39 of 39 articles
Conformal prediction wraps any model to produce prediction sets with a proven, distribution-free coverage guarantee. A guide to the mechanism and its...
Data drift and concept drift silently degrade deployed models. Learn how PSI, the KS test, DDM, ADWIN, and label-free estimation detect it...
Speculative decoding speeds up LLM inference: a small draft model proposes tokens that a large target model then verifies in a single,...
A model can be accurate yet report untrustworthy probabilities. Measuring calibration with reliability diagrams, ECE, and Brier score, and fixing it after.
A measured Parquet benchmark on 10 million rows compares zstd, snappy, gzip, and lz4 by file size, write and read speed, dictionary...
How a schema registry enforces versioned data contracts on Kafka: Avro and Protobuf encoding, the 5-byte wire format, and compatibility modes and...
How Apache Spark executes a query: the Catalyst optimizer, AQE, the Tungsten engine and UnsafeRow, the sort-based shuffle, data skew, and join...
Compare Trino, StarRocks, and DuckDB as lakehouse query engines over Iceberg on object storage: architecture, pushdown, caching, and how to choose one.
A vendor-neutral guide to batch vs streaming architectures: Lambda vs Kappa, micro-batch vs event-at-a-time, event time, watermarks, windows, and exactly-once.
Data contracts and quality checks enforce pipeline reliability: ODCS v3.1.0, six quality dimensions, and enforcement across records, warehouses, and streams.
How Apache Parquet stores columnar data on disk and Apache Arrow holds it in memory: file layout, encoding, compression, predicate pushdown, and...
DuckDB vs Polars compared with a reproducible benchmark: measured query times and peak memory on a 10M-row Parquet file, plus Arrow interop...
A 2026 comparison of Apache Iceberg, Delta Lake, and Apache Hudi — architecture, multi-engine reach, and how format convergence is reshaping the...
A practical guide to Kubernetes pods, the networking model that makes them isolated, and why direct connections to a database container in...
Learn how Gaussian Processes provide principled uncertainty quantification for regression, classification, and Bayesian optimization with Python and GPyTorch.
Learn the Central Limit Theorem — why sample means become normal, the math behind it, Python simulations, and applications in ML, A/B...
Learn how Self-Supervised Learning creates powerful pretrained models from unlabeled data with contrastive learning, masked modeling, and PyTorch code.
Learn how Deep SVDD maps normal data to a compact hypersphere for anomaly detection with PyTorch code, math, and real-world industrial applications.
Learn how Genetic Algorithms work — selection, crossover, mutation — and implement a complete GA in Python for optimization, scheduling, and ML...
Learn how dbt transforms raw data into analytics-ready tables with SQL, modular models, testing, and documentation for modern data pipelines.
Learn how Change Data Capture with Debezium streams database changes to Kafka in real-time for event-driven architectures, analytics, and microservices.
Learn how to use Apache Airflow to orchestrate production data pipelines with DAGs, operators, sensors, and scheduling best practices in Python.
Learn how Graph Attention Networks use attention mechanisms on graph data for node classification, link prediction, and recommendation systems with PyTorch code.
Learn how to implement a production Apache Kafka consumer in Python covering consumer groups, offset management, delivery semantics, and error handling.
Learn how to build a production Apache Kafka engine that generates multivariate time series data from server metrics with Python producers and...
Clean code is the discipline of lowering the cost of reading software, where developers spend roughly 58% of their time. A sourced...
Master Git and GitHub best practices including branching strategies, commit conventions, code reviews, and CI/CD workflows used by professional developers.
SVM vs OCSVM explained with inline SVG diagrams: how each works, mathematical foundations, kernel trick, complete Python implementations, real-world use cases, and...
How to manage metadata and time-series data together: facility hierarchies, sensor registries, 4 architecture patterns (TimescaleDB, InfluxDB, Parquet, TDengine), schema design, ingestion...
A category-by-category comparison of databases and storage formats for preprocessed time-series data: TimescaleDB, InfluxDB, ClickHouse, Parquet/DuckDB, Iceberg, and feature stores, with a...
Domain adaptation for time-series anomaly detection: 9 complete Python scripts including model architecture, DANN/MMD/CORAL losses, training loop, and evaluation.
Transfer learning, fine-tuning, and domain adaptation explained: the hierarchy, DANN implementation, and 6 strategies for anomaly detection on heterogeneous cobots.
Move time-series data from InfluxDB into Apache Iceberg on AWS S3 with Telegraf, Glue, and Athena: a complete reference telegraf.conf, automation, and...
Build real-time CEP pipelines with Apache Flink: pattern API, fraud detection, IoT anomaly detection, stock market patterns, Kafka integration, Docker setup, and...
Build REST APIs with FastAPI from scratch: complete CRUD tutorial, Pydantic validation, SQLAlchemy database integration, JWT authentication, testing with pytest, and Docker...
Time-series forecasting in 2026: from classical ARIMA to foundation models like Chronos and TimesFM. Complete guide covering statistical methods, deep learning architectures,...
Time-series anomaly detection methods in 2026: from classical statistical approaches to transformer-based foundation models. Practical guide with model comparison, implementation examples, and...
A practical guide to Docker from development to production: images, containers, registries, Dockerfiles, Compose, networking, volumes, multi-stage builds, and debugging.
Python vs Rust comparison for developers: performance benchmarks, memory safety, ecosystem maturity, learning curve, and practical guidance on when to use each...