Deep dive into neural network architectures including transformers, CNNs, RNNs, attention mechanisms, and modern foundation models.

Internal mechanics of the transformer architecture including self-attention, Q/K/V projections, positional encoding, encoder-decoder variants, and fine-tuning approaches.

Bidirectional Encoder Representations from Transformers. Architecture fundamentals, MLM and NSP pre-training, fine-tuning patterns, and the BERT family of variants.

Generative Pre-trained Transformers. Decoder-only architecture, next-token prediction, scaling laws, RLHF alignment, and the GPT lineage from GPT-1 to GPT-4.

Mixture of Experts architecture. Sparse activation, expert routing mechanisms, load balancing, and notable MoE models including Mixtral and DeepSeek.

Build custom frontier models with Amazon Nova Forge and distributed training infrastructure with SageMaker HyperPod.

CNNs, vision transformers, object detection, segmentation, image classification, and modern computer vision architectures.

Collaborative filtering, content-based filtering, hybrid approaches, ranking algorithms, and personalization strategies.

Classical methods (ARIMA, Prophet) and deep learning approaches (LSTM, Temporal CNNs) for time series prediction.

LoRA, QLoRA, PEFT techniques, model adaptation, transfer learning, and parameter-efficient fine-tuning methods.

MLflow, Weights & Biases, experiment management, reproducibility, hyperparameter tracking, and model comparison.

GPU types, cluster management, resource allocation, scheduling, and infrastructure for training and inference workloads.

TensorRT optimization pipeline, CUDA memory management, stream concurrency, custom kernels, and GPU inference best practices.

Model quantization, TensorFlow Lite, ONNX Runtime, edge hardware (TPUs, NPUs), and deployment patterns for edge devices.

ML lifecycle management, CI/CD for ML, model versioning, experiment tracking, and production ML pipelines.

Inference servers, deployment patterns, model optimization, batching, and scaling strategies for serving ML models in production.

Feature stores, model registries, training infrastructure, MLOps automation, and platform architecture patterns.

Feature engineering platforms for managing, storing, and serving ML features with consistency across training and inference.

Data pipelines, storage layers, data versioning, lineage tracking, and architecture patterns for ML data platforms.

Latency optimization, throughput tuning, quantization, pruning, and performance engineering for ML systems.

Model optimization techniques including quantization, pruning, distillation, batching, caching, and performance tuning for inference.

Unit testing, integration testing, behavioral testing, performance testing, and validation strategies for ML models.

Model monitoring, drift detection, governance, quality gates, observability, and compliance for production ML systems.

ML model deployment strategies including A/B testing, shadow deployment, canary releases, and rollout patterns for production ML.

Production ML monitoring including drift detection, model performance tracking, data quality monitoring, and automated retraining triggers.

Governance frameworks, model documentation, risk management, compliance requirements, and AI policy development.

Differential privacy, federated learning, secure multi-party computation, and privacy-preserving techniques for ML.