Layers of AI/ML and Generative AI stack

Layers in AI/ML and Generative AI Environments

AI/ML stacks refer to the layers of technologies and tools used to build, deploy, and manage AI/ML models. Key components include:

1. Data Layer: Tools for data collection, storage, and preprocessing (e.g., databases, data lakes, ETL tools).
2. Feature Engineering: Tools and frameworks for transforming raw data into meaningful features (e.g., Pandas, numpy).
3. Model Development: Libraries and frameworks for building models (e.g., TensorFlow, PyTorch, Scikit-learn).
4. Model Training: Infrastructure for training models at scale (e.g., GPU/TPU, distributed training frameworks).
5. Model Evaluation: Tools for validating model performance (e.g., MLflow, TensorBoard).
6. Deployment: Platforms for deploying models into production (e.g., Docker, Kubernetes, AWS SageMaker).
7. Monitoring and Maintenance: Tools for tracking model performance and maintaining models (e.g., Prometheus, Grafana).

What is Generative AI?

Generative AI refers to algorithms that can generate new content, such as text, images, music, or code. Key aspects include:

1. Techniques:
   • Generative Adversarial Networks (GANs): Models that pit a generator against a discriminator to create realistic data.
   • Variational Autoencoders (VAEs): Models that encode data into a latent space and then decode it back to generate new data.
   • Transformer-based Models: Models like GPT-3/4 that generate text by predicting the next word in a sequence.RAG is a more customized Gen AI for retrieval of relevant data.
2. Applications:
   • Text Generation: Creating articles, poetry, and dialogue (e.g., GPT-3, ChatGPT).
   • Image Generation: Creating realistic images from scratch (e.g., DALL-E, StyleGAN).
   • Music Composition: Generating original music tracks (e.g., OpenAI Jukebox).
   • Code Generation: Writing code snippets or entire programs (e.g., GitHub Copilot).