling in RAG systems is crucial for maintaining efficient resource use and optimal service performance.