Day 7 of learning FastAPI & MLOps ๐
Today I:
โข Learned Docker fundamentals ๐ณ
โข Converted an ML project into a Docker image
โข Deployed the container on AWS โ๏ธ
From local to cloud โ real-world learning ๐ฅ
#Docker#AWS#FastAPI#MachineLearning#MLOps#Backend#100DaysOfCode
Day 4โ6 of learning FastAPI ๐
In the last 3 days, I worked on:
โข POST, PUT, DELETE requests in FastAPI
โข Serving ML models using FastAPI
โข Making code more production-ready
โข Started learning Docker ๐ณ
Things are getting real now ๐ฅ
#FastAPI#MachineLearning#Docker#MLOps
Day 3 of learning FastAPI ๐
Today I learned:
โข Path parameters โ dynamic URLs
โข Query parameters โ filter & search data
โข HTTP status codes โ clear API responses
Slowly building strong backend fundamentals ๐ช
#FastAPI#Python#BackendDev#100DaysOfCode#LearningInPublic
Day 2 of learning FastAPI ๐
Learned core HTTP methods today:
โข GET โ fetch data
โข POST โ create data
โข PUT โ update/replace data
โข DELETE โ remove data
Feeling more confident building real APIs now! ๐ฅ
#FastAPI#Python#Backend#100DaysOfCode#LearningInPublic
Day 1 of learning FastAPI ๐
โข Learned what an API is & why we need it
โข Understood monolithic architecture
โข Compared FastAPI vs Flask
โข Built my first FastAPI endpoint โจ
Excited for this journey! ๐ฅ
#FastAPI#Python#100DaysOfCode#Backend#LearningInPublic
๐ Journey Complete: Generative AI
From LangChain basics โ RAG โ AI Agents โ itโs been an incredible learning ride! ๐ก
Built projects, debugged errors, and learned how GenAI systems truly work end-to-end.
Generative AI โ
#GenerativeAI#LangChain#AI#100DaysOfAI#AIAgents
๐งฉ Day 15 of Generative AI
Explored Tools in LangChain today!
๐น Built-in & Custom Tools
๐น Toolkits, Tool Binding & Execution
Also started working on my first AI Agent ๐ค
Things are getting really exciting! โก
#GenerativeAI#LangChain#AIAgents#AI#100DaysOfAI
โก Day 14 of Generative AI
Didnโt learn anything new today โ focused on building my first RAG project! ๐ง
Felt amazing to see everything come together in action ๐ค
#GenerativeAI#LangChain#RAG#AI#100DaysOfAI
๐ Day 13 of Generative AI
Dived into Retrievers & their types like MMR ๐ง
Finally understood the core of RAG (Retrieval-Augmented Generation) and even started building my own RAG application! โก
#GenerativeAI#LangChain#RAG#AI#100DaysOfAI
๐พ Day 12 of Generative AI
Learned about Vector Stores & how they differ from Vector Databases ๐ง
Also explored ChromaDB for storing and retrieving embeddings efficiently! โก
#GenerativeAI#LangChain#AI#ChromaDB#100DaysOfAI
โ๏ธ Days 4โ11 of Generative AI
Covered key LangChain concepts:
๐น Structured Output & Output Parsers
๐น Chains & Runnables
๐น Document Loaders
๐น Text Splitters
Feeling more confident building GenAI pipelines now! ๐
#GenerativeAI#LangChain#AI#100DaysOfAI
โ๏ธ Day 3 of Generative AI
Explored LangChain Prompts today:
๐น Static & Dynamic Prompts
๐น Chat History
๐น Message Placeholder
Getting a feel for how prompts shape GenAI workflows ๐ค
#GenerativeAI#LangChain#AI#100DaysOfAI