---
title: "MLflow"
description: "24 posts about MLflow, summarised, each linking to the original."
---

# MLflow
> 24 posts about MLflow, summarised, each linking to the original.

## Articles

### [How Discovery Bank delivers hyper-personalized banking at scale: behavioral AI, governed data, and real-time decisioning](https://yomu.fyi/post/how-discovery-bank-delivers-hyper-personalized-banking-at-scale-behavi.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Jack Yallop, Stuart Emslie
- Published: Sep 1, 2026

Discovery Bank’s hyper-personalized banking model addresses how to make interactions relevant to individual behavior while meeting financial-services demands for scale, speed, security, and governance. The bank combines demographic, transactional, digital-engagement, savings, borrowing, credit-risk, rewards, and lifestyle-related data on the Databricks Data and AI Platform, producing governed reusable features, indicators, scores, forecasts, and recommendations. Behavioral models and a reusable next-best-action decisioning layer support personalization, fraud detection, servicing, and banker assistance; TRUST alerts assess transactions against client-specific norms and can escalate from explanation to account locking. A four-layer architecture adds control services and specialized generative AI and agents to trusted data and analytical foundations, with governance remaining in the loop. Reported results include a 40% uplift in client engagement impact, 20x faster pipeline development and data processing, 5x faster data-product creation, more than 300 models built per day, and return on investment above 500%.


### [How Databricks is turning video into searchable, actionable intelligence](https://yomu.fyi/post/how-databricks-is-turning-video-into-searchable-actionable-intelligenc.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Justin Monaldo, Kacey Hertan, Yvan Aquino
- Published: Jun 26, 2026

Databricks presents video analysis as a data engineering problem for organizations with terabytes of footage that is difficult and expensive to review manually. An app accepts a video and a natural-language prompt, then triggers a Lakeflow job on Serverless GPU Compute to run Meta’s SAM3 segmentation model frame by frame and retain matching moments. Those clips preserve original timestamps and are sent through the Databricks Foundation Model API for summaries that can be written to tables or passed into downstream workflows. In one example, 26 minutes of traffic footage became one minute and 55 seconds of relevant video. The model-agnostic pipeline uses MLflow signatures to support interchangeable or custom models, while event-driven execution and independent serverless GPUs allow concurrent processing without cluster management or idle GPU costs.


### [End-to-End RAG Workflow: How Retrieval Augmented Generation Works](https://yomu.fyi/post/end-to-end-rag-workflow-how-retrieval-augmented-generation-works.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Databricks Staff
- Published: Jun 23, 2026

Retrieval Augmented Generation (RAG) connects a large language model to external knowledge at inference time, addressing outdated training data and access to proprietary or real-time information. The workflow ingests and normalizes sources, splits documents into chunks, embeds them in a vector store, retrieves context, assembles a prompt, and generates an answer. Semantic search can be combined with BM25 keyword search through reciprocal rank fusion, while reranking can improve precision; the same embedding model must be used during ingestion and querying. The guide presents evaluation and deployment considerations, including separate measurement of retrieval precision and generation faithfulness, versioning, monitoring, and containerized components. It identifies poor retrieval as the most common failure mode and explains that RAG reduces, but does not eliminate, hallucinations.


### [Data scientists: Powering the future of AI and analytics](https://yomu.fyi/post/data-scientists-powering-the-future-of-ai-and-analytics.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Databricks Staff
- Published: Jun 18, 2026

Data scientists connect statistics, programming, and domain knowledge to turn raw data into models, recommendations, and decisions with business consequences. The role now extends beyond classical modeling to large language models, generative AI applications, agentic systems, and production workflows involving deployment, monitoring, and retraining. Modern practice combines Python, SQL, and libraries such as scikit-learn, PyTorch, Spark, and MLflow with data engineering basics, communication, and domain expertise. Data scientists contribute across problem framing, data access, preparation, feature engineering, experimentation, deployment, and lifecycle monitoring, with governed access and lineage helping align training data with production data. The article concludes that AI assistants and agents can automate routine work, but human judgment remains necessary for trustworthy evaluation, business framing, and actionable recommendations.


