---
title: "Machine Learning"
description: "622 posts about Machine Learning, summarised, each linking to the original."
---

# Machine Learning
> 622 posts about Machine Learning, summarised, each linking to the original.

## Articles

### [Profiling in PyTorch (Part 2): From nn.Linear to a Fused MLP](https://yomu.fyi/post/profiling-in-pytorch-part-2-from-nn-linear-to-a-fused-mlp.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Aritra Roy Gosthipaty, Rémi Ouazan Reboul, Sergio Paniego, Pedro Cuenca, Sayak Paul
- Published: Jun 11, 2026
- Type: Tutorial

Part 2 of the series examines how PyTorch profiler traces change from a single bias-enabled nn.Linear to a three-layer MLP with an intervening activation, using scripts run on an NVIDIA A100-SXM4-80GB GPU. It explains that nn.Linear represents y = x @ w.T + b, dispatches aten::addmm, and folds bias addition into the cuBLAS GEMM epilogue, while aten::t only rewrites weight strides and launches no GPU kernel. For the MLP, eager execution uses three GEMMs plus separate activation and multiplication kernels, whereas torch.compile fuses the latter operations and keeps the intermediate out of HBM, while leaving the GEMMs unchanged. A hand-written Liger Triton kernel provides the same fusion without Dynamo guards or compile latency, trading Inductor’s shape-specialized speed for generic execution across changing shapes.


### [Under the Hood: Scaling Responsible AI at Uber](https://yomu.fyi/post/under-the-hood-scaling-responsible-ai-at-uber.md)
- Company: [Uber](https://yomu.fyi/company/uber.md)
- Published: Jun 9, 2026
- Type: Explainer

Uber describes a company-wide Responsible AI program designed to govern models developed across its many platforms and teams as AI becomes central to products and services. Its five pillars are visibility, explainability, governance, education, and adoption, implemented through a centralized Model Catalog, automated metadata, feature attribution, lifecycle controls, and employee learning resources. Model Cards provide searchable model descriptions, metrics, deployment details, and documentation, while Michelangelo integrates global, local, and deep-learning explanations using PFI, SHAP-based methods, TreeSHAP, and integrated gradients. Governance is shifted left into product and engineering reviews, with completion prompts before deployment, and adoption work brings existing models into scope through lifecycle-aware, technology-agnostic, context-sensitive controls. The program gives engineers and oversight teams shared visibility into model behavior and risk while aiming to support continued AI growth without losing accountability and trust.


### [How an Agent Built a 3D Paris Gallery by Chaining Two Hugging Face Spaces](https://yomu.fyi/post/how-an-agent-built-a-3d-paris-gallery-by-chaining-two-hugging-face-spa.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Mishig Davaadorj
- Published: Jun 9, 2026
- Type: Explainer

A coding agent built a 3D Paris gallery by chaining two Hugging Face Spaces: an image-generation Space produced dark-background monument images, and VAST-AI/TripoSplat reconstructed each single image as a 3D Gaussian splat. It then handled integration work by correcting the splats’ Y-down orientation, auto-framing monuments, compressing .ply files to .ksplat at about one-third the size, and building and deploying a static Three.js viewer. The Spaces’ agents.md files provided schemas, call and polling templates, file-upload instructions, and authentication guidance, allowing the agent to call them without a client library. The same pipeline produced six-monument galleries for Egypt and Japan after the prompts changed. The account presents callable, documented multimedia components as a way to reduce integration work and make new multimedia applications cheaper to create.


### [Migrating Your GitHub CI to Hugging Face Jobs](https://yomu.fyi/post/migrating-your-github-ci-to-hugging-face-jobs.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Abubakar Abid
- Published: Jun 9, 2026
- Type: Tutorial

Trackio's GitHub Actions CI was limited by generic hosted machines, maintenance-related delays, and lack of readily available GPU runners, while the project needed CPU checks and CUDA-based tests. The setup keeps GitHub Actions as the CI coordinator and uses a dispatcher Space plus the huggingface/jobs-actions bridge to launch ephemeral GitHub runners inside Hugging Face Jobs, selected through labels such as hf-jobs-cpu-upgrade and hf-jobs-t4-small. The guide covers duplicating and configuring the dispatcher, creating a GitHub App, supplying runner credentials and an HF token, changing runs-on, choosing Docker images, and validating runs and logs; Playwright and NVIDIA CUDA images are used for CPU and GPU examples. In Trackio, the CPU job took 1m10s versus a 1m40s GitHub ubuntu-latest baseline, while the GPU check passed in 45s and cost less than a cent at the t4-small rate; logs were available through both systems.


