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9 min readThe Complete Neural Network Training Guide: From Data to Deployed Model
A full practical walkthrough of training a neural network - data prep, architecture selection, optimizer config, common failure modes, and getting to production.
9 min readBuilding a Dataset for ML: What Makes Good Training Data
Data quantity requirements, labeling strategies, augmentation techniques, the data flywheel, and how production models generate their own training data.
10 min readLLM Fine-Tuning in Practice: A Developer's Complete Walkthrough
Dataset preparation, LoRA hyperparameters, Unsloth for faster training, evaluation against the base model, and avoiding catastrophic forgetting and overfitting.
10 min readBuilding Semantic Search: Finding Results by Meaning, Not Keywords
How semantic search works, embedding-based architecture, pgvector vs ChromaDB, hybrid search with BM25, and cross-encoder re-ranking for better results.
9 min readData Labeling Guide: How to Annotate Training Data Efficiently and Accurately
Labeling tools, quality control with inter-annotator agreement, active learning to cut labeling costs by 60-80%, and programmatic labeling with Snorkel.
9 min readML Deployment Patterns: From REST API to Edge Inference
REST APIs, batch inference, streaming, edge deployment, model serving frameworks, and canary deployments for safely rolling out new model versions.
9 min readML Model Compression: Pruning, Quantization, and Knowledge Distillation
How to make models smaller and faster for production using pruning, quantization (FP32 to INT8), knowledge distillation, ONNX conversion, and when full precision is necessary.
9 min readA/B Testing ML Models in Production: What's Different and How to Do It Right
Why ML A/B tests differ from standard software tests, novelty effects, multi-armed bandits, shadow mode testing, and how to measure model impact rigorously.
9 min readReinforcement Learning for Software Developers: A Practical Guide
What RL is, where it works, where it fails, RLHF for LLM alignment, and how to decide between RL and supervised learning for your specific problem.
9 min readML Monitoring in Production: Detecting Data Drift Before It Breaks Your Model
Why models degrade, what to monitor, detecting drift with statistical tests, automated retraining triggers, and tools like Evidently AI and Arize.
10 min readComputer Vision for Software Developers: What You Can Build Today
Using pretrained models for classification, detection, OCR, and segmentation. APIs vs local inference. When to fine-tune vs use a multimodal LLM. CLIP for image search.
8 min readBERT Explained for Developers: What It Is, How It Works, and When to Use It
BERT introduced bidirectional context to NLP in 2018. Here is what that means, how it differs from GPT, and when to reach for it over a modern LLM API.