All the articles with the tag "mlops".
Parquet was built for scans; ML needs random access, fat embedding columns, cheap column backfills, and versioned datasets. A practitioner's look at the Lance format and LanceDB: what the peer-reviewable numbers actually show, where the hype outruns the evidence, and how it changes the dataloader and RAG designs from my earlier posts.
A precise mental model for ML pipeline orchestration—mapping durable backend workflows (Temporal), data schedulers (Airflow, Prefect, Dagster), ML-native pipeline frameworks (Kubeflow, Metaflow, ZenML), and distributed compute engines (Ray). Built for engineers who need to answer 'design an ML pipeline' in interviews. Includes 2025-2026 updates: Airflow 3, KFP v2, Ray 2.x, MLflow 3.
From stateless inference to tool-augmented AI agents. Learn how the Model Context Protocol (MCP), secure sandboxes, and holistic versioning enable the next generation of AI systems.
Standard MLOps advice tells you to learn Git and Docker. But for the next generation of AI Engineers, that's just the baseline. This roadmap focuses on the Infrastructure Round—deep-diving into how data is structured for speed, how it's fed into models, how those models scale across clusters, and how we squeeze every drop of performance out of the silicon.
The unsung hero of modern data processing is how we structure data itself. Learn how Apache Parquet and Apache Arrow solve the fundamental trade-off between storage efficiency and compute speed.