### [Real-world grounding in agentic AI](https://yomu.fyi/post/real-world-grounding-in-agentic-ai.md)
- Company: amazon.science
- Author: Rose Yu
- Published: Jun 8, 2026
- Type: Problem & solution

Foundation models deployed as physical AI agents face critical risks when natural language hallucinations translate into physical law violations and operational hazards. To ensure physical consistency and reliability, researchers from the University of California, San Diego and Amazon Fulfillment Technology introduced four grounding pillars. Physics-guided deep learning embeds symmetries and differential equations into pretraining to obey conservation laws while reducing dataset requirements. The UQ4CT framework applies mixture-of-experts architectures to produce calibrated uncertainty estimates, cutting expected calibration error by over 25% and halting operations when safety thresholds are breached. Furthermore, the Adapting-While-Learning framework and formal verifiers like Hilbert integrate numerical simulators and interactive proof systems to enforce mathematical and scientific correctness.


### [Bridging intent and execution in agentic systems](https://yomu.fyi/post/bridging-intent-and-execution-in-agentic-systems.md)
- Company: amazon.science
- Author: Gaurav Gupta, Vatshank Chaturvedi
- Published: Jun 8, 2026
- Type: Problem & solution

AI agent performance increasingly depends on the harness mediating interactions between language models and tools rather than raw model reasoning. To address mismatches between model intent and harness execution, the authors developed Simple Strands Agent, an open-source single-agent harness. The architecture implements robust tool-interface practices, such as condensing middle log outputs from bash commands, enforcing line-aware string replacements with ambiguity checks, and returning diff files after edits for verification. Evaluated across benchmarks like SWE-Bench-Verified, SWE-Bench-Pro, and Terminal-Bench 2, these model-harness alignments yielded consistent performance gains across frontier models without task-specific tuning. The findings show that model families require tailored interface codesign alongside stable evaluation infrastructure to achieve optimal benchmark reliability.


### [The Open Source Community is backing OpenEnv for Agentic RL](https://yomu.fyi/post/the-open-source-community-is-backing-openenv-for-agentic-rl.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: ben burtenshaw, Joseph Spisak, Lysandre, Davide Testuggine, will brown, Joy Liu, Peyton Walters, Chris Wing, Daniel (Unsloth), Andrew Zhou, Govind Kamtamneni, Michael Han, Hamid Shojanazeri, Sanyam Bhutani, Zach Wentz, Emre Guven, Lewis Tunstall, Sergio Paniego, Banghua Zhu, Haoguang Cai
- Published: Jun 8, 2026
- Type: Announcement

OpenEnv is becoming a community-coordinated, open-source tool for building agentic execution environments such as terminals and browsers, with the stated goal of helping train open-source agents. Starting now, a committee including Meta-PyTorch, Reflection, Unsloth, Modal, Prime Intellect, Nvidia, Mercor, Fleet AI, Microsoft, Hugging Face, and RadixArk will coordinate the project, which now lives at huggingface/OpenEnv and is supported or adopted by numerous AI organizations. Technically, OpenEnv is being narrowed to an interoperability and protocol layer: environments expose a Gymnasium-style reset(), step(), and state() API through client/server deployments, standard HTTP or WebSocket protocols, Docker packaging, and MCP compatibility. It does not define rewards or training loops; those remain in specialized libraries, while OpenEnv connects harnesses, environments, and trainers across ecosystems. Planned work includes external rewards, dataset-backed tasksets, harness integrations, end-to-end examples, and automatic environment validation.


