-
Orchestration in 2026: Airflow assets and dynamic task mapping vs AWS Step Functions and Lambda, and what to pick for data vs ML
Modern workflow orchestration is no longer just about scheduling a Python script at midnight. Today’s data platforms must coordinate warehouse transformations, react to incoming files, handle failures
-
The data and ML tool map: hello worlds for dbt, Airflow, Spark, MLflow and friends, and when to use each
If you are new to data engineering or machine learning, the modern tool landscape can feel unnecessarily complicated.
-
Data versioning and lineage: reproducing last month’s training set exactly
Imagine this production incident.
-
Inference observability: latency percentiles, token metrics, and drift at the endpoint
An inference endpoint can be healthy by every conventional infrastructure metric and still deliver a terrible user experience.
-
Ridge, lasso, and elastic net: what L1 and L2 actually do to your coefficients
Linear regression is one of the easiest machine learning algorithms to understand—and one of the easiest to get wrong when your features are correlated, noisy, or measured on wildly different scales.
-
Nonparametric tests when your assumptions break: Mann–Whitney, Wilcoxon, Kruskal–Wallis
Imagine you’re analyzing an A/B experiment for an e-commerce platform.
-
Numerical stability: why log-sum-exp exists and how float32 silently lies
A machine learning model produces three logits: 1,000, 1,001, and 999. You apply softmax, expecting three probabilities that sum to one.
-
Training instabilities: loss spikes, gradient clipping, and mixed-precision traps
A training run can look perfectly healthy for thousands of steps and then suddenly produce a loss that is 100 times larger than expected. Sometimes the model recovers. Sometimes gradients become infin
-
Cutting inference cost: caching, routing, and small-model fallbacks
Every request to a large language model has a cost. For a small prototype, that cost may seem insignificant. But production AI applications and chatbots can process millions of requests, often with le
-
Evaluating RAG systems: faithfulness, relevance, and hallucination rate
A retrieval-augmented generation (RAG) system can produce an answer that sounds authoritative, responds directly to the user’s question, and includes citations—while being completely wrong.