Modern Generative AI relies on a deeply integrated technology stack that spans data infrastructure, machine learning frameworks, and distributed training environments. Building this stack involves not only managing vast datasets for model training, validation, and testing, but also ensuring efficient orchestration of compute, storage, and AI-specific workflows.
This guide outlines ten essential layers of the Generative AI technology stack—each critical for enabling large-scale, performant AI systems.
1. Data Lake #
At the foundation lies the enterprise data lake, built on modern, software-defined, Kubernetes-native object storage. Unlike traditional archive-oriented storage appliances, these systems support high-performance AI workloads and integrate seamlessly with cloud-native ecosystems.
Common deployment options include major public cloud platforms such as AWS, Google Cloud Platform (GCP), and Microsoft Azure, as well as on-premises or hybrid solutions like MinIO.
They must support:
- Streaming workloads
- Atomic metadata/object operations
- Efficient encryption and erasure coding
- Lambda compute integration
2. OTF-Based Data Warehouse #
Modern data warehouses built on Open Table Format (OTF) specifications—like Apache Iceberg, Apache Hudi, and Delta Lake—use object storage as their foundation. Pioneered by Netflix, Uber, and Databricks, these frameworks enable scalable, schema-evolving warehouses on commodity storage.
Example technologies:
- Dremio Sonar (processing engine)
- Dremio Arctic (catalog service)
- Starburst / Open Data Lakehouse
3. Machine Learning Operations (MLOps) #
MLOps extends DevOps practices to machine learning workflows, automating everything from training to deployment. These tools ensure continuous integration and reproducibility while leveraging object storage for model artifacts and datasets.
Key platforms:
- MLRun (McKinsey & Company)
- MLflow (Databricks)
- Kubeflow (Google)
4. Machine Learning Frameworks #
ML frameworks provide the core libraries for building and training models. The defining element is the Tensor—a multi-dimensional data structure that supports automatic differentiation and GPU acceleration.
Leading frameworks:
- PyTorch (Meta)
- TensorFlow (Google)
5. Distributed Training #
Distributed training accelerates model development by running computations across multiple nodes or GPUs in parallel. This is critical for large datasets and complex architectures like transformers.
Key libraries:
- DeepSpeed (Microsoft)
- Horovod (Uber)
- Ray (Anyscale)
- Spark PyTorch Distributor (Databricks)
- Spark TensorFlow Distributor (Databricks)
6. Model Hub #
Model hubs enable rapid experimentation and deployment through shared pre-trained models. Hugging Face dominates this space, offering both model hosting and libraries like Transformers and Datasets.
Example platform:
- Hugging Face
7. Application Frameworks #
Generative AI frameworks simplify building applications powered by large language models (LLMs). They handle tasks such as tokenization, vectorization, retrieval, and prompt orchestration for workflows like Retrieval-Augmented Generation (RAG).
Popular frameworks:
- LangChain
- AgentGPT
- Auto-GPT
- BabyAGI
- Flowise
- GradientJ
- LlamaIndex
- Langdock
- TensorFlow (Keras API)
8. Document Processing #
Preparing unstructured content for AI ingestion requires automated document parsing, text chunking, and embedding generation. These tools convert diverse document formats into vector-ready data for downstream processing.
Libraries:
- Unstructured
- Open-Parse
9. Vector Databases #
Vector databases enable semantic search and context-aware retrieval, essential for RAG and knowledge-grounded AI. They replace keyword-based searches with embedding-based similarity matching.
Leading vector databases:
- Milvus
- Pgvector
- Pinecone
- Weaviate
10. Data Exploration and Visualization #
Visualization is key to understanding model inputs and performance. Python-based libraries simplify data profiling, correlation analysis, and feature exploration.
Libraries:
- Pandas
- Matplotlib
- Seaborn
- Streamlit
Summary: The Generative AI Tech Stack #
| # | Layer | Example Tools |
|---|---|---|
| 1 | Data Lake | MinIO, AWS, GCP, Azure |
| 2 | OTF Data Warehouse | Dremio, Starburst, Delta Lake |
| 3 | MLOps | MLflow, Kubeflow, MLRun |
| 4 | Frameworks | PyTorch, TensorFlow |
| 5 | Distributed Training | DeepSpeed, Horovod, Ray |
| 6 | Model Hub | Hugging Face |
| 7 | App Frameworks | LangChain, Auto-GPT, Flowise |
| 8 | Document Processing | Unstructured, Open-Parse |
| 9 | Vector Databases | Milvus, Pinecone, Weaviate |
| 10 | Visualization | Pandas, Seaborn, Streamlit |
By integrating these ten layers, architects can build a scalable, flexible, and high-performance Generative AI stack—capable of supporting large-scale model training, retrieval-based inference, and enterprise AI applications.