Tagged articles

data versioning

7 articles · Page 1 of 1

Oct 1, 2026 · Operations

Beyond v2_final: Production-Grade ML Data Versioning & Experiment Tracking with DVC + MLflow

This article demonstrates a complete, reproducible ML pipeline using DVC for data versioning and MLflow for experiment tracking, covering immutable data snapshots, time-based splits, pipeline definitions, model registration gates, canary deployment, rollback procedures, CI integration, and retention policies — all validated with concrete code and failure-mode analysis.

CI/CDDVCMLOps

0 likes · 60 min read

Beyond v2_final: Production-Grade ML Data Versioning & Experiment Tracking with DVC + MLflow

Data Bricklaying Diary

Sep 2, 2026 · Artificial Intelligence

Why Random Splits Fail for Enterprise Data: Preventing Time, Entity & Business Leakage

This article explains why random row-based splitting creates false generalization in enterprise ML, detailing how to define prediction timepoints, combine business entity grouping with time windows, and implement five-layer leakage checks to ensure test data truly simulates unseen production data.

ML evaluationbusiness groupingdata leakage

0 likes · 23 min read

Why Random Splits Fail for Enterprise Data: Preventing Time, Entity & Business Leakage

Data Bricklaying Diary

Aug 18, 2026 · Artificial Intelligence

Semantic Models ≠ Live State: Why Agents Need Object Runtime

This article distinguishes semantic models (which define business object meanings) from object runtime (which provides traceable, versioned projections of specific object states for AI agents), detailing five core responsibilities, differences from data platforms and agent runtimes, a credit-adjustment case study, and guidance on when and how to implement minimal object projections.

Agent architectureCDCMCP

0 likes · 21 min read

Semantic Models ≠ Live State: Why Agents Need Object Runtime

Data Bricklaying Diary

Jul 23, 2026 · Artificial Intelligence

Operating High-Quality Datasets as Continuous Data Products for AI

This article presents a six-step framework for operating high-quality datasets as continuous data products, covering responsibility assignment, version baselines, quality and AI effect monitoring, feedback-to-candidate pipelines, controlled release strategies, and retirement mechanisms to ensure datasets evolve with business, models, and risk boundaries.

AI data productsDataOpsLLMOps

0 likes · 14 min read

Operating High-Quality Datasets as Continuous Data Products for AI

Data STUDIO

Dec 3, 2025 · Artificial Intelligence

Pixeltable: One Table to Power Multimodal AI with Declarative Python

Pixeltable introduces a unified table abstraction that treats images, text, embeddings and model outputs as columns, enabling declarative multimodal AI pipelines, eliminating glue code, supporting built‑in vector indexing, versioned experiments, extensible custom functions, and a concise 30‑line RAG implementation.

PixeltablePythonRAG

0 likes · 15 min read

Pixeltable: One Table to Power Multimodal AI with Declarative Python

DeWu Technology

Dec 11, 2024 · Artificial Intelligence

MLOps Practices for Improving Order Fulfillment Timeliness

The supply‑chain team leveraged core MLOps practices—versioning, testing, automated reproducible pipelines, deployment monitoring, and documentation—to eliminate data leakage, ensure online consistency, and accelerate model upgrades, using traffic‑replay, FAAS‑based decoupling, and approval workflows, ultimately cutting order‑fulfillment times, reducing costs, and enabling business teams to adopt reliable AI models at scale.

MLOpsautomationdata versioning

0 likes · 18 min read

MLOps Practices for Improving Order Fulfillment Timeliness

ITPUB

Jun 5, 2018 · Databases

How T‑TDSQL Enables Full‑Temporal Data Management in Distributed Databases

T‑TDSQL, a temporal extension of Tencent's TDSQL, introduces a full‑temporal data model, scalable distributed architecture, MVCC‑based history visibility algorithms, and rich temporal features that address financial audit, data lineage, and high‑performance analytics for massive historical datasets.

Distributed DatabaseMVCCTDSQL

0 likes · 15 min read

How T‑TDSQL Enables Full‑Temporal Data Management in Distributed Databases