### [What’s new in Genie Code at Data + AI Summit 2026](https://yomu.fyi/post/what-s-new-in-genie-code-at-data-ai-summit-2026.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Julia Powell, Gal Oshri, Weston Hutchins
- Published: Jun 17, 2026

At Data + AI Summit 2026, Databricks announced expansions to Genie Code for complex, agentic data and ML work. The changes include a full-page command center for managing concurrent threads and assets, upgrades across production ML engineering, and scheduled tasks that run prompts while users are away. For ML workflows, Genie Code uses Databricks production expertise and Genie Ontology, integrates with MLflow and Model Serving, and can move GPU jobs to AI Runtime while using workspace environment features. It can write features, coordinate edits, run and debug code, compare candidates, inspect endpoint health, and diagnose issues, with users deciding what to keep. Scheduled tasks are described as coming soon, creating reviewable threads from prompts and optional Databricks assets.


### [Introducing Genie ZeroOps: Put your data and AI operations on autopilot](https://yomu.fyi/post/introducing-genie-zeroops-put-your-data-and-ai-operations-on-autopilot.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Bilal Aslam, Lennart Kats, Ray Zhu, Mike Del Balso, Ori Zohar
- Published: Jun 16, 2026

Genie ZeroOps is an autonomous background agent for monitoring and operating data and AI assets, including jobs, pipelines, tables, and ML models. It continuously detects visible and silent failures, uses Unity Catalog lineage and platform observability to assess root causes, generates remediation through development workflows, and verifies fixes in isolated sandboxes. These environments use shallow, zero-copy table clones, scoped permissions, and network isolation, so proposed changes run against real data without touching production or applying anything before approval. For ML workloads, the agent can diagnose degraded predictions, train a candidate on corrected features, evaluate it against the production model’s existing eval suite and criteria, and support live-traffic ramping when it is measurably better. Genie ZeroOps is entering private preview in the coming weeks, initially supporting jobs, pipelines, tables, and ML workloads; Apps and Lakebase databases are on the roadmap.


### [Empower your healthcare agents with ready-to-use MCP on Databricks Marketplace](https://yomu.fyi/post/empower-your-healthcare-agents-with-ready-to-use-mcp-on-databricks-mar.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Yen Low, Mark Lee, Matthew Giglia, Nicholas Siebenlist, Jay Bhankharia, Paul Ford, Itai Weiss
- Published: Jun 11, 2026

Databricks announces ready-to-use Model Context Protocol (MCP) servers for healthcare and life sciences through Databricks Marketplace, addressing the need to combine curated biomedical knowledge, timely data, specialized tools, and private records. Listings include services for drug and target intelligence, literature, clinical trials, FDA information, Medicare coverage, ontologies, real-world evidence, clinical semantics, and interoperability, while Climb connects live public sources with private Gold-layer data under Unity Catalog governance. Marketplace and custom MCP servers are centralized in the MCP Catalog and governed by Unity AI Gateway, with Genie Spaces, AI Search, Unity Catalog functions, and SQL Warehouses available as managed MCP servers. Users can assemble agents in AI Playground, Agent Bricks, or notebooks, then deploy endpoints or apps with MLflow tracing, evaluation, human feedback, and AI guardrails; examples span molecular-property lookup, clinical questions, and drug research.


### [AI Serving Platform That Adapts to Your Model](https://yomu.fyi/post/ai-serving-platform-that-adapts-to-your-model.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Anshul Gupta
- Published: Jun 10, 2026

Databricks Custom Model Serving addresses the operational burden of serving custom models, whose resource profiles, traffic patterns, and latency requirements vary widely from small CPU classifiers to large GPU-backed language models. Its fully managed platform packages MLflow models and uses isolated Kubernetes deployments, model-appropriate runtimes, and a short request path to limit interference and per-request overhead. At the center, the AutoPilot Pod Autoscaler combines active concurrency and queue signals for horizontal scaling with CPU, GPU, and memory measurements for model-aware target-concurrency adjustment, allowing one controller to adapt across workloads. Warm pools, provisioned concurrency, and zero-downtime updates address startup and deployment concerns, while reported production results include 90%+ cost savings for some customers, up to 2x improvement in p99 and p50 latency, 100K+ QPS, and 99.99% availability.