### [ClickHouse appoints new leader for Asia Pacific and expands global go-to-market leadership team](https://yomu.fyi/post/clickhouse-appoints-new-leader-for-asia-pacific-and-expands-global-go.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Jun 8, 2026
- Type: Announcement

ClickHouse announced an expansion of its global go-to-market leadership team, appointing Ed Lenta as Vice President, Asia Pacific and Japan (APJ). Based in Singapore, Lenta will lead regional go-to-market efforts and accelerate adoption of ClickHouse Cloud and the open-source database, following leadership roles at Databricks, AWS, and VMware. The company also appointed Takeshi Kaneko as Country Manager of Japan and added executives for financial services, public sector, global solutions architecture, and revenue strategy and operations. ClickHouse said the appointments support its growth after surpassing $250 million in annual run-rate revenue, more than triple the prior year, and exceeding 4,000 customers, including more than 1,000 net new customers in one quarter.


### [What is Explainable AI (XAI)?](https://yomu.fyi/post/what-is-explainable-ai-xai.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Databricks Staff
- Published: Jun 5, 2026
- Type: Explainer

Explainable AI (XAI) comprises techniques that help people understand how AI systems produce specific outputs, particularly when machine-learning and deep-learning models operate as black boxes. It distinguishes intrinsically interpretable models, such as decision trees and linear or logistic regression, from post-hoc methods including SHAP, LIME, counterfactuals, saliency maps and Grad-CAM. A typical workflow selects a model and prediction, applies a method suited to the model and audience, reviews outputs such as feature scores or heatmaps, and uses them to assess accuracy, fairness, reliability and compliance. The article stresses that post-hoc explanations are approximations rather than definitive proof, so teams should validate them with domain expertise and, where appropriate, combine methods; MLflow and Unity Catalog can preserve explanation artifacts, lineage and auditability.


### [The AI and Data Transformation Playbook for Enterprise Teams](https://yomu.fyi/post/the-ai-and-data-transformation-playbook-for-enterprise-teams.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Databricks Staff
- Published: Jun 5, 2026
- Type: Explainer

The playbook presents AI and data transformation as an enterprise capability built on trustworthy, well-governed data, rather than a model-only project. It explains how governance, scalable pipelines, and continuous quality monitoring address incompatible formats, missing values, duplicate records, and schema inconsistencies from systems such as CRM platforms, operational databases, IoT sensors, and cloud applications. Operational guidance covers KPI design, named pipeline ownership, review checkpoints, data mapping and lineage, automated de-duplication, deterministic enrichment, regulatory classification, and role-based access. It states that deterministic tests and human review remain necessary for AI-assisted code, while telemetry and quarterly audits help detect reliability, governance, and data quality degradation. The concluding position is that operational discipline—not model sophistication—supports reliable machine learning, predictive analytics, and generative AI outcomes.


### [Nemotron 3.5 Content Safety: Customizable Multimodal Safety for Global Enterprise AI](https://yomu.fyi/post/nemotron-3-5-content-safety-customizable-multimodal-safety-for-global.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Varun Singh, Isabel Hulseman, Anuj Doshi, Shyamala Prayaga
- Published: Jun 4, 2026
- Type: Announcement

Nemotron 3.5 Content Safety is NVIDIA’s 4B-parameter model for evaluating user prompts, optional images, and optional assistant responses together, with multilingual coverage and enterprise-specific policy enforcement. It extends Nemotron 3 by accepting natural-language custom policies, producing optional auditable reasoning traces, and supporting binary verdicts, category-labelled verdicts, or THINK mode; disabling THINK preserves the default low-latency path. Built on Google Gemma 3 4B IT with a LoRA adapter and a 128K context window, it uses the Aegis 2.0 taxonomy of 13 core categories and 10 subcategories, and targets deployment on GPUs with 8GB or more of VRAM. The accompanying multimodal, multilingual dataset includes concise reasoning traces, while reported results average 96.5% on Multilingual Aegis, 88.8% on RTP-LX, and about 85% across the evaluated benchmark set.


### [Agentic Supplier Management with MongoDB Atlas, Voyage AI, and Multi-Modal Search](https://yomu.fyi/post/agentic-supplier-management-with-mongodb-atlas-voyage-ai-and-multi-mod.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Author: Ronan Conlon
- Published: Jun 3, 2026
- Type: Problem & solution

Retailers face major operational bottlenecks when managing supply chain disruptions through legacy enterprise resource planning systems and siloed documents. Traditional tabular structures struggle to index multi-modal data like images and PDF contracts, delaying critical supplier discovery during regional crises or tariff changes. To overcome these constraints, organizations decouple supplier management into a dedicated operational data layer using MongoDB Atlas and Voyage AI. This architecture stores polymorphic supplier profiles and high-dimensional vector embeddings within a unified collection, enabling natural language semantic search for alternate partners. Real-time updates propagate across systems using MongoDB Change Streams, allowing businesses to rapidly assess supply chain impacts and identify alternative vendors in minutes.


