// Machine Learning

Time Series Forecasting: ARIMA, LightGBM, and LSTM Compared

Time series has seasonality, trend, and temporal dependencies that standard ML ignores. Here is when to use ARIMA vs. LightGBM lag features vs. LSTM — and the critical mistake of random data splits.

May 18, 2026

7 min read

Mahmudul Haque Qudrati

CEO & ML Engineer

// Machine Learning

How Product Teams Can Work Effectively With Machine Learning

What ML can and cannot do for your product, how to write an ML spec, how to evaluate model readiness, and what PMs consistently get wrong working with data scientists.

May 18, 2026

8 min read

Mahmudul Haque Qudrati

CEO & ML Engineer

// Machine Learning

The 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.

May 18, 2026

9 min read

Mahmudul Haque Qudrati

CEO & ML Engineer

// Machine Learning

Building 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.

May 18, 2026

9 min read

Mahmudul Haque Qudrati

CEO & ML Engineer

// Machine Learning

LLM 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.

May 18, 2026

10 min read

Mahmudul Haque Qudrati

CEO & ML Engineer

// Machine Learning

Building 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.

May 18, 2026

10 min read

Mahmudul Haque Qudrati

CEO & ML Engineer

// Machine Learning

Data 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.

May 18, 2026

9 min read

Mahmudul Haque Qudrati

CEO & ML Engineer

// Machine Learning

ML 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.

May 18, 2026

9 min read

Mahmudul Haque Qudrati

CEO & ML Engineer

// Machine Learning

ML 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.

May 18, 2026

9 min read

Mahmudul Haque Qudrati

CEO & ML Engineer

// Machine Learning

A/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.

May 18, 2026

9 min read

Mahmudul Haque Qudrati

CEO & ML Engineer

// Machine Learning

Reinforcement 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.

May 18, 2026

9 min read

Mahmudul Haque Qudrati

CEO & ML Engineer

// Machine Learning

ML 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.

May 18, 2026

9 min read

Mahmudul Haque Qudrati

CEO & ML Engineer

Our Blog

Recent Articles

Time Series Forecasting: ARIMA, LightGBM, and LSTM Compared

How Product Teams Can Work Effectively With Machine Learning

The Complete Neural Network Training Guide: From Data to Deployed Model

Building a Dataset for ML: What Makes Good Training Data

LLM Fine-Tuning in Practice: A Developer's Complete Walkthrough

Building Semantic Search: Finding Results by Meaning, Not Keywords

Data Labeling Guide: How to Annotate Training Data Efficiently and Accurately

ML Deployment Patterns: From REST API to Edge Inference

ML Model Compression: Pruning, Quantization, and Knowledge Distillation

A/B Testing ML Models in Production: What's Different and How to Do It Right

Reinforcement Learning for Software Developers: A Practical Guide

ML Monitoring in Production: Detecting Data Drift Before It Breaks Your Model