### [Modern BSA/AML compliance on Databricks](https://yomu.fyi/post/modern-bsa-aml-compliance-on-databricks.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Kateryna Savchyn, Pavithra Rao, Mimi Park, Emerson Bayuk
- Published: Jun 10, 2026

AML operations are strained by fragmented systems, high false-positive volumes, manual case documentation, and opaque vendor scoring, leaving analysts focused on backlog rather than financial-crime intelligence. The proposed Databricks Data + AI Platform unifies transaction monitoring, KYC, sanctions, case history, and policy data under Unity Catalog, using Lakeflow Connect and a Bronze–Silver–Gold Delta architecture with masking, row-level security, and lineage. MLflow, Model Serving, Lakehouse Monitoring, and inference tables support institution-specific detection models, while Agent Bricks coordinates agents for evidence gathering, recommendations, and SAR drafting with analysts retaining final decisions. The architecture also uses Lakebase for governed operational state and Databricks Apps for analyst and executive experiences. Reported outcomes include a 75% reduction in false positives reaching the analyst queue and compressing three-to-six-hour investigations to minutes.


### [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

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.


### [Observability for any agent, anywhere: Production-ready tracing with OpenTelemetry & Unity Catalog on Databricks](https://yomu.fyi/post/observability-for-any-agent-anywhere-production-ready-tracing-with-ope.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Firas Farah, Bruno Faria, Anoop Sunke
- Published: May 22, 2026

Databricks supports writing OpenTelemetry (OTel) traces directly to Unity Catalog, where real-time telemetry is stored in Delta tables for governed analytics and retention. AI traces capture prompts, tool calls, responses, latency, and execution paths, enabling debugging, evaluation, and monitoring, while lakehouse storage also allows SQL queries, dashboards, joins with business data, and PII controls. The managed, serverless ingestion layer uses Zerobus Ingest to accept OTLP over gRPC from collectors and REST integrations, streaming spans, logs, and metrics to Unity Catalog without intermediate message buses. A LangGraph support manager assistant demonstrates instrumentation with mlflow.langchain.autolog() and an @MLflow.trace root span, while Genie is invoked through MCP for data-driven questions. The resulting traces can be searched in MLflow, evaluated at scale, and monitored continuously, with FAQ details stating 200 QPS starting throughput, no storage limit, and Unity Catalog governance options for access control, masking, and filtering.


### [From "What Happened?" to "What Will Happen?"](https://yomu.fyi/post/from-what-happened-to-what-will-happen.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Ryuta Yoshimatsu, Javier Poveda Panter, Dominik Safaric, Philipp Singer, Diana Kriuchkova, Sauraj Gambhir, Dael Williamson, Bryan Smith
- Published: May 21, 2026

Databricks Genie makes descriptive analytics accessible in natural language, but predictive questions still require specialized data science workflows and carefully prepared datasets. This post presents a multi-agent supervisor deployed as a Databricks App, combining Genie, TabPFN, and Agent Bricks to turn business questions into predictions. The orchestrator asks Genie to use governed Lakehouse data, schemas, relationships, and semantics to generate labeled training data through SQL, then sends it to TabPFN, which predicts in a single forward pass without feature preprocessing, model selection, or hyperparameter tuning. The resulting conversational experience supports descriptive and predictive analytics with Unity Catalog lineage and access control, while an MLflow GenAI evaluation harness monitors reliability and regressions. Its central limitation is that predictions depend on Genie producing a meaningful dataset with a clear label, so missing signals, joins, outcomes, or agent omissions can make results unreliable.


