sagemaker
PulseAugur coverage of sagemaker — every cluster mentioning sagemaker across labs, papers, and developer communities, ranked by signal.
- authored Eugene Yan 90%
- used by Calgary 90%
- affiliated with Amazon Elastic Compute Cloud 70%
- used by vLLM 70%
- used by Kubernetes 70%
- used by MLOps 70%
- used by Amazon Elastic Compute Cloud 70%
- used by Docker 70%
- employed by Eugene Yan 60%
- used by TensorFlow 60%
- affiliated with Kubernetes 50%
- uses Apache Airflow 50%
7 day(s) with sentiment data
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Calgary 311 Data Analysis Pipeline Costs and Findings Detailed
This article details the findings from analyzing six years of Calgary 311 data using an MLOps pipeline built on AWS SageMaker. The author discusses the costs associated with running the entire pipeline, identifies compo…
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Exowatt uses SageMaker to customize AI agents, outperforming frontier models
Exowatt has developed a new AI pipeline using Amazon SageMaker's serverless model customization to fine-tune models for specific tasks. This approach allows Exowatt to create specialized AI agents for monitoring and man…
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Calgary uses shadow mode ML model for 311 requests on SageMaker
The author details a process of using a machine learning model in shadow mode on Amazon SageMaker to score Calgary's open 311 requests. This approach allows for evaluation of the model's performance without it directly …
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NVIDIA, OpenAI, and AWS detail infrastructure and scaling advancements
NVIDIA is enhancing its TensorRT performance optimization with a new Global Performance Tuner, aiming to scale beyond single build routes. OpenAI is rapidly expanding its online storage infrastructure to support over on…
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Calgary 311 triage model implementation faces MLOps challenges
The author discusses the challenges of implementing MLOps for the city of Calgary's 311 triage model. They highlight that a model requiring manual execution is not a true pipeline and detail a process involving SageMake…
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Meta releases 816B Muse Spark Open model for agents, with usage restrictions
Meta has released Muse Spark Open, an 816 billion parameter model with 42 billion active parameters, designed for agentic tasks and coding. The model boasts a 1 million token context window and native multimodality, wit…
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MCP Server Unifies Six AWS Inference Backends for Simplified Management
A new MCP server has been developed to manage six different AWS inference backends, including Amazon Bedrock, SageMaker endpoints, vLLM on EC2 instances, and Gemma 4 on Inferentia2 and Trainium chips. This server, imple…
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AWS launches AI-powered autoscaling for EC2, promising cost savings
Amazon Web Services (AWS) has launched Intelligent Adaptive Scaling (IAS), a new AI-driven autoscaling feature for its Elastic Compute Cloud (EC2). This feature utilizes machine learning models to predict workload spike…
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Gemma 4 inference options detailed across AWS services
This article explores how to perform inference with the Gemma 4 large language model on Amazon Web Services (AWS). It details various methods, including using Amazon Bedrock, setting up a SageMaker real-time endpoint, a…
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AI frameworks and game development trends explored
This cluster covers two distinct AI applications: one explores AI development frameworks like TensorFlow and PyTorch, comparing open-source versus proprietary solutions with case studies. The other delves into the impac…
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LLM planner's structural flaws persist despite model upgrades
A series on building an open-source LLM planning engine called PlannerCritic has revealed a structural flaw in the planner's ability to create reliable plans. Despite using advanced models like GPT-4o and implementing r…
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AWS AI/ML Services Beyond EC2 and S3 for Cloud Engineers
This article highlights 15 AWS AI and machine learning services that cloud and DevOps engineers should be aware of, moving beyond foundational services like EC2 and S3. It covers a range of offerings designed for variou…
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MLOps: Migrating ML Inference from SageMaker to Snowflake
This article discusses the benefits of migrating machine learning inference workloads from Amazon SageMaker to Snowflake. The author advocates for moving models to where the data resides within Snowflake, rather than co…
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MLOps Explained: Bridging the Gap from Notebook to Production · 8 sources tracked
This cluster of articles explores MLOps, the practice of applying DevOps principles to machine learning models to ensure they can be reliably deployed and maintained in production. Several pieces detail how to build sel…
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Qdrant cuts RAG token costs by 67% with native ColBERT reranking
Qdrant has introduced a native ColBERT reranking feature that significantly reduces token costs for Retrieval-Augmented Generation (RAG) systems. This new capability allows Qdrant to perform token-to-token comparisons d…
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Amazon to retire Mechanical Turk, ending new customer sign-ups
Amazon is phasing out its Mechanical Turk crowdsourcing service by stopping new customer sign-ups on July 30, 2026. While existing users can continue to access the platform, Amazon has stated it does not plan to introdu…
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LINE MAN Wongnai cuts AI server costs 9x, boosts app speed 4x
LINE MAN Wongnai has significantly reduced its AI server costs by 9 times and improved application speed by 4 times. This was achieved through a strategic shift in their MLOps approach, optimizing resource utilization a…
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Bank Risk Intelligence Engine Relies on MLOps and Data Analytics Tools
This article details the daily tasks of a Risk Data Analytics professional within a modern bank, focusing on the MLOps pipeline. The role involves utilizing tools like Python, SQL, and Jupyter Notebooks to manage and an…
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Cisco AI launches FAPO for automated LLM pipeline optimization
Cisco AI has developed FAPO, an open-source system designed to autonomously optimize multi-step LLM pipelines. FAPO uses Claude Code to evaluate, classify failures, propose variants, and iterate on prompts to achieve ta…
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Deploying a 35B MoE Model to SageMaker Cost-Effectively
This article details the process of deploying a fine-tuned 35B Mixture-of-Experts (MoE) model to Amazon SageMaker. It focuses on practical strategies for cost-effective deployment, specifically using QLoRA fine-tuning f…