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🟣 Intermediate Level · Requires Basic Python & ML Awareness
WEEKEND BOOTCAMP | INTERMEDIATE EDITION

6-Week Intermediate AI/ML
Training Program

Deep Learning · Medium AI Concepts · Cloud MLOps · Agentic AI with RAG

  • 🧠 Deep Learning: PyTorch, neural networks, CNNs & Transformer basics
  • 🤖 Medium AI Concepts: RAG, prompt engineering & fine-tuning overview
  • ☁️ Cloud MLOps: SageMaker, Azure ML, Vertex AI — deploy real models
  • 🦾 Agentic AI: LangGraph, tool-calling agents & multi-step pipelines

Sat 3 hrs + Sun 3 hrs = 6 hrs/week | 36 Total Hours

📅 Cohort Start: May 2, 2026

36
Total Hours
6
Weeks
6
POC Projects
$550
Full Price
Built with:
PyTorch
LangGraph
AWS SageMaker
Pinecone / RAG
6-Week Intermediate AI/ML
Sat 3 hrs + Sun 3 hrs = 6 hrs/week
$550
full 6-week program
Registration Open
  • 36 Total Learning Hours
  • 6 Hands-on POC Projects
  • Cloud deployment labs
  • Lifetime recording access
  • Private Discord community
  • Spairo Academy certificate
Limited to 20 participants per cohort
🔒Secure payment via Stripe
0
Total Hours
0
Weeks
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Projects Built
0
Full Price
3
Cloud Platforms
✦ Overview

Who It's For & Goals

Designed for professionals who have Python basics and some ML familiarity. Bridge the gap from beginner to production-ready AI engineer in 6 intensive weekends.

💻

Software Engineers

Add deep learning, RAG pipelines, and agentic AI to your skillset and ship AI-powered features.

📊

Data Analysts

Move beyond dashboards into ML model deployment and cloud AI pipeline engineering.

🎓

Basic AI Graduates

Continue your journey from the Spairo Basic AI/ML program into deeper ML and agentic concepts.

🏢

ML Practitioners

Solidify your cloud deployment and agentic AI skills to move from notebooks to production.

🔄

Backend Engineers

Learn to integrate vector databases, RAG, and AI agents into existing backend services.

🚀

AI Product Builders

Build real AI products with cloud MLOps, multi-agent pipelines, and end-to-end deployment.

✦ Curriculum

6-Week Intermediate Deep Dive

Every week: Saturday (3 hrs) Training + Sunday (3 hrs) Hands-on POC & Project.

WK 1

🧠 Intermediate Python & Deep Learning Foundations

☀️ SATURDAY | 3 Hours | TRAINING
01

Advanced Python — OOP, decorators, async/await & type hints for ML engineering

02

PyTorch fundamentals — tensors, autograd, computational graphs & GPU usage

03

Neural network architecture — layers, activations, loss functions & optimizers

04

MLflow for experiment tracking — logging metrics, params, artifacts & model registry

🌅 SUNDAY | 3 Hours | POC + PROJECT
🔬 HOUR 1–1.5: PROOF OF CONCEPT
Build a 3-layer neural network in PyTorch from scratch, train on a chest X-ray pneumonia detection dataset — log precision, recall & AUC to MLflow
💡 HOUR 2–3: MINI PROJECT
ML Experiment Dashboard — track 5 model variants (with class-imbalance handling) using MLflow UI, compare ROC curves & F1 scores across runs
⚒️ TOOLS
  • PyTorch
  • MLflow
  • CUDA / GPU Colab
  • NumPy
  • Matplotlib
📅 SUMMARY
Day 1:Training + Demo
Day 2:POC + Project
Output:NN + Dashboard
WK 2

🔮 Transformer Architecture & Medium LLM Concepts

☀️ SATURDAY | 3 Hours | TRAINING
01

Attention mechanism deep dive — self-attention, multi-head attention & positional encoding

02

Hugging Face Transformers — loading, fine-tuning, and inference with pre-trained models

03

Advanced prompt engineering — system prompts, few-shot, chain-of-thought & structured output

04

Fine-tuning concepts — LoRA, PEFT overview, when to fine-tune vs. prompt-engineer

