Summary of "[ಕನ್ನಡ] Generative AI Full Course 2026 | MicroDegree"

High-level summary

This session is a beginner-to-intermediate introduction to Generative AI (GenAI) and practical engineering considerations in 2026. It covers why GenAI matters, basic machine‑learning foundations, how training works, neural networks and model scale, deployment and cost tradeoffs, how to pick and use models (cloud vs self‑hosted), building agents and RAG pipelines, and practical development tools (Jupyter/Colab, Python, REST APIs).

The presenter emphasizes conceptual understanding over memorizing buzzwords, then practical steps: how training optimizes parameters, why model size matters for compute/memory/cost, and how to prototype quickly using notebooks and cloud model APIs instead of trying to train huge models locally.

Emphasis: learn the concepts (not just buzzwords), then apply practical engineering techniques for prototyping and production.

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Main ideas, concepts and lessons

1. Why Generative AI matters now

• GenAI is becoming a baseline expectation across many roles: AI engineers, developers, DevOps, cloud engineers, testers, and non‑technical roles.
• There are massive opportunities in every industry; focus on practical usage (jobs/projects) rather than only theory.

2. Basic ML concepts (linear regression example)

• Dataset: inputs (x) and outputs (y). Inputs = independent variables; outputs = dependent variables.
• Model: a mathematical function mapping x → y (e.g., y = m x + c).
• Training: finding unknown parameters (weights) so predictions match known outputs.
• Intuitive training loop:
  • Initialize parameters (m, c) randomly.
  • Compute predictions y_hat for training x.
  • Compute error/loss (e.g., squared error).
  • Adjust parameters to reduce loss and repeat.
• Terms:
  • Bias = intercept.
  • Slope = parameter that changes line angle.
  • Loss = aggregate of prediction errors; objective is to minimize loss.

3. Optimization: gradient descent & learning rate

• Gradient descent: iteratively update parameters in the direction that reduces loss.
• Direction (sign) indicates increase/decrease for parameters; magnitude is controlled by learning rate.
• Learning rate tradeoffs:
  • Too large → overshoot or unstable training.
  • Too small → slow convergence.
• Training large corpora is computationally heavy and requires many iterations.

4. Train / Validation / Test

• Split data: train, validation (optional), and test. Common ratios: 70:30 or 80:20 depending on use case.
• Purpose:
  • Train: minimize loss on training set.
  • Validate: tune hyperparameters.
  • Test: measure generalization.
• Poor test performance → need more data, different model or architecture, or retraining/fine‑tuning.

5. Neural networks (intuition)

• Neuron/node: simple function mapping inputs to output; nodes grouped into layers.
• Layers: input → hidden layer(s) → output.
• Depth increases representational power (deep learning).
• Parameters are weights connecting nodes; more nodes/layers → more parameters.
• Hidden layers create intermediate representations; stacking increases feature extraction complexity.
• Diminishing returns: beyond a point more depth/parameters may not help and can overfit or be inefficient.

6. Model scale, parameters and resource implications

• Models labeled by parameter count: e.g., 7B, 30B, 70B, 175B, multi‑trillion.
• Bigger models require more RAM/GPU memory; more parameters → more multiply/accumulate ops → higher latency & cost.
• Hardware needs:
  • Small models: can run locally on modest RAM/GPU.
  • Large models (tens/hundreds of billions): require powerful servers and lots of GPU memory (24–256 GB+), commonly cloud‑hosted.
• Cost tradeoff: training/hosting big models is expensive and slow; many teams use hosted APIs instead of training from scratch.

7. Frameworks, model formats and saving/loading

• Common frameworks: TensorFlow, PyTorch.
• They handle model saving/loading and serialization formats (e.g., .pb, Torch formats).
• Save model weights after training; load later for inference.
• Frameworks provide checkpointing and export formats for reuse.

