Posts

Create an agent that automatically diagnoses Python errors by analyzing stack traces and reading source code

Find the best chunking strategy for your RAG pipeline by benchmarking four methods side by side.

Detect layout changes, text edits, and added or removed sections between two document versions using OpenCV and PaddleOCR

Create a document QA agent that parses PDFs, chunks text, and answers questions with citations

Pull structured table data from PDFs and images using Table Transformer and OpenCV preprocessing

Automate feature generation from relational data with Featuretools DFS, custom primitives, and feature selection

Rank and select the most predictive features for your ML models using scikit-learn’s feature importance tools

Create a filesystem agent with OpenAI tools that manages local files through a secure, sandboxed agent loop.

Create an LLM-powered financial agent that pulls live market data and produces actionable analysis

Automate GitHub issue triage with an LLM-powered agent that classifies bugs, assigns priority labels, and routes to the right team.

Combine BM25 and vector search with Reciprocal Rank Fusion to get better results than either approach alone.

Extract grammatically correct keyphrases from documents using POS-pattern vectorizers instead of fixed n-gram windows

Turn unstructured documents into a structured knowledge graph you can query, using GPT-4o for triple extraction

Detect and overlay driving lanes in video feeds with OpenCV classical methods and YOLOv8 segmentation models

Detect any language and translate it to English or other targets using lingua and MarianMT in a single FastAPI service.

Extract legal entities like case citations, statutes, and parties from text using Transformers and spaCy

Create a log analysis agent that uses LLMs and regex tools to find patterns, errors, and anomalies in log files

Turn recorded meetings into action items and summaries using Whisper transcription and LLM agents

Give your AI agent a real memory system that stores, searches, and recalls past conversations using vector embeddings.

Ship ML models confidently by A/B testing them with a FastAPI traffic-splitting framework

Speed up ML model deployments with a two-tier cache that pulls from S3 and falls back to local disk storage.

Distribute ML model files globally with CloudFront caching, signed URLs, and automated S3 uploads with boto3

Stop paying to store abandoned checkpoints and failed experiments by building an automated artifact GC pipeline on S3.

Push and pull ML model files through container registries with ORAS for versioned, cached distribution

Ensure model integrity and provenance by cryptographically signing and verifying model files before deployment

Run HuggingFace model predictions on large Parquet datasets with Ray Data parallelism and write results back efficiently

Automatically decide whether to promote or rollback a canary model using Mann-Whitney U, KS tests, and effect sizes

Automate model training and evaluation in CI with GitHub Actions, DVC pipelines, and CML reports

Shrink your PyTorch models dramatically by chaining magnitude pruning with quantization in a single pipeline.

Stop hardcoding hyperparameters and use Hydra to manage model configs, run sweeps, and track experiments cleanly

Scan Python ML environments for CVEs, pin safe versions, and automate vulnerability checks in CI pipelines

Automate ML model deployments to SageMaker with Terraform configs you can version and reproduce

Detect data and prediction drift in production ML models using Evidently reports served through a FastAPI monitoring API

Distribute ML inference traffic across multiple model servers with NGINX, FastAPI, and Docker Compose

Combine MLflow for experiment logging with DuckDB for SQL analytics to find your best model configurations fast

Create a self-service dashboard where stakeholders can explore model predictions and feature importance

Serve ML features at sub-millisecond latency using Redis as an online feature store with a FastAPI interface

Ship new models safely with percentage-based routing, real-time metrics, and automated promotion or rollback logic.

Create a self-hosted model health dashboard with FastAPI, SQLite, and simple HTML charts

Cut LLM inference costs by caching semantically similar requests with Redis and locality-sensitive hashing

Instrument your model serving layer to record token counts, compute costs per request, and alert when spending spikes

Process ML inference requests asynchronously with Celery workers and Redis, handling GPU batching and priority queues

Prevent model inference failures by validating request data with Pydantic models and custom validators

Find your model API’s breaking point with Locust load tests and automated performance reports.

Create a self-hosted model registry API that tracks metrics, parameters, and deployment status with SQLite

Instrument a FastAPI model server with prometheus_client and build Grafana dashboards that catch latency spikes and distribution shifts

Set up real-time performance monitoring that sends alerts to Slack when your model metrics drop

Run a production-grade MLflow model registry with PostgreSQL storage, model versioning, stage transitions, and artifact management.

Ship a lightweight model registry on AWS that tracks versions, manages stages, and serves production models without MLflow overhead.

Automatically detect failing ML models in production and roll back to the last known good version