Topic
All digests tagged Machine Learning Operations (MLOps)
Suggested route
Move from earlier context through the available material to the newest signals. This route is a reading aid, not a difficulty ranking.
Arize's agent Alex evolved from a 50% hallucination prototype to a robust system. Key lessons include moving planning into custom harness code, treating large tool results as files, and using small markdown files for long-term memory.
Read article
Learn the full pipeline for post-training Nemotron for European localization. We cover defining behavior contracts, generating synthetic data with Persona datasets, and fine-tuning with LoRA.
Read article
Wells Fargo built a proprietary Foundation Model for transactions, pre-training a transformer on billions of raw customer transactions. This single backbone model improves fraud detection and streamlines multiple downstream financial tasks.
Read article
Learn how to build an automated total-loss auto claims workflow using LlamaIndex Parse. Discover the multi-stage process of document classification, parsing, and extraction, ensuring high accuracy and cost efficiency.
Read article
To convince your team to invest in AI Evals, don't hard sell. Instead, perform structured error analysis on user traces, quantify failure modes, and consistently demonstrate value by showing how you find and fix issues.
Read article
Learn how to 'actually tokenmaxx' LLMs. This guide details a systematic workflow using DSPy and shadow evaluation to cut prompt bloat by 51% while maintaining quality and reducing compute costs.
Read article
Learn the 7 most common mistakes in AI evaluation (Evals). From using generic metrics to failing to validate LLM judges, this guide shows how to build Evals you can actually trust.
Read article
Learn how to build a continuous 'train-to-deploy' AI loop using open models. Discover FireAttention, custom CUDA kernels, and advanced training methods to match closed-source model quality at scale.
Read article
MasterClass details its multi-agent AI system, NCX, and the advanced observability stack built on CoreWeave. Learn how tracing, MCP servers, and an AgentOps loop enable self-improvement in complex AI applications.
Read article
The biggest AI models require distributed inference because they exceed single GPU memory capacity. Learn how Data, Pipeline, Tensor, and Expert parallelism scale LLMs across multiple GPUs.
Read article
Roland Gavrilescu presents a blueprint for autoresearch built on three pillars: the loop is the product, system distillation is the moat, and optimizing for valued work per watt. Learn how to build self-improving AI agents.
Read article
The speaker critiques AI 'slop' and argues that accurate model evaluation requires building small, realistic datasets and testing against specific failure modes, rather than relying on leaderboards or vendor demos.
Read article
Lyft rebuilt its customer support agent stack using a meta-agent architecture with LangGraph and LangSmith. This self-serve platform reduced agent build time from six months to two weeks, boosting resolution rate by 16%.
Read article
Stop relying on MTEB scores! Learn how to optimize embedding models by mastering quantization, managing vector dimensions, and building cheap first-phase/expensive reranker search architectures.
Read article
Model Hardware Standard (MHS) allows AI agents to run physical science experiments autonomously. By standardizing hardware interaction, LLMs can now control complex lab equipment like microscopes for drug discovery and research.
Read article
Great models often fail in production due to poor scoping, monitoring gaps, or changing real-world data. Success requires aligning model value with business need and planning for drift.
Read article
AlphaLab is a multi-agent system that automates quant research by managing the entire lifecycle: from natural language problem input to running cluster jobs and statistical tests. Its core value lies in building verifiable evaluation environments.
Read article
Data's value lies in the trajectory and reasoning trace, not just the final output. The speaker argues that successful companies must pivot from being mere 'data businesses' to becoming infrastructure providers (neo-labs).
Read article
Harbor provides a rollout-centered view of evaluating and improving AI agents. It connects sandboxed environments, agent evaluations, and optimization workflows into a practical framework.
Read article
Discover how to build self-healing AI agents by capturing every interaction trace. Learn about Merkle DAGs, Flink SQL anomaly detection, and using proprietary data for specialized model fine-tuning.
Read article