### [How to Build Real-Time Fraud Detection using Spark Real-Time Mode and Lakebase](https://yomu.fyi/post/how-to-build-real-time-fraud-detection-using-spark-real-time-mode-and.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Sixuan He, Navneeth Nair
- Published: May 19, 2026

Card fraud operates in seconds, while organizations need to score and block suspicious transactions before settlement without adding a separate streaming stack. This Solution Accelerator presents an open-source, end-to-end implementation on Databricks, covering transaction ingestion, Spark Real-Time Mode processing, fraud scoring, Lakebase feature serving, and a Databricks Apps monitoring dashboard. RTM processes streaming workloads in milliseconds and targets sub-300ms latency, while Lakebase supplies a managed, serverless Postgres serving layer with sub-millisecond reads. The design keeps batch ETL, training, streaming, and governance within the same platform. Deployment progresses from a synthetic rate-source quick start to Kafka-based parsing, stateful per-card velocity tracking with TTL, enrichment, weighted scoring, routing, and optional MLflow-trained RandomForest scoring, followed by a Streamlit app for live decisions.


### [Databricks context engineer associate: the industry’s first certification for reliable AI agent systems](https://yomu.fyi/post/databricks-context-engineer-associate-the-industry-s-first-certificati.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Rachel Canetta, James Kantor, Trang Le
- Published: May 19, 2026

Databricks announces the Databricks Certified Context Engineer Associate, described as the industry’s first certification purpose-built for context-aware AI systems. It assesses whether practitioners can design, assemble, and govern the information AI agents receive at inference time, including system prompts, retrieval, memory, external tools, context-window management, and governance. Candidates work with Databricks technologies such as AI Search, Lakebase, MLflow, MCP, and Unity Catalog, while advanced coverage includes multi-agent systems, long-horizon workflows, and evaluation of context decisions. The beta exam will be available free to Data + AI Summit attendees, who may take it once; beta results are expected in 6–8 weeks.


### [The Rosetta stone of CPS: Claroty’s AI-powered library](https://yomu.fyi/post/the-rosetta-stone-of-cps-claroty-s-ai-powered-library.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Ben Hazan, Anton Berlinsky, Ohad Avni, Itay Wagner, Guy Zalcman, Dor Bdolach, Ravid Ariely, Gal Sberro
- Published: May 13, 2026

CPS asset identification is difficult because 88% of CPS assets do not transmit an exact product code and 76% use product codes that differ from the vendor's official records. Claroty's AI-Powered CPS Library addresses this entity-resolution problem with a hybrid architecture that combines classic matching, statistical inference, generative AI, NLP, reasoning agents, and human-in-the-loop review to reconcile protocol data, vendor records, manuals, and vulnerability information. Its Databricks-based lakehouse uses Delta Lake, Unity Catalog, Spark pipelines, Model Serving, MLflow evaluations, Lakebase, and Databricks Apps to maintain governed, auditable mappings across a catalog of more than 17 million assets. The post reports 25% improved vulnerability-attribution accuracy and says early tests gave 56% of analyzed devices new or updated recommendations for outdated firmware, while low-confidence mappings remain subject to expert review.


### [Clinical operations intelligence belongs on the Lakehouse](https://yomu.fyi/post/clinical-operations-intelligence-belongs-on-the-lakehouse.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Nicholas Siebenlist, Surya Sai Turaga
- Published: May 13, 2026

The Site Feasibility Workbench addresses fragmented clinical operations data by placing a site-selection decision-support application, its models, and data on Databricks. Released as a fully open-source Databricks App, it combines a FastAPI backend, React frontend, Unity Catalog-governed data, Lakebase operational state, and AI/BI Genie within the workspace. TA-segmented LightGBM models use the organization’s CTMS, EDC, and IRT history alongside real-world evidence, patient access, qualification, Open Payments, and protocol-execution signals to rank sites. Each prediction stores SHAP attributions in a governed Unity Catalog Delta table, while MLflow tracks training runs and model versions; saved shortlists persist to Lakebase and Genie answers governed-data questions. The six-step workflow supports protocol selection through final shortlisting, including diversity considerations; the release is a decision-support layer rather than a source of record, and deployment reportedly takes about 30 minutes before security review and validation.


