Pallavi A.

Stop paying for $3,000 "RAG" bootcamps.

Qdrant just put a full, production-grade vector search course on YouTube.

For free.

This isn't a demo. It's a 7-day sprint where the final project is to ship a complete, production-ready documentation search engine.

The full curriculum for real engineers:
➡️ Day 1
• Get on Qdrant Cloud & build your first basic vector search.
➡️ Day 2
• Master Points, Vectors, Payloads, & Chunking.
• Project: Build a Semantic Movie Search.
➡️ Day 3
• Learn HNSW Indexing fundamentals.
• Project: Benchmark HNSW for actual recall vs. latency.
➡️ Day 4:
• Master Hybrid Search (sparse + dense) with score fusion.
• Project: Build a Hybrid Search Engine that actually finds keywords.
➡️ Day 5:
• Learn Vector Quantization to slash memory costs.
• Master high-throughput ingestion & accuracy with rescoring.
• Project: Quantization Performance Optimization.
➡️ Day 6:
• Use Multivectors for advanced reranking.
• Learn the Universal Query API.
• Project: Build a Recommendation System.
➡️ Day 7:
• Final Project: Synthesize all 6 days to ship a production-ready doc search.

➡️ Bonus:
➕  Full integration guides for LlamaIndex, Tensorlake, camelAI, Jina AI, Unstructured(dot)io, and more.

This is the syllabus that separates the "demo builder" from the "production engineer."

This is how you build RAG that actually scales. (I will put the playlist in the comments.)

♻️ Repost to save someone $$$ and a lot of confusion.
✔️ You can follow Pallavi, for more insights.

Training a model is easy. Keeping it alive in production? That’s MLOps.

Most courses stop at “model trained successfully ✅”.

This one begins after that point, where the real work starts.

The free MLOps with Databricks course skips the theory and dives straight into production.

10 short, hands-on lectures covering:
▫️MLOps fundamentals
▫️Databricks workflows
▫️MLflow tracking & model registry
▫️Serving architectures
▫️Endpoint deployment
▫️CI/CD & monitoring

Built on Databricks + MLflow - the same tools teams use in real production pipelines.

If you’ve ever said “it works on my machine,” this course helps you make it work everywhere.

🎥 MLOps with Databricks - Free Edition by Maria Vechtomova & Başak Tuğçe Eskili 💪

Free on YouTube. Practical from the first lecture.
The link is in the comments :)

♻️ Repost to help others learn from this course too.

Follow Pallavi for more insights on AI/ML :)

This Stanford playlist is 4 years old. And it's still better than 99% of the AI courses sold for $2,000 in 2025.

The entire 60-lecture series is on YouTube. For $0.

This is CS224W: Machine Learning with Graphs.

Why is a 4-year-old course still the GOAT?
1️⃣ Foundations > Hype - It teaches the fundamental math of GNNs, PageRank, and Embeddings. This stuff doesn't change.
2️⃣ Taught by a Legend - Jure Leskovec's lectures are timeless. Clear, deep, and practical.
3️⃣ Still the Blueprint - 90% of real-world graph AI (recsys, knowledge graphs, fraud detection) runs on the architectures taught right here.

This is what Big Tech actually uses:
• Google uses GNNs for search and maps.
• Amazon uses them for recommendations.
• Pinterest uses them for PinSAGE.
• Pharma uses them to discover new drugs.

What I learned in the first 3 lectures:
➕ Why all my past "ML" projects were basic (they treated data as tables, not networks).
➕ How "embeddings" (Node2Vec) actually capture the structure of a network.
➕ The exact math behind Graph Neural Networks (GNNs) from the ground up.

If you want to understand connected data, this course is the ultimate resource.
(I’ll drop the playlist link in the comments.)


♻️ Repost to save someone $$$ and a lot of confusion.
✔️ You can follow Pallavi, for more insights.

Context Engineering

◾ The system that gathers and assembles all information an AI needs to understand, reason, and act on a user's request.
◾ It’s the core discipline that moves an AI from a simple reactive chatbot to an autonomous agent that can solve complex problems.

📌 𝐓𝐡𝐞 𝐂𝐨𝐦𝐩𝐨𝐧𝐞𝐧𝐭𝐬 𝐨𝐟 𝐂𝐨𝐧𝐭𝐞𝐱𝐭

The user's input (the "prompt") is just one small piece. The final "Prompt" (Box 5 in the diagram) is a rich bundle of information:

[1.] 𝐒𝐞𝐬𝐬𝐢𝐨𝐧 𝐂𝐨𝐧𝐭𝐞𝐱𝐭
◾ User Input: The original query that kicks off the process (Box 1).
◾ Chat History: What was just discussed? This is held in Short-Term Memory (Box 7) to maintain a coherent conversation.

[2.] 𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐞𝐝 𝐂𝐨𝐧𝐭𝐞𝐱𝐭
◾ RAG Context: Information pulled from Long-Term Memory (Box 3). These are facts, documents, and data retrieved via vector search to ground the agent.
◾ Tool/Schema Context: Definitions of the "Action Tools" (Box 4) the agent can use. It needs to know what it can do (e.g., run code, search the web, use an API).

[3.] 𝐈𝐧𝐭𝐞𝐫𝐧𝐚𝐥 𝐂𝐨𝐧𝐭𝐞𝐱𝐭
◾ Agent Reasoning: The agent's "inner monologue" or "chain of thought" (Box 2). This is where it makes decisions and coordinates its plan.
◾ User Info: Static data about the user, like preferences or permissions.

📌 𝐓𝐡𝐞 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 𝐀𝐬𝐬𝐞𝐦𝐛𝐥𝐲 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰

This is the "engineering" part, a real-time data pipeline.

[1.] Orchestration
◾ The Agent (2) receives the user's Input (1) and takes control. It's the "brain" of the operation.

[2.] Retrieval
◾ The Agent queries RAG (3) to fetch relevant knowledge from Long-Term Memory.
◾ It also identifies which Action Tools (4) are needed for the task.

[3.] Assembly
◾ The Agent bundles all the context - User Input, Chat History, RAG results, Tool definitions, and its own reasoning, into a single, massive "Prompt" (5).

[4.] Execution & Memory
◾ This complete prompt is used to generate the Answer (6).
◾ The entire interaction is then stored in Short-Term Memory (7) for the next turn, and key insights are used to update Long-Term Memory (8), making the agent smarter over time.

Follow Pallavi for more such insights :)

Image Source - Weaviate