🌅 SUNDAY | 3 Hours | POC + PROJECT
🔬 HOUR 1–1.5: PROOF OF CONCEPT
Fine-tune a DistilBERT model on a custom text classification dataset using HuggingFace Trainer API
💡 HOUR 2–3: MINI PROJECT
Customer Sentiment Analyzer — fine-tuned sentiment classifier with a Gradio web UI and live demo
⚒️ TOOLS
  • HuggingFace Transformers
  • PyTorch
  • LoRA / PEFT
  • Gradio
  • WandB
📅 SUMMARY
Day 1:Training + Demo
Day 2:POC + Project
Output:Sentiment App
WK 3

📚 Vector Databases & RAG Pipelines

☀️ SATURDAY | 3 Hours | TRAINING
01

Embeddings — sentence transformers, text embeddings & semantic similarity search

02

Vector databases — Pinecone, ChromaDB & FAISS — indexing, querying & filtering

03

RAG architecture — retrieval-augmented generation end to end with LangChain

04

RAG evaluation with RAGAs — faithfulness, context relevance & answer quality metrics

🌅 SUNDAY | 3 Hours | POC + PROJECT
🔬 HOUR 1–1.5: PROOF OF CONCEPT
Build a semantic search engine on 500 documents with Pinecone — compare keyword vs. vector retrieval quality
💡 HOUR 2–3: MINI PROJECT
Enterprise Document Q&A — RAG system over a company knowledge base with source citation and RAGAs evaluation
⚒️ TOOLS
  • Pinecone
  • ChromaDB
  • LangChain
  • RAGAs
  • Sentence Transformers
📅 SUMMARY
Day 1:Training + Demo
Day 2:POC + Project
Output:Document Q&A
WK 4

☁️ Cloud MLOps — AWS, Azure & GCP (Intermediate)

☀️ SATURDAY | 3 Hours | TRAINING
01

AWS SageMaker — training jobs, model hosting, endpoints & A/B deployment

02

Azure ML Studio — AutoML, pipelines & managed model deployment

03

GCP Vertex AI — custom training, model registry & prediction endpoints

04

MLOps fundamentals — CI/CD for ML, model monitoring, drift detection & retraining

🌅 SUNDAY | 3 Hours | POC + PROJECT
🔬 HOUR 1–1.5: PROOF OF CONCEPT
Deploy a PyTorch model to a SageMaker real-time endpoint and test inference with sample data
💡 HOUR 2–3: MINI PROJECT
Cloud ML Prediction API — FastAPI wrapper around a cloud-deployed model with auth, rate limiting & monitoring
⚒️ TOOLS
  • AWS SageMaker
  • Azure ML Studio
  • Vertex AI
  • FastAPI
  • Docker
📅 SUMMARY
Day 1:Training + Demo
Day 2:POC + Project
Output:Cloud ML API
WK 5

🦾 Agentic AI — LangGraph, Tools & Multi-Step Pipelines

☀️ SATURDAY | 3 Hours | TRAINING
01

LangGraph fundamentals — stateful graph-based agent orchestration & node design

02

Tool calling & function calling — OpenAI tools, Anthropic tool use & Gemini functions

03

Memory management — short-term, long-term (Mem0) & episodic memory for agents

04

Cloud Agentic AI — AWS Bedrock Agents, Azure AI Agent Service & Vertex AI Agents

🌅 SUNDAY | 3 Hours | POC + PROJECT
🔬 HOUR 1–1.5: PROOF OF CONCEPT
Build a LangGraph agent with 4 tools (search, calculator, RAG retrieval, email sender) and persistent memory
💡 HOUR 2–3: MINI PROJECT
Autonomous Research Pipeline — agent that searches, reads papers, synthesizes findings & posts a summary report
⚒️ TOOLS
  • LangGraph
  • Mem0
  • Bedrock Agents
  • Tavily Search
  • FastAPI
📅 SUMMARY
Day 1:Training + Demo
Day 2:POC + Project
Output:Research Agent
WK 6

🛡️ AI Security, Prompt Attacks & Production Capstone

☀️ SATURDAY | 3 Hours | TRAINING
01

Prompt security & OWASP Top 10 for LLMs — prompt injection (direct & indirect), jailbreaking patterns, insecure output handling & training data poisoning

02

AI security anti-patterns — trusting raw LLM output blindly, storing PII in prompts, unbounded tool execution, over-privileged agents & indirect injection via retrieved documents

03

AI security patterns — input sanitisation & intent classification, output validation & schema enforcement, sandboxed tool execution, rate limiting, audit logging & human-in-the-loop checkpoints