### [Using MemAlign to Improve Evaluation of Traditional Machine Learning in Genie Code](https://yomu.fyi/post/using-memalign-to-improve-evaluation-of-traditional-machine-learning-i.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Stepan Nosov, Pavle Martinović, Tejas Sundaresan, Alkis Polyzotis, Nemanja Petrovic
- Published: May 8, 2026

Genie Code’s traditional machine learning workflows are difficult to evaluate because notebook quality depends on code, best practices, and adaptation to data and task. The team built an evaluation pipeline with nine LLM judges, each scoring applicable dimensions—including data exploration, imputation, training, MLflow logging, and cell organization—from 1 to 3, using test cases across classification, regression, and forecasting. On 50 human-graded notebooks, initial judge alignment was weak in several areas, including model training (MAE 0.680) and model use (0.562), with positivity bias contributing to missed technical flaws. Applying MLflow’s MemAlign, which combines generalized semantic guidelines with retrieved episodic examples, reduced judge error by 74–89% on the most important dimensions; removing episodic memory weakened most gains, while data exploration showed a nonsignificant regression.


### [Built in, not bolted on: What AI-native actually means in cybersecurity](https://yomu.fyi/post/built-in-not-bolted-on-what-ai-native-actually-means-in-cybersecurity.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Aly McGue
- Published: Apr 28, 2026

The discussion defines AI-native cybersecurity applications as systems architected with intelligence at their core, rather than traditional products with AI added later. It links tool sprawl to slower threat detection and response and argues that proprietary, context-rich security telemetry is the foundation for adaptive defense. Barracuda uses Databricks to consolidate fragmented data, normalize schemas, support real-time streaming detection, run ML operations through MLflow, and power natural-language log search across billions of security events with strict data isolation. The work began by defining customer outcomes, then progressed through incremental delivery; normalized data enabled models and agents to use cross-domain context. This approach extended across WAF-as-a-service, automated configuration, API security, and bot protection while shared outcomes aligned product, data science, engineering, and business teams.


### [Model risk management in 2026: A banker's guide to the revised interagency guidance](https://yomu.fyi/post/model-risk-management-in-2026-a-banker-s-guide-to-the-revised-interage.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Pavithra Rao, Jennifer Miller, Chaitanya Varanasi, Kim Hatton
- Published: Apr 25, 2026

The post presents the April 17, 2026 revision to model risk management guidance as a shift toward risk-based, principles-driven oversight, replacing several earlier issuances from the Federal Reserve, FDIC, and OCC. It identifies five operational changes: materiality tiering, end-to-end lifecycle governance, reproducible effective challenge, continuous drift and performance monitoring, and application of MRM principles to GenAI and agentic systems. Its proposed response is a Databricks reference architecture built on Unity Catalog, Delta Lake, Lakeflow Declarative Pipelines, Feature Store, MLflow, Model Registry, Model Serving, and assurance tools. The design makes tiering metadata-driven and turns lineage, validation, approvals, monitoring, documentation, and retirement records into evidence generated during normal model work. The stated conclusion is that a unified substrate can reduce integration and manual evidence work, allowing regulatory changes to become configuration exercises rather than multi-quarter programs.


### [AI App Development: Guide To Building AI-Powered Apps](https://yomu.fyi/post/ai-app-development-guide-to-building-ai-powered-apps.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Databricks Staff
- Published: Apr 22, 2026

Production-grade AI app development combines model orchestration, transactional state, governance, security, and live-data integration rather than only interface construction. The guide recommends defining users, outcomes, success metrics, timelines, and AI-relevant journeys, then shipping must-have features before enhancements. It evaluates builders by end-to-end support, technical audience, platform integration, code export, CI/CD, data connections, pricing, and deployment limits. For Databricks teams, it describes Databricks Apps, Declarative Automation Bundles, and Lakebase: serverless app compute, version-controlled deployment, and managed PostgreSQL with synced Unity Catalog Delta tables. It also recommends monitoring outputs, collecting correction feedback, refining prompts with automated evaluations, and governing models through MLflow, concluding that integrated infrastructure can leave teams focused on production AI workflows.


[Older posts](https://yomu.fyi/topic/mlflow/page/2.md)