We implement parametric and non-parametric ML models using optimized pipelines:

• Supervised Learning:
• Regression: OLS, Ridge/L2, Lasso/L1, ElasticNet via scikit-learn, statsmodels.
• Classification: Logistic Regression, Decision Trees, Random Forests, SVM (linear/kernelized), Gradient Boosted Trees (XGBoost, LightGBM, CatBoost), ensemble voting/stacking strategies.
• Unsupervised Learning:
• Clustering: K-Means++, DBSCAN, Gaussian Mixture Models, Spectral Clustering.
• Dimensionality Reduction: PCA, LDA, ICA, t-SNE, UMAP for low-rank manifold embedding.
• Semi-Supervised / Self-Supervised:
• SSL pipelines using pseudo-labeling, consistency regularization, and contrastive objectives with SimCLR or BYOL.
• AutoML:
• Integration of automated search frameworks (H2O AutoML, Google AutoML, Auto-Sklearn) with hyperparameter tuning via Optuna/Hyperopt/BayesSearchCV.

We construct and train neural networks tailored to the modality and data regime:

 Computer Vision:

• CNN Architectures:
• Transfer learning from ImageNet-pretrained backbones (e.g., EfficientNet, DenseNet, MobileNet).
• Custom top-layer classifiers for multi-class/multi-label outputs.
• Object detection with YOLOv5/v8, SSD, Faster R-CNN.
• Instance segmentation via Mask R-CNN, DeeplabV3+.
• OCR Pipelines:
• CRNN + CTC loss pipelines, or transformer-based OCR using TrOCR/Donut.

• Transformer Architectures:
• Encoder-only (BERT, RoBERTa), decoder-only (GPT-2/3, LLaMA), encoder-decoder (T5, FLAN-T5).
• Fine-tuning on custom corpora with HF Trainer, PEFT adapters (LoRA, QLoRA).
• Tokenization strategies: WordPiece, SentencePiece, Byte-Pair Encoding (BPE).
• Task-Specific:
• Seq2Seq (summarization, translation), NER (CRF tagging), sentiment classification, RAG for long-context QA using LangChain + FAISS or Weaviate.

Time-Series & Forecasting:

• Classical Methods: ARIMA/SARIMA, Holt-Winters exponential smoothing.
• Deep Learning Models: LSTM, GRU, Temporal CNNs, Transformer-based models (Informer, Autoformer, DeepAR).

End-to-end orchestration of reproducible, modular, and monitorable ML workflows:

• Pipeline Orchestration:
• DAG-based orchestration using Kubeflow Pipelines, Apache Airflow, or Prefect for data preprocessing, training, validation, and deployment stages.
• Experiment Tracking & Reproducibility:
• Metadata tracking with MLflow, Weights & Biases, DVC, including artifact lineage and dataset version control.
• Model Registry & Promotion:
• Structured staging (Staging, Production, Archived) with MLflow Registry, SageMaker Model Registry, or Vertex AI Model Management.
• Containerized Serving:
• Model inference endpoints with Triton Inference Server, TorchServe, TF Serving, or FastAPI w/ Docker on Kubernetes (K8s).
• gRPC + REST endpoints with autoscaling pods, GPU-aware schedulers via KEDA.
• Drift & Degradation Monitoring:
• Data, concept, and model drift detection using Evidently, WhyLabs, and alerting integrated with Prometheus + Grafana or ELK Stack.

Maximize throughput and minimize training time for large-scale models:

• Multi-GPU / Multi-Node Training:
• Frameworks: PyTorch DDP, HuggingFace Accelerate, DeepSpeed, Horovod (TensorFlow).
• Mixed precision via NVIDIA Apex / AMP.
• Model Compression:
• INT8/FP16 quantization, structured/unstructured pruning, distillation to student networks.
• Deployment via ONNX, OpenVINO, TensorRT for latency-critical environments.
• Edge Inference:
• Compile & deploy quantized models on NVIDIA Jetson Nano/Xavier, Coral Edge TPU, Apple CoreML, or TensorFlow Lite for microcontrollers.

Deploy transformer-based and retrieval-augmented generation (RAG) systems for enterprise-scale reasoning tasks:

• LLM Fine-Tuning & PEFT:
• Low-Rank Adaptation (LoRA/QLoRA), adapter-based finetuning with transformers, peft, bitsandbytes.
• RAG Architectures:
• Embedding models (OpenAI, Cohere, E5, InstructorXL) + vector search (FAISS, Weaviate, Pinecone).
• Chained prompts and memory routing via LangChain or LlamaIndex.
• Multimodal AI:
• Visual Question Answering (VQA) with BLIP/CLIP.
• Diffusion-based generation via Stable Diffusion, ControlNet, and ComfyUI pipelines.
• Code generation via Codex, CodeT5, StarCoder, or LlamaCode.

Embed intelligent behavior across enterprise systems:

• Inference APIs:
• Async inference endpoints via FastAPI or Triton behind API Gateways (AWS, Kong, Gloo).
• 3rd-Party AI Integrations:
• GPT/OpenAI API, AWS Bedrock, Google Vertex AI APIs, Azure Cognitive Services.
• Speech: Whisper, AssemblyAI, Google STT/TTS.
• RPA/AI integration with UiPath, Automation Anywhere using ML endpoints.