The weekly briefing for production AI
The week in AI/ML data engineering — curated, with a take on each.
RAG, vector search, MLOps, LLM serving, pipelines, observability. We read the firehose so you don't — every link gets a verdict and an editor's take. No hype, no reposts.
Read by data & ML engineers building production AI. Unsubscribe anytime.
Serving LLM Inference for a FinTech App with 100k Monthly Users
Design an LLM inference system for a FinTech app with 100k users. Focus on latency, cost, and reliability using NVIDIA and AWS tools.
Three SLOs every search team needs: monitoring search latency, availability and quality with OpenTelemetry
OpenTelemetry spans can enhance monitoring of search latency, availability, and quality within Elastic Observability. The integration allows for SLOs, burn rate alerts, and anomaly detection to be built using signals from OpenTelemetry data. However, implementation complexity and operational overhead may affect its suitability for some teams.
Also this week
All issues →Let the big model think, let the small model work: Splitting LLM costs in Elastic Workflows
How Companion.energy Reduced Query Latency 25x and Compressed Terabytes to Gigabytes with Tiger Cloud
Previous Issues
Full archive →How Hugging Face Inference Endpoints, Jobs, and Buckets Power Search on Papers with Code
If you're managing a large collection of papers and need advanced search capabilities, this hybrid approach can enhance your query results. Just be prepared for the additional complexity in infrastructure management.
Three Kinds of RAG Corpus, and What It Costs to Build for the Wrong One
If you're architecting a RAG system, understanding your document shapes is crucial to avoid costly inefficiencies. Assessing your data's characteristics now will save you headaches (and expenses) later.
Building an AI Text Detector From Scratch
When building AI/ML systems, understanding how a model scales in production is as crucial as its accuracy. This project showcases the potential of custom solutions but also highlights the challenges of operationalizing AI effectively.
What is AI Observability? A Complete Guide to Debugging and Monitoring Modern AI Systems at Scale
When your AI system behaves inconsistently despite healthy infrastructure metrics, effective observability becomes crucial. Focusing on methodologies for practical implementation can help bridge the gap between performance and user satisfaction.
How Databricks Feature Store serves features with sub-second freshness
In scenarios where real-time feature freshness is critical, understanding the operational and financial implications of a feature store is essential. Evaluate whether the benefits outweigh the complexities and costs before adopting.
Inside vLLM: Anatomy of a High-Throughput LLM Inference System (2025)
When considering vLLM, remember that while it promises high throughput, the real test will be how well it manages the transition to online, multi-GPU operations without compromising data quality. Be prepared for the operational complexities involved.
How OneAdvanced deployed over 50 AI agents on UK-sovereign AWS
If your organization requires UK-sovereign AI solutions, OneAdvanced's deployment might be a reference point. Just be wary of the hidden costs and operational challenges that come with scaling such a system.
Tiered KV cache for large LLMs on Amazon SageMaker HyperPod with Curvine
If you're looking to optimize inference time for large language models, this tiered cache could be worth exploring. Just ensure you validate its performance and cost-effectiveness against your current solutions before fully committing.
The 4 Failure Modes of Agent Context in Production
When deploying AI agents, understanding and managing context is crucial to ensure they perform reliably in production. Failure to address context-related issues can lead to significant operational setbacks.
Inference meta-monitoring for Amazon SageMaker AI endpoints with Amazon Quick
If you're operating ML models in production, monitoring for drift and data quality is critical for maintaining performance. Evaluate this tool thoroughly, especially if you're already on AWS, but be aware of potential integration challenges.
The Inference Engineering Masterclass — Philip Kiely & Ali Taha, Baseten
If you're considering new tools for inference engineering, be wary of the hype surrounding Baseten. Monitor how they leverage their funding and the actual performance of their offerings before making any commitments.
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Production AI is a data engineering problem.
- →The week's signal in RAG, vector search, MLOps & serving — curated
- →A verdict and an editor's take on every link, not just headlines
- →One email, every Tuesday. No hype, no reposts, no spam
Read by data & ML engineers building production AI. Unsubscribe anytime.