8. Cloud hosting vs self‑hosting; APIs and productization

• Cloud providers (AWS Bedrock, Anthropic, OpenAI, Hugging Face, Azure, etc.) host foundation models and provide inference endpoints/APIs.
• Benefits: avoid provisioning heavy GPUs locally; providers expose REST APIs and charge per usage (commonly per token or per request).
• Typical approaches:
  • Use hosted model via API for faster product development.
  • Train/customize privately for full control or on‑premise requirements.
• Pricing: usually per input/output token (or per image pixels, etc.); check model‑specific pricing and maximum token limits.

9. Model selection checklist

• Modality compatibility: text, image, audio/speech, video — choose a model trained for your modality.
• Training data / domain fit: relevance of the model’s training data to your use case.
• Parameter size & resource constraints: local vs cloud hosting and cost considerations.
• API features: max tokens, latency, fine‑tuning/instruction‑tuning support, embeddings support.
• Licensing: open source vs commercial (local use vs API-hosted with costs and SLA).
• Evaluate models in a playground or test environment before production.

10. Retrieval Augmented Generation (RAG), fine‑tuning and agents

• RAG: store embeddings of domain data in a vector DB; retrieve relevant docs at query time and provide them as context to the model.
• Fine‑tuning: adapt a base model to domain data for improved performance on specialized tasks.
• Agents / LangChain / flows:
  • Combine models, tool integrations, retrieval, and business rules to create autonomous agents.
  • Add guardrails (content filtering, safety checks) to prevent unsafe outputs.
• Building internal/custom models is long and resource intensive; many companies use hosted APIs plus RAG/fine‑tuning.

11. Practical Python development & prototyping (Jupyter, Colab, VS Code)

• Quick prototyping tools:
  • Google Colab / Colab Pro: browser‑based, preinstalled ML libs, temporary GPU access — great for experiments and sharing.
  • Jupyter Notebook / JupyterLab: local interactive development.
  • VS Code / PyCharm: for full projects and production code; support notebooks.
• Typical notebook workflow:
  • Create and run cells interactively.
  • Install packages inside notebook: !pip install or %pip.
  • Load datasets (pandas.read_csv), inspect (head(), tail()), visualize.
  • Save dependencies: pip freeze > requirements.txt.
  • Share notebooks (Colab makes sharing easy).
• Use Colab for temporary GPU access and iteration; use local/cloud servers for production.

12. REST APIs, JSON and practical API usage

• HTTP request anatomy: endpoint URL, headers (metadata, auth), and body (payload).
• Common HTTP methods:
  • GET: retrieve data.
  • POST: create/send data (often used to submit prompts).
  • PUT/PATCH: modify or replace resources.
  • DELETE: remove resources.
• JSON: standard request/response format (key/value pairs, nested objects, arrays).
• Tools: Postman for testing APIs without code.
• Typical flow: POST a JSON body with prompt and model parameters, parse JSON response for generated text.

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Methodologies / step-by-step procedures

A. Standard training loop (conceptual steps)

1. Prepare dataset: collect input/output pairs; split into train/validation/test.
2. Initialize model parameters (weights) randomly.
3. For each epoch (repeat until convergence):
   • Forward pass: compute outputs y_hat.
   • Compute loss (e.g., MSE, cross‑entropy).
   • Backward pass: compute gradients w.r.t. parameters.
   • Update parameters with an optimizer (SGD, Adam) using the learning rate.
   • Monitor training and validation loss/metrics.
4. Save model checkpoints periodically.
5. Evaluate on test set after training.
6. Optionally fine‑tune on domain data or tune hyperparameters.

B. Gradient descent intuition and hyperparameter handling

• Gradient direction indicates how to change parameters to reduce loss.
• Learning rate decides step size; balance stability and speed.
• Recompute loss and gradients after each update.
• Use mini‑batches for stochastic gradient descent and tune batch size, learning rate, and optimizer.