### [Ground truth is a process, not a dataset](https://yomu.fyi/post/ground-truth-is-a-process-not-a-dataset.md)
- Company: amazon.science
- Author: Venkatesh Saligrama
- Published: Jun 3, 2026
- Type: Problem & solution

Evaluating search-augmented AI research reports requires verifying complex, multi-source claims that depend heavily on surrounding context. Unassisted PhD-level domain specialists tasked with establishing static ground-truth labels achieved only 60.8% accuracy on a hidden test set during controlled testing. To resolve benchmark errors on cognitively demanding tasks, Amazon AGI introduced the audit-then-score evaluation protocol alongside DeepFact-Bench and the DeepFact-Eval verification system. Under this protocol, model disagreements serve as challenges requiring concrete counterevidence and written rationales, enabling human auditors to evaluate competing claims and update benchmark ground truth prior to scoring. Operating under this iterative workflow, expert test accuracy rose to 90.9% across four rounds, and DeepFact-Eval reached 83.4% accuracy with GPT-4.1.


### [Direct Preference Optimization Beyond Chatbots](https://yomu.fyi/post/direct-preference-optimization-beyond-chatbots.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Erick Lachmann, Gabriel Pimenta de Freitas Cardoso, Francisco de Almeida Rocha Alves
- Published: Jun 3, 2026
- Type: Explainer

DharmaOCR applies Direct Preference Optimization (DPO) beyond chat alignment to reduce text degeneration in structured OCR, where outputs are either acceptable transcriptions or failure loops. In a benchmark of Brazilian Portuguese structured-document OCR, vanilla degeneration rates among open-source model families ranged from below 1% to above 33%, while supervised fine-tuning (SFT) did not consistently reach production-acceptable levels. After SFT, the pipeline generated multiple candidates for 23,726 documents, used an automated LLM judge to score them, and retained the model’s degenerate outputs as rejected examples paired with chosen outputs. DPO reduced degeneration in every tested family, by 59.4% on average and up to 87.6%, without reducing extraction quality according to the benchmark; applicability is presented as dependent on failures being categorically identifiable, reliably scoreable, and sufficiently numerous.


### [Stack Benchmarking](https://yomu.fyi/post/stack-benchmarking.md)
- Company: [Ramp](https://yomu.fyi/company/ramp.md)
- Author: Ryan Stevens
- Published: Jun 3, 2026
- Type: Benchmark

Ramp’s Stack is an AI-native accounting suite designed to automate repetitive book-closing work, including reconciliations, variance analysis, data entry, and schedules and accruals. To avoid overfitting to individual design partners, Ramp built a benchmark of synthetic business worlds, realistic accounting tasks, and accountant-written grading criteria, including standard and roll-forward worlds for testing memory transfer. The benchmark contains 237 tasks and 3,469 grading criteria in the analyzed slice, and supports repeated runs to compare models, prompts, tools, skills, harness changes, and memory behavior. Optimization included ablating skills, shrinking a spreadsheet skill from 14,000 to 5,000 characters, and tuning the system against end-to-end task performance rather than narrow evaluations. The resulting Stack system achieved the highest agent performance, with 4% higher accuracy and 3% better Pass@1, while remaining on the latency frontier with GPT 5.4; schedules and accruals were harder for raw models than variance analysis.


### [When history fails you, borrow from geography](https://yomu.fyi/post/when-history-fails-you-borrow-from-geography.md)
- Company: [Airbnb](https://yomu.fyi/company/airbnb.md)
- Author: Harrison Katz
- Published: Jun 2, 2026

When historical travel data became uninformative during the asynchronous post-COVID recovery, Airbnb redesigned its demand forecasting to borrow signals across geographies rather than waiting for local data. Using a hierarchical Bayesian framework, the team propagated updated posterior estimates from early-recovering corridors as informative priors for structurally similar, later-recovering corridors. This allowed accurate corridor-level demand forecasting in near real time during periods of high disruption and uneven market reopening.


