Workshop Catalog

Overview

Master the fundamentals of high-performance computing in this comprehensive two-day workshop. Learn cluster architecture, job scheduling, parallel programming concepts, and optimization techniques used in modern supercomputing environments.

What You'll Learn

• HPC cluster architecture and components (compute nodes, interconnects, storage)
• Job schedulers: SLURM, PBS, LSF configuration and best practices
• Parallel programming paradigms: MPI, OpenMP, hybrid approaches
• Performance profiling and optimization techniques
• Storage systems: Lustre, GPFS, parallel I/O
• Real-world cluster deployment and troubleshooting

Prerequisites

• Basic Linux command line experience
• Understanding of C or Fortran (helpful but not required)
• Laptop with SSH client

Duration & Format

2 full days (9:00 AM - 5:00 PM each day) • Hands-on labs on AWS ParallelCluster • Continental breakfast and lunch included

Overview

A condensed introduction to high-performance computing covering essential concepts, cluster architecture basics, and hands-on experience with job scheduling. Perfect for those new to HPC or looking for a refresher.

What You'll Learn

• HPC cluster fundamentals and terminology
• Introduction to SLURM job scheduling
• Basic parallel programming concepts
• Performance monitoring essentials
• Hands-on: submitting and managing jobs

Duration & Format

1 full day (9:00 AM - 5:00 PM) • Hands-on labs • Continental breakfast and lunch included

Overview

Deploy production-ready AI systems at enterprise scale. This workshop focuses on real-world constraints: scalability, monitoring, cost optimization, and organizational integration. No toy datasets—only production scenarios.

What You'll Learn

• AI project lifecycle: from POC to production
• Model deployment strategies: REST APIs, batch processing, edge inference
• Monitoring and observability for ML systems
• Cost optimization and resource management
• Data governance and compliance (GDPR, privacy)
• Building business cases and ROI analysis for AI initiatives

Prerequisites

• Basic understanding of machine learning concepts
• Python programming experience
• Familiarity with cloud platforms (AWS, Azure, or GCP)

Duration & Format

2 full days (9:00 AM - 5:00 PM each day) • Deploy real models to cloud infrastructure • Meals included

Overview

Build and manage GPU clusters, distributed training infrastructure, and MLOps pipelines. This advanced workshop covers the infrastructure layer that powers modern AI at scale—from single-node multi-GPU setups to cluster-scale distributed training.

What You'll Learn

• GPU cluster architecture and networking (InfiniBand, RoCE)
• Container orchestration for AI workloads (Kubernetes, Slurm)
• Distributed training frameworks: Horovod, DeepSpeed, PyTorch DDP
• Storage optimization for training data (parallel filesystems, object stores)
• MLOps tooling: MLflow, Kubeflow, Ray
• Performance profiling and optimization for GPU workloads

Prerequisites

• Experience training ML models
• Strong Linux and networking knowledge
• Familiarity with containers (Docker/Singularity)

Duration & Format

2 full days (9:00 AM - 5:00 PM each day) • Hands-on with multi-GPU cloud instances • Meals included

Overview

Master the art and science of prompt engineering for large language models. Learn systematic approaches to crafting effective prompts, from basic techniques to advanced strategies for complex tasks.

What You'll Learn

• Fundamentals: zero-shot, few-shot, chain-of-thought prompting
• Advanced techniques: ReAct, tree-of-thought, self-consistency
• Prompt templates and systematic evaluation
• Domain-specific prompting (code generation, data extraction, reasoning)
• Multi-model strategies and cost optimization
• Debugging and iteration frameworks

Prerequisites

• Basic understanding of LLMs (ChatGPT, Claude, etc.)
• No coding required, but helpful for advanced sections

Duration & Format

1 full day (9:00 AM - 5:00 PM) • Hands-on exercises with GPT-4 and Claude • Meals included

Overview

Design and implement end-to-end AI pipelines from data ingestion through model serving. Learn orchestration, versioning, monitoring, and automation for production ML workflows.