C. Model selection checklist (practical)

• Identify task and modality.
• Check available models for that modality/task.
• Verify training data and intended use (safety/legal).
• Evaluate model size vs resources: local vs cloud.
• Run quick experiments in a playground.
• Choose hosting approach: cloud API vs self‑hosted.
• If domain‑specific, consider RAG or fine‑tuning.

D. Quick Colab / Jupyter notebook workflow (step‑by‑step)

1. Open Google Colab or JupyterLab.
2. Create a new notebook (.ipynb).
3. Install dependencies: !pip install package-name or %pip install package-name.
4. Load datasets: df = pd.read_csv(‘path/to/file.csv’).
5. Inspect and visualize: df.head(), df.hist(), plt.show().
6. Prototype cell‑by‑cell and debug interactively.
7. Save/export notebook and capture dependencies: pip freeze > requirements.txt.
8. Share via Colab link or export to GitHub.

E. Calling a model via REST API (step‑by‑step)

1. Identify API endpoint (URL).
2. Acquire API credentials and set auth headers.
3. Construct JSON request (model ID, prompt, parameters like max tokens, temperature).
4. Send request (typically POST).
5. Parse response JSON to extract generated text or structured output.
6. Handle errors, rate limits, and monitor token usage for cost control.

F. Building a simple RAG-based agent (high-level steps)

1. Collect and clean domain data (documents, FAQs, manuals).
2. Embed documents with an embedding model and store vectors in a vector DB (Pinecone, Milvus, FAISS).
3. On user query:
   • Embed the query.
   • Retrieve top‑k similar documents from the vector DB.
   • Construct a prompt combining retrieved context and the user question.
   • Call an LLM to generate an answer.
4. Add guardrails: input validation, output filtering, logging, and human oversight.
5. Expose the agent as a REST endpoint for integration.

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Deployment & engineering considerations

• Hosting large models locally requires planning: GPU count, VRAM, CPU, system RAM, storage, and parallelization strategies.
• Production best practices:
  • Use batching, caching, and async patterns to improve throughput and cost efficiency.
  • Monitor token usage, throttling, and cost.
  • Choose the right model size for the job to control latency and expense.
• Prefer cloud hosted endpoints (SaaS) if you do not want to manage GPUs; pay per token/request.
• Use vector DB + RAG for domain grounding and to reduce prompt length/cost.

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Tools, providers and technologies mentioned

• Model providers: Amazon Bedrock, Anthropic, OpenAI (GPT family), Hugging Face, Azure OpenAI, and other vendors.
• Frameworks & libraries: TensorFlow, PyTorch, LangChain, Langflow, Pandas, NumPy, OpenCV.
• Notebooks & IDEs: Google Colab, Jupyter Notebook / JupyterLab, VS Code, PyCharm.
• Utilities: Postman for API testing; vector DBs for embeddings (Pinecone, FAISS, Milvus conceptually).
• Concepts: RAG, embeddings, tokens, fine‑tuning, guardrails.

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Speakers, participants and sources referenced

• Primary instructor: unnamed course instructor delivering the lecture.
• Student/participant names referenced:
  • Akash (asks Colab/Jupyter/installation questions)
  • Vijayalakshmi
  • Prakash
  • Sharath
  • Yashas
  • Mufesh
  • Other class participants (casual references)
• Companies/technologies cited: AWS (Bedrock, SageMaker), Anthropic, OpenAI/GPT, Hugging Face, Azure, TensorFlow, PyTorch, Google Colab, VS Code, PyCharm, Postman, vector DB systems, LangChain / Langflow.

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Optional deliverables (offered)

• A concise one‑page checklist for a practical first project (choose model → prototype in Colab → evaluate → deploy via API) with exact commands and a minimal Python example for calling a hosted model endpoint.
• Runnable Python code demonstrating the linear‑regression training loop (NumPy or PyTorch) showing initialization, forward pass, loss calculation, and backprop/gradient descent.

If you’d like one of these rendered as a standalone artifact (checklist or runnable code), it can be produced next.

Category

Educational

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