### [Data Science vs Data Analytics: Compare Careers, Skills, and Degrees](https://yomu.fyi/post/data-science-vs-data-analytics-compare-careers-skills-and-degrees.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Databricks Staff
- Published: Jun 2, 2026
- Type: Explainer

Data analytics and data science are compared as data-focused career paths, with analytics interpreting existing, usually structured data for trends and business decisions, while science builds models and automated systems to predict future outcomes. The guide contrasts typical questions, outputs, tools, education paths, and roles: analysts use SQL, Excel, Tableau, or Power BI for dashboards and reports, whereas data scientists use Python, R, Apache Spark, and MLflow for predictive models and algorithms. It describes analytics types from descriptive through prescriptive, and a data science workflow spanning collection, feature engineering, training, validation, and deployment, including unstructured data such as text, images, and sensor streams. It also explains collaboration, including analysts defining problems and baselines before scientists build models, and offers portfolio suggestions and questions for choosing a path.


### [Practical Data Lakehouse Examples and Use Cases](https://yomu.fyi/post/practical-data-lakehouse-examples-and-use-cases.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Databricks Staff
- Published: Jun 2, 2026
- Type: Explainer

The article presents practical data lakehouse patterns for streaming analytics, IoT pipelines, machine learning workflows, and enterprise reporting, addressing the gap between theoretical definitions and deployable examples. It explains how a lakehouse combines low-cost, schema-flexible object storage with schema enforcement, ACID transactions, data versioning, lineage tracking, and query performance, allowing SQL and ML workloads to use shared open-format tables. Examples include second-level fraud detection, medallion-based historical analytics, predictive maintenance from sensor data, and governed customer 360 profiles. The implementation guidance covers raw storage and partitioning, centralized catalogs, decoupled compute, role-based access, time travel, migration coexistence, SLAs, observability, and lifecycle policies. Together, these patterns are presented as a unified architecture that reduces duplication and data movement while supporting governed analytics at scale.


### [Holo3.1: Fast & Local Computer Use Agents](https://yomu.fyi/post/holo3-1-fast-local-computer-use-agents.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Maxime Langevin, Hamza Benchekroun, Axel Moyal, Emrick Sinitambirivoutin, Antonio Loison, Avshalom Manevich, Tony Wu, Pierre-Louis Cedoz, Aurélien Lac, Ronan Riochet
- Published: Jun 2, 2026
- Type: Announcement

Holo3.1 is a family of computer-use models released to improve robustness across web, desktop, and mobile environments, agent frameworks, and deployment targets. Based on Qwen, it adds native function-calling alongside structured JSON outputs, expands mobile automation, and offers 0.8B, 4B, 9B, and 35B-A3B sizes for different cost, latency, and performance needs. On AndroidWorld, the 35B-A3B model rises from 67% to 79.3%, while the 4B and 9B variants rise from 58% to 72%; function-calling and native execution achieve near-parity across the stated benchmarks, and Holo3.1 improves more than 25% over Holo3 in the Holotab harness. The release also provides 35B-A3B FP8, Q4 GGUF, and NVFP4 checkpoints for local inference: NVFP4 matches FP8 on OSWorld at about two points below BF16, reaches 1.74 times BF16 token throughput on DGX Spark, and with harness optimizations cuts average step time from 6.8 seconds to 3.3 seconds.


### [RAG vs Fine Tuning: Enterprise Decisions for AI Models and AI Systems](https://yomu.fyi/post/rag-vs-fine-tuning-enterprise-decisions-for-ai-models-and-ai-systems.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Databricks Staff
- Published: Jun 2, 2026
- Type: Explainer

The guide frames enterprise choice between retrieval-augmented generation (RAG) and fine tuning as a trade-off between injecting knowledge at inference time and changing model weights before deployment. RAG connects an LLM to external stores by embedding a query, searching vector databases for similar chunks, and adding retrieved context to the prompt, making current information and citations possible. Fine tuning uses curated input-output examples and supervised training to update parameters; full-model training is expensive, while LoRA trains a smaller set of added weights and can support a single-GPU pilot. The decision depends on whether the priority is stable behavior and output format or fresh, traceable knowledge, alongside data quality, latency, compute, and maintenance requirements. For many mature deployments, the guide recommends combining both: fine tuning for domain understanding and consistency, and RAG for real-time data.


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