What You'll Learn

• Pipeline architecture patterns and best practices
• Data versioning and feature stores (DVC, Feast)
• Workflow orchestration: Airflow, Prefect, Dagster
• Model versioning and experiment tracking (MLflow, W&B)
• CI/CD for ML: testing, validation, automated deployment
• Monitoring data drift and model performance

Prerequisites

• Python programming experience
• Experience training ML models
• Basic understanding of Docker and cloud platforms

Duration & Format

2 full days (9:00 AM - 5:00 PM each day) • Build complete pipeline from scratch • Meals included

Overview

Learn to fine-tune large language models for domain-specific tasks using parameter-efficient methods like LoRA. Hands-on workshop covering data preparation, training, evaluation, and deployment.

What You'll Learn

• When to fine-tune vs. prompt engineering vs. RAG
• Dataset preparation and quality assessment
• Parameter-efficient fine-tuning: LoRA, QLoRA, adapters
• Training techniques: learning rates, batch sizes, hyperparameter tuning
• Evaluation metrics and testing strategies
• Deployment and serving fine-tuned models

Prerequisites

• Strong Python and PyTorch/Hugging Face experience
• Understanding of transformers and LLM architecture
• Access to GPU instance (we'll provide cloud credits)

Duration & Format

1 full day (9:00 AM - 5:00 PM) • Fine-tune open-source models on real tasks • Meals included

Overview

Essential Linux skills for AI/ML practitioners. Learn to navigate remote servers, manage environments, optimize workflows, and troubleshoot common issues in AI development environments.

What You'll Learn

• Linux fundamentals: filesystem, permissions, processes
• SSH, tmux, screen for remote development
• Package management and dependency resolution
• Environment management: virtualenv, conda, Docker
• GPU monitoring and resource management
• Shell scripting for automation and batch jobs

Prerequisites

• Basic computer literacy
• Interest in AI/ML development
• Laptop (any OS, we'll provide cloud Linux instances)

Duration & Format

1 full day (9:00 AM - 5:00 PM) • Hands-on exercises on cloud Linux servers • Meals included

Overview

Understand attack vectors, vulnerabilities, and defenses for AI systems. Learn to secure models, protect training data, prevent adversarial attacks, and ensure privacy compliance.

What You'll Learn

• Threat modeling for ML systems
• Adversarial attacks: evasion, poisoning, model extraction
• Defense mechanisms and robustness testing
• Privacy-preserving ML: differential privacy, federated learning
• Prompt injection and LLM security vulnerabilities
• Compliance: GDPR, CCPA, industry-specific regulations

Prerequisites

• Understanding of ML model development
• Python programming experience
• Basic security concepts helpful but not required

Duration & Format

1 full day (9:00 AM - 5:00 PM) • Hands-on attack and defense exercises • Meals included

Overview

Design and build autonomous AI agents that can plan, use tools, and execute multi-step tasks. Learn agent architectures, frameworks, and best practices for production deployment.

What You'll Learn

• Agent architectures: ReAct, Plan-and-Execute, Reflexion
• Tool use and function calling with LLMs
• Memory systems: short-term, long-term, vector stores
• Agent frameworks: LangChain, AutoGPT, LlamaIndex
• Multi-agent systems and orchestration
• Safety constraints and guardrails for autonomous agents

Prerequisites

• Python programming experience
• Understanding of LLMs and prompt engineering
• API integration experience helpful

Duration & Format

1 full day (9:00 AM - 5:00 PM) • Build working agents with real tool integrations • Meals included

Overview

Learn professional audio/video production techniques for technical content. Covers live streaming, event recording, multi-camera setups, and post-production workflows for educational and conference content.

What You'll Learn

• Camera selection and setup for technical presentations
• Audio: microphones, mixers, and avoiding common pitfalls
• Live streaming platforms and workflows (YouTube, Twitch, custom RTMP)
• Multi-camera switching and scene composition
• Screen capture and presentation integration
• Post-production: editing, color grading, audio cleanup

Prerequisites

• Basic computer skills
• Interest in content creation or event production
• No AV experience required

Duration & Format

1 full day (9:00 AM - 5:00 PM) • Hands-on with professional equipment • Meals included