AI & Machine Learning

You've got a powerful language model, but it's not quite tailored to your domain. Full fine-tuning would consume your entire GPU cluster and bankrupt your budget.

So you've built an amazing ML model. It's accurate, it's smart, but it's absolutely massive.

You've probably hit this wall: your LLM inference is fast enough for individual tokens, but generating a 500-token response feels sluggish.

You're running a production LLM serving system. Your 70B model is generating responses beautifully, but your GPU memory is being strangled by KV cache bloat.

You're running an LLM service, and something feels off. When request volume spikes, your GPU utilization drops.

You're building with language models, and suddenly you're dependent on multiple APIs. What happens when OpenAI hits its rate limit?

Your AI infrastructure is bleeding money. You're probably not thinking about it in the right way.

You've just spent three months fine-tuning your language model. The metrics look great in isolation.

You've deployed your LLM application to production. Traffic is growing.

You've built an LLM-powered feature. It works.

You've probably hit that wall: your LLM knows everything about its training data, but nothing about your proprietary documents.

You know that feeling when you ask an LLM a complex question that requires understanding how multiple pieces of information connect?

You're staring at a pile of documents. Some are PDFs with images embedded.

Master the foundational thinking behind machine learning -- from problem framing and the bias-variance tradeoff to the scikit-learn API. Learn to ask the right questions before writing a single line of model code.

Move past accuracy and build a complete evaluation toolkit for classification models. Master confusion matrices, precision-recall tradeoffs, ROC curves, and cost-optimized threshold selection.

Put all the pieces together in a complete, production-ready ML project. Build a churn prediction system from problem definition through deployed FastAPI endpoint, with proper evaluation and monitoring.

Master PyTorch's tensor ecosystem and automatic differentiation engine -- the foundation that makes deep learning work, from creating and manipulating tensors to understanding how gradients flow through computational graphs.

Learn how to build neural networks using PyTorch's nn.Module system -- from defining layers and forward passes to composing complex architectures, initializing weights, and saving models.

Master the PyTorch training loop from the inside out -- choosing loss functions, comparing optimizers like Adam and SGD, implementing learning rate schedules, and building production-ready training pipelines.

Build and train convolutional neural networks for image classification in PyTorch, covering convolution mechanics, pooling, ResNet skip connections, data augmentation, and achieving 90%+ accuracy on CIFAR-10.

Understand how RNNs and LSTMs process sequential data, from the vanishing gradient problem to gated memory cells, with practical PyTorch implementations for classification, generation, and time series forecasting.

Leverage pretrained models to build production-ready classifiers with limited data -- covering feature extraction vs fine-tuning strategies, learning rate scheduling, and domain adaptation techniques in PyTorch.

Explore the transformer architecture that revolutionized NLP, understand BERT vs GPT, and learn to fine-tune pretrained models for text classification using the Hugging Face ecosystem.

Build a complete multi-modal deep learning system that fuses image and text data, combining CNNs, transformers, and fusion strategies into a production-ready project with experiment tracking and API deployment.

Build a RAG pipeline from primitives: text chunking, embeddings, vector storage, and similarity search. Understand each layer so you can diagnose failures and optimize retrieval quality in production.