04

Guardrail implementation — NeMo Guardrails (topical rails, fact-checking rails), Llama Guard for content moderation, PromptFoo for automated red-teaming & production deployment with Docker & CI/CD

🌅 SUNDAY | 3 Hours | CAPSTONE PROJECT
🔬 HOUR 1–1.5: SECURITY AUDIT + INTEGRATION
Red-team your Week 5 agent with 10 real attack vectors (prompt injection, role-play jailbreaks, indirect injection via RAG docs) — patch each vulnerability using guardrails & input validation
🏆 HOUR 2–3: CAPSTONE DELIVERY
Secure Production AI Application — agentic RAG system with guardrail layer, input/output validation, audit logging, containerised with Docker, deployed to cloud with CI/CD pipeline & security test report
⚒️ TOOLS
  • NeMo Guardrails
  • Llama Guard
  • PromptFoo (red-teaming)
  • LangSmith (tracing)
  • Docker / GitHub Actions
📅 SUMMARY
Day 1:Security Deep-Dive
Day 2:Red-Team + Capstone
Output:Secure Production App
✦ Learning Outcomes

Six Weeks, Six Milestones

Build progressively — each week feeds directly into the next until you ship a production-grade AI application.

WK 1

Deep Learning Foundations

PyTorch neural networks trained and tracked with MLflow experiment dashboards

WK 2

Transformer Mastery

Fine-tuned transformer model with Gradio UI deployed for live inference

WK 3

RAG Pipelines

Enterprise document Q&A system with vector search, source citation & quality evaluation

WK 4

Cloud MLOps

Model deployed to 3 cloud platforms — SageMaker, Azure ML & Vertex AI prediction API

WK 5

Agentic AI Pipeline

Multi-tool LangGraph agent with persistent memory and autonomous task execution

WK 6

🏆 Production Capstone

Fully deployed, monitored, and containerized AI application — ready for portfolio & interviews

✦ Tools & Technologies

The Intermediate AI/ML Stack

Industry-standard tools used by mid-to-senior AI engineers at leading tech companies.

PyTorch
HuggingFace Transformers
MLflow
WandB
LoRA / PEFT
Gradio
Pinecone
ChromaDB
LangChain
LangGraph
RAGAs
AWS SageMaker
Azure ML Studio
GCP Vertex AI
FastAPI
Docker
Mem0
LangSmith
🔥PyTorch
🤗HuggingFace
📦Pinecone
🔗LangGraph
☁️AWS SageMaker
🔷Azure ML
🌐Vertex AI
📊MLflow / WandB
🧠RAG Pipelines
🐳Docker / CI/CD
🔬RAGAs Evaluation
🦾AI Agents
🔥PyTorch
🤗HuggingFace
📦Pinecone
🔗LangGraph
☁️AWS SageMaker
🔷Azure ML
🌐Vertex AI
📊MLflow / WandB
🧠RAG Pipelines
🐳Docker / CI/CD
✦ Pricing

Transparent, All-Inclusive Pricing

One price for the full 6-week intermediate program. No extras, no surprises.

✦ FAQ

Frequently Asked Questions

Everything you need to know about the Intermediate AI/ML Training program.

You should be comfortable with Python basics — variables, functions, loops, and ideally some NumPy/Pandas exposure. Completing the Spairo Basic AI/ML program or equivalent is recommended.
No local GPU is required. We use Google Colab Pro and cloud GPU instances (provided via lab credits) for training workloads. A standard laptop is sufficient for all non-training exercises.
The Intermediate program goes deeper — PyTorch deep learning, transformer fine-tuning, vector databases, cloud MLOps, and full agentic AI pipelines. It's designed for professionals who want to build and deploy real AI systems, not just call APIs.
Yes — Intermediate graduates receive a discount toward the Advanced AI/ML program (12-week, $850). The advanced track covers LLM training from scratch, RLHF, multi-agent orchestration, and production deployment at scale.
We work with free-tier and trial credits across AWS, Azure, and GCP which are sufficient for all lab exercises. You may incur minimal costs (usually under $10 total) if you exceed free tier limits, which we help you manage during the program.

Ready to Level Up Your AI/ML Skills?
Start May 2, 2026.

Join the Summer 2026 Intermediate AI cohort. From deep learning to production agents — in 6 weekends.

✉️ Ask a Question 💬 WhatsApp Us
$550
Full 6-week program