Posts

Deploy new model versions to a small percentage of traffic first and promote or rollback based on real metrics

Ship safer AI apps by teaching models to critique and revise their own outputs using Constitutional AI patterns and Python.

Train ML models across multiple clients without sharing raw data using Flower’s FedAvg strategy and differential privacy

Keep your LLM apps working when inputs exceed context limits using practical token management patterns

Find and fix the bottlenecks slowing down your model training by monitoring GPU compute, memory, and data pipeline throughput

Set up Evidently AI to track data drift, model quality, and deploy automated monitoring for your ML pipelines.

Cut your ML serving images from 8GB to under 2GB with multi-stage builds, slim base images, and smart caching

Serve more concurrent LLM requests on the same GPU by tuning KV cache memory, enabling PagedAttention, and using prefix caching

Turn scanned documents into structured data with LayoutLM’s combined text, layout, and image understanding

Replace slow Pandas pipelines with Polars for fast feature engineering, aggregations, and ML-ready data transforms.

Strip backgrounds from photos with one function call and swap in new scenes using AI generation

Send simple queries to cheap models and complex ones to powerful models automatically with semantic routing

Test your LLM for safety issues before deployment with automated benchmarks for toxicity, bias, refusals, and jailbreak resistance.

Get sub-second LLM responses from Groq’s API with its OpenAI-compatible interface and custom AI accelerator chips

Use the Replicate API to run open-source AI models in the cloud with simple Python calls and pay per second

Turn your trained models into scalable REST APIs with BentoML’s model serving framework and one-click containerization

Set up NVIDIA Triton to serve multiple model formats with dynamic batching, ensembles, and real-time metrics for high-throughput inference.

Master GPU cluster setup with Slurm to run distributed training jobs, manage queues, and scale PyTorch across multiple nodes efficiently.

Train models faster and use less GPU memory by switching to mixed precision with just a few lines of PyTorch code

Connect live data streams to your ML models using Kafka producers, consumers, and stream processing in Python

Turn any photo into art by combining it with the style of famous paintings using deep neural networks in Python

Turn low-resolution images into sharp high-res versions using neural network upscalers that add realistic detail

Call foundation models on AWS Bedrock using Python, with examples for inference, streaming, and RAG.

Cut your Claude API costs in half by batching requests with the Anthropic Message Batches API

Get Claude to cite its sources with exact quotes using the Anthropic citations API in Python

Stop re-uploading documents every request – use the Files API to upload once and reference by file ID

Send images to Claude for analysis, OCR, and visual Q&A using the Anthropic Python SDK with base64 and URL inputs

Get Claude to show its work on hard problems using the extended thinking API with budget tokens

Run high-volume Claude workloads at half price with the Message Batches API — batch creation, polling, and result parsing.

Wire up function calling across multiple turns with Claude, handling tool_use and tool_result messages

Extract data, summarize, and answer questions about PDFs using Claude’s built-in document processing

Cache system prompts, documents, and tool definitions with Anthropic’s prompt caching to slash latency and API costs

Pre-calculate token counts and costs for Claude API calls to manage your budget effectively

Reduce Claude API costs for tool-heavy agents using token-efficient tool mode and schema caching.

Stream Claude responses token-by-token into your UI with the Anthropic Python SDK streaming methods

Wire up Claude’s tool use to build agents that call functions, chain tools together, and handle multi-step tasks

Build multi-model chat apps with a single API using AWS Bedrock Converse for Claude, Llama, and Mistral

Get blazing-fast LLM inference from Cerebras hardware using their OpenAI-compatible Python API

Boost your search pipeline accuracy by reranking results with Cohere’s cross-encoder rerank models

Call DeepSeek’s R1 and V3 models for code and reasoning through their OpenAI-compatible API in Python.

Use the Fireworks AI API to get fast inference from Llama 3.1 70B, Mixtral, and other open models via the OpenAI Python SDK.

Send text, images, videos, and PDFs to Gemini via the Vertex AI SDK with structured JSON output and GCP authentication

Connect to OpenAI’s Realtime API over WebSockets for real-time voice input, text output, and function calling

Call 400+ LLMs from every major provider through a single API key and endpoint with OpenRouter.

Build AI-powered search into your apps using Perplexity’s Sonar models and the OpenAI SDK.

Call Stability AI’s hosted API to generate, edit, and upscale images without managing GPU infrastructure

Deploy open-source LLMs like Llama 3 and Mixtral at scale using Together AI’s fast inference API with Python examples.

Embed text and code with Voyage AI’s Python SDK to build fast similarity search and code retrieval tools

Set up W&B Weave to log every LLM call, track prompt changes, and visualize token costs across experiments

Connect to xAI’s Grok models using the OpenAI SDK with custom base URL for chat and tool use