The Generative AI Tech Stack

GenAI refers to systems capable of creating new content, such as text, images, code, or music, by learning patterns from existing data. Here are the key building blocks for GenAI Tech Stack:

1 - Cloud Hosting & Inference: Providers like AWS, GCP, Azure, and Nvidia offer the infrastructure to run and scale AI workloads. 2 - Foundational Models: Core LLMs (such as GPT, Claude, Mistral, Llama, Gemini, Deepseek) trained on massive data, form the base for all GenAI applications. 3 - Frameworks: Tools like LangChain, PyTorch, and Hugging Face help build, deploy, and integrate models into apps. 4 - Databases and Orchestration: Vector DBs (such as Pinecone, Weaviate), orchestration tools (such as LangChain, LlamaIndex) manage memory, retrieval, and logic flow. 5 - Fine-Tuning: Platforms like Weights & Biases, OctoML, and Hugging Face enable training models for specific tasks or domains. 6 - Embeddings and Labeling: Services like Cohere, Scale AI, Nomic, and JinaAI help generate and label vector representations to power search and RAG systems. 7 - Synthetic Data: Tools like Gretel, Tonic AI, and Mostly AI create artificial datasets to enhance training. 8 - Model Supervision: Monitor model performance, bias, and behavior. Tools such as Fiddler, Helicone, and WhyLabs help. 9 - Model Safety: Helps ensure ethical, secure, and safe deployment of GenAI systems. Solutions like LLM Guard, Arthur AI, and Garak help with this.

Author

Ai Backbone Network (ABN), ABN ASIA was founded by people with deep roots in academia, with work experience in the US, Holland, Hungary, Japan, South Korea, Singapore, and Vietnam. ABN Asia is where academia and technology meet opportunity. With our cutting-edge solutions and competent software development services, we're helping businesses level up and take on the global scene. Our commitment: Faster. Better. More reliable. In most cases: Cheaper as well.

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