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Steps to building real-world AI systems. I've given a simple detailed explanation below. 𝗦𝘁𝗲𝗽 1 – 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 & 𝗖𝗼𝗺𝗽𝘂𝘁𝗲 𝗟𝗮𝘆𝗲𝗿 • This is where all the 𝗵𝗲𝗮𝘃𝘆 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 𝗵𝗮𝗽𝗽𝗲𝗻𝘀. • It provides the 𝗵𝗮𝗿𝗱𝘄𝗮𝗿𝗲 (GPUs, TPUs, CPUs) and 𝗰𝗹𝗼𝘂𝗱 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺𝘀 to run large models. • Examples: AWS, Azure, GCP etc. 𝗦𝘁𝗲𝗽 2 – 𝗖𝗼𝗿𝗲 𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀 • These are the 𝗯𝗿𝗮𝗶𝗻𝘀 𝗼𝗳 𝘆𝗼𝘂𝗿 𝗮𝗽𝗽. • They understand queries, reason, and generate answers. • Examples: GPT (OpenAI), Claude, Gemini, Mistral etc. 𝗦𝘁𝗲𝗽 3 – 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 • Frameworks 𝗰𝗼𝗻𝗻𝗲𝗰𝘁 your model with tools, data, and workflows. • They make it easier to build chatbots, retrieval systems, or automation flows. • Examples: LangChain, Hugging Face, LLM Guard etc. 𝗦𝘁𝗲𝗽 4 – 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 & 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 • Handles how data and models interact during processing. • Includes vector databases, retrieval pipelines, and orchestration tools. • Examples: Pinecone, Weaviate, LlamaIndex etc. • Ensures your model can 𝗳𝗶𝗻𝗱, 𝗿𝗲𝘁𝗿𝗶𝗲𝘃𝗲, 𝗮𝗻𝗱 𝘂𝘀𝗲 the right data quickly. 𝗦𝘁𝗲𝗽 5 – 𝗠𝗼𝗱𝗲𝗹 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 • Improves model speed, efficiency, and cost-effectiveness. • Tools help fine-tune, compress, or track performance. • Examples: OctoML, Weights & Biases, Hugging Face. • Use this layer when scaling your AI product or deploying to production. 𝗦𝘁𝗲𝗽 6 – 𝗗𝗮𝘁𝗮 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀 & 𝗟𝗮𝗯𝗲𝗹𝗶𝗻𝗴 • Converts text, images, or data into 𝗻𝘂𝗺𝗲𝗿𝗶𝗰𝗮𝗹 𝗿𝗲𝗽𝗿𝗲𝘀𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻𝘀 (vectors). • Enables semantic search and understanding. • Examples: Cohere, JinaAI, Nomic, ScaleAI. 𝗦𝘁𝗲𝗽 7 – 𝗗𝗮𝘁𝗮 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 & 𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 • Helps create 𝗺𝗼𝗿𝗲 𝗱𝗮𝘁𝗮 for better training. • Generates synthetic data or expands small datasets. • Examples: Gretel, Tonic AI, Mostly. • Ideal when your dataset is limited or sensitive. 𝗦𝘁𝗲𝗽 8 – 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 & 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 • Tracks how your model behaves in real-world use. • Detects errors, biases, or drifts over time. • Examples: WhyLabs, Fiddler, Helicone. 𝗦𝘁𝗲𝗽 9 – 𝗔𝗜 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 & 𝗚𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀 • Protects your model and users from unsafe or biased outputs. • Adds filters, compliance rules, and policy enforcement. • Examples: Garak, Arthur AI, LLM Guard. • Essential for enterprise or public-facing applications. ✅ 𝗙𝗶𝗻𝗮𝗹 𝗙𝗹𝗼𝘄 𝗶𝗻 𝗦𝗶𝗺𝗽𝗹𝗲 𝗪𝗼𝗿𝗱𝘀: 1. Start with compute and cloud setup. 2. Pick your core model (LLM). 3. Use frameworks to build workflows. 4. Connect data pipelines and vector databases. 5. Optimize and fine-tune your models. 6. Embed and label your data. 7. Generate or augment additional datasets. 8. Monitor model performance continuously. 9. Add security guardrails before deployment. ✅ 𝗥𝗲𝗽𝗼𝘀𝘁 𝘁𝗵𝗶𝘀 𝘀𝗼 𝗺𝗼𝗿𝗲 𝗽𝗲𝗼𝗽𝗹𝗲 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝗵𝗼𝘄 𝗔𝗜 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝘄𝗼𝗿𝗸𝘀!