Kicking off a new article series here on X, based on what I’m learning while building and launching an enterprise agentic AI product at a startup. https://t.co/8QMSJmVkMt
Built SmartSales as part of AWS KIRO Hackathon @kirodotdev: An AI app that listens to live sales calls, extracts key details & pushes CRM-ready data instantly.
Full journey here: https://t.co/YSqSU3YY9p. #hookedonkiro
Planning to build a GenAI or AI-powered product?
Here’s the modern AI app stack you need to scale, serve, and secure your models. 👇
1. Data Layer: Foundation for AI
Use Snowflake, BigQuery, Postgres, Airflow, and dbt to collect, clean, and move data efficiently.
2. Model Development
Train with PyTorch, TensorFlow, HuggingFace, Scikit-learn, and track experiments using MLflow or Weights & Biases.
3. Model Versioning & Packaging
Ensure reproducibility and deployment using tools like MLflow, BentoML, Docker, and ONNX.
4. Deployment & Serving
Expose your model as an API using FastAPI, Flask, and serve it with Triton or TorchServe. Automate with GitHub Actions and Jenkins.
5. MLOps Infrastructure
Orchestrate workflows using Kubernetes, Kubeflow, and Flyte to scale across environments with Docker containers.
6. Monitoring & Observability
Track model accuracy and failures using WhyLabs, Prometheus, Fiddler, and Superset to stay in control.
7. Retrieval & Augmentation (for LLMs)
Use Pinecone, Weaviate, and FAISS with LangChain and LlamaIndex to connect external knowledge and enable RAG.
8. AI Agent Tools & Frameworks
Use AutoGen, LangGraph, CrewAI with n8n or
to orchestrate tool use and external actions (APIs, code, browsers).
9. Security & Compliance
Stay enterprise-ready with OAuth2, Rebuff, GDPR, and Guardrails to manage access, risk, and regulations.
10. Frontend & Interface
Deliver AI through React, Streamlit, GraphQL, and WebSockets. Use multimodal input/output via Whisper or Gemini.
AI apps today aren’t just models, they’re full-stack systems.
And this is the stack to build them right.
Moving GenAI from pilot to production involves mapping out:
- model type with your unique use case and budget
- thoroughly weighing the closed source vs open source model trade-offs
Want the full breakdown with examples & cost models? Read my blog: https://t.co/OlrACBsi73
New blog Alert! Check out my Beginner’s guide to setting up AWS MCPs where I talk about:
- MCPs & why they matter
- Hands-on setup steps (VS Code + Cline + Docker)
Read here: https://t.co/lST735FVUE
#AWS#AWSCommunityBuilders#mcp#LearnInPublic
🔥 Hot off the press: I’m officially an Agentblazer Innovator! I leveled up my AI + Agentforce skills on @Trailhead - and you should too. Skill up and become an Agentblazer today https://t.co/DRG7lOxwNR
Wow, thanks, Ashpreet! 🙌 Agno's clean documentation made my learning all the more quick and enjoyable!
BTW adding this tweet screenshot to my blog post for some well-earned bragging rights! 😆📸