Interactive ML Demos & Systems
Playgrounds for machine learning systems and applications.
Explore production-grade machine learning applications. Master ML lineage, AWS failover, and loss function regression through interactive simulations and demos.
Advanced PEFT - LoRA Multi-Adapter Orchestration
Orchestrate multiple LoRA adapters for Dialect Reconstruction, PII Masking, and Sentiment Neutralization. Optimize PEFT workflows on AWS SageMaker.
Dynamic switching between specialized LoRA weights without model reloading.
Native adapters for Dialect Reconstruction, PII Redaction, Sentiment Neutralization, and Style Transfer.
Granular hyperparameter tuning via real-time adjustment of Rank, Alpha, and Target Modules (Attention vs. FF).
Compute-efficient inference optimized for ml.g5.xlarge instances with VRAM telemetry monitoring.
AWS SageMaker integration for Serverless or Multi-Model Endpoints (MME).
Live trace observability via debug adapter retrieval latency and S3 artifact injection logs in real-time.
Looking for Solutions?
- Deploying ML Systems 👉 Book a briefing session
- Hiring an ML Engineer 👉 Drop an email
- Learn by Doing 👉 Enroll AI Engineering Masterclass
Bayesian Demand Modeling & Production MLOps
Scale retail revenue with Bayesian-optimized price elasticity. Features a multi-model ensemble (DLN, LightGBM, SVR), DVC lineage, and AWS Lambda deployment.
Multi-model failover system (PyTorch deep learning model, LightGBM, SVR, Elastic Net).
HPO via Bayesian optimization with Optuna.
Low-latency caching with ElastiCache Redis.
Weekly-scheduled ML lineage management with DVC & Prefect.
Automated data drift and fairness/bias testing (SHAP).
CI/CD integration with GitHub Actions, AWS CodeBuild, and Snyk for security scanning.
SVD Image Compression & PCA Deep Dive
Apply Singular Value Decomposition for Principal Component Analysis. Interactive image compression. Incremental, Randomized, and Kernel PCA.
Interactive SVD Rank Adjustment
Step-by-step Mathematical Derivation of PCA
Comparison of 5 PCA methods (Incremental, Kernel, etc.)
Real-world Telecom Churn Data Simulation
Low-Rank Approximation Visualizations
Visualizing Regression: From Loss Functions to Generalization Bounds
Explore MSE, MAE, L1/L2 regularization, generalization bounds with interactive explorers for loss functions and model complexity.
Interactive Loss Function Explorer (MSE, MAE)
Regularization Strength (λ) Simulator
Comparison of Parametric vs Non-parametric models
Generalization Bound Mathematical Analysis
Real-world Regression Scenarios & MSE Results
Looking for Solutions?
- Deploying ML Systems 👉 Book a briefing session
- Hiring an ML Engineer 👉 Drop an email
- Learn by Doing 👉 Enroll AI Engineering Masterclass
Orchestrating Autonomous Agent Networks
A Python framework for building autonomous agent networks with multi-step reasoning. Automated formation, TaskGraph orchestration, and model-agnostic optimization.
Autonomous Agent Formations (Solo, Supervising, Squad, Random)
Graph-Based Task Execution via TaskGraph (Nodes & Edges)
Model-Agnostic LLM Curation with LiteLLM integration
Advanced Memory Management using mem0ai and Chroma DB
Built-in RAG Support and External Tooling via Composio
Automated Workflow Optimization and Dependency Resolution
