Posts

Autoscale ML model endpoints on Kubernetes using custom Prometheus metrics for inference latency and request queue depth

Set up a production-grade model serving cluster using Ray Serve with Docker containers and autoscaling replicas

Monitor exactly how much each model endpoint costs with custom Prometheus counters and Grafana panels

Route and load-balance ML inference traffic across model replicas with Envoy and gRPC

Serve multiple ML models with automatic routing, load balancing, and TLS using Docker Compose and Traefik.

Serve HuggingFace models as fast APIs using LitServe with batching, GPU acceleration, and Docker deployment.

Serve ML models in production with Ray Serve’s auto-scaling and FastAPI’s request handling combined

Save hours of training time by implementing checkpoints that handle crashes, disk limits, and multi-GPU setups

Build a cost calculator that estimates ML training expenses before you spin up expensive GPU instances

Combine TensorBoard for ML metrics and Prometheus for system metrics into one training monitoring stack you can run locally.

Train and deploy ML models on SageMaker using the Python SDK with managed infrastructure and spot instances

Train HuggingFace models across multiple GPUs using Composer’s FSDP integration, callbacks, and built-in speed-up recipes.

Use Lightning Fabric to add multi-GPU and mixed precision training to your PyTorch code with minimal changes

Create a training job queue that manages GPU resources with Redis, worker pools, and job prioritization

Create a training job scheduler that checks GPU availability, queues jobs by priority, and exposes a REST API for submission

Track, store, and switch between model versions using DVC pipelines backed by S3 remote storage

Cut ML inference cold starts from minutes to milliseconds with preloaded ECS containers that keep models in memory.

Eliminate cold-start latency in ML APIs by warming up models at startup and adding proper health checks with FastAPI.

Create a FastAPI agent that catches Prometheus alerts, pulls relevant metrics, and gets an LLM to explain what went wrong

Get better LLM answers by having multiple agents debate and a judge agent select the winner

Wire up specialist agents that discover, communicate, and delegate tasks over Google’s Agent-to-Agent protocol.

Scale PyTorch training across multiple nodes using Lightning Fabric with NCCL backend communication

Analyze sentiment in any language with a single model using XLM-RoBERTa and Hugging Face Transformers

Step-by-step guide to creating an agent that combines vision capabilities with tool use for image analysis and text extraction.

Link named entities in text to Wikipedia articles using spaCy for NER and cross-encoder models for disambiguation

Stitch images into panoramas with OpenCV’s high-level Stitcher and a manual pipeline using feature matching, homography, and blending.

Learn to build AI agents that break down complex problems, manage dependencies, and automatically replan when things go wrong

Build an end-to-end defect detection system from labeled images to a REST API using YOLOv8 and FastAPI

Turn receipt photos into structured data with PaddleOCR, OpenCV preprocessing, and regex-based field extraction

Extract relationships like works_at and founded_by from text using spaCy, transformers, and LLMs to build knowledge graphs.

Create an autonomous research agent that gathers info from the web and produces structured reports

Extract structured data from resumes using spaCy for entity recognition and Transformers for section classification.

Create an agent that searches your docs, reranks with cross-encoders, and generates grounded answers.

Read text from real-world photos using PaddleOCR’s detection and recognition models with confidence filtering and batch processing

Create an autonomous agent that books meetings, checks availability, and sends invites with OpenAI tool calling

Create search that understands both meaning and mood by combining sentence embeddings with sentiment analysis

Route production traffic to both primary and shadow models concurrently, log results, and decide when to promote the new model

Ship a Slack bot that answers questions, summarizes threads, and takes actions using LLM tool calling

Create a fast spell checker that handles typos, misspellings, and domain-specific terms with Python libraries.

Create a conversational SQL agent with tool calling that inspects schemas, runs read-only queries, and refines results across multiple turns

Ingest and process streaming data for ML with Apache Arrow’s columnar format and zero-copy IPC

Create privacy-safe synthetic datasets that preserve statistical properties using CTGAN and the SDV library

Detect and anonymize names, emails, phone numbers, and custom PII patterns in text with Presidio

Pick the right chunking strategy for your RAG app and stop losing retrieval quality to bad splits

Build accurate text classifiers with minimal labeled data using SetFit’s few-shot learning approach

Cluster text documents into meaningful groups without labeled data using embeddings, UMAP, and HDBSCAN in Python

Create automated text correction systems using rule-based tools, neural models, and LLMs with practical Python examples and performance benchmarks.

Find and remove near-duplicate texts at scale using MinHash fingerprints and LSH for fast similarity search

Create a semantic search system that encodes text into vectors and finds similar documents in milliseconds using FAISS indices.

Classify whether text pairs agree, contradict, or are unrelated using NLI models and Transformers