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VERSION:2.0
PRODID:-//Palaner//palaner Single Event//EN
METHOD:PUBLISH
CALSCALE:GREGORIAN
BEGIN:VEVENT
UID:meetup-a0b66a12ff@palaner.app
DTSTAMP:20260802T150723Z
DTSTART:20260815T080000Z
DTEND:20260815T093000Z
SUMMARY:Build & Learn: From Data Science to AI Engineering Week 1
LOCATION:Octupus Bar
DESCRIPTION:Saved in Palaner as: saved\n\nPalaner match: 28% · why: lots of
  people going\n\n📅 Week 1: Kickoff — From Data Science to AI Engineering\n
 After a short break\, Build & Learn is back with a new seven-week focus:\n
 learning how to build and ship real AI applications. 👩‍💻\n\nAI engineering
  is becoming an increasingly important path for data scientists\, analysts
  and Python developers. The biggest shift is not simply learning more mach
 ine learning theory—it is learning how to turn an AI prototype into a work
 ing application that other people can use.\n\nOver seven weeks\, we will b
 uild AND deploy basic end to end LLM/AI project touching key technologies 
 ( FastAPI\, Postgres\, Docker\, a real AI pipeline. )\n\nWeek 1 — Kickoff 
 & Setup: Set up Python\, GitHub\, Docker\, API keys\, and run the starter 
 project locally.\nWeek 2 — Learn the LLM specific layer. (OpenAI API)\nWee
 k 3 — Build production ready AI backends. (Fast API\, MCP server)\nWeek 4 
 — Connect AI to your data with RAG (vector database)\nWeek 5 — Lean into e
 vals and observability (tracing\, evaluation)\nWeek 6 — Build & Ship Your 
 Version\nWeek 7 — Demo Day\n\nBy the end of the 7 weeks you will have a wo
 rking AI application in your own GitHub repository and experience building
  an AI pipeline\, backend\, database and frontend.\n\n✨ Who is this for?\n
 This cycle is designed for data scientists\, analysts\, software developer
 s and anyone interested in transitioning toward AI engineering.\nNo previo
 us LLM or AI engineering experience is required. However\, you should alre
 ady understand basic Python or be willing to complete some preparation bef
 ore the first build session.\n\n✨ Who’s hosting?\nI’m Lindsey\, a senior d
 ata scientist working on AI systems\, causal inference and data products.\
 nI’ve worked on machine learning\, uplift modelling\, fraud detection and 
 production LLM systems. I care about learning through building—and about m
 oving beyond AI hype toward applications that actually work.\n\n💻 Bring: Y
 our laptop and curiosity. Please also grab ☕ at the octopus bar to support
  their business and providing the location!
URL:https://www.meetup.com/build-learn-data-science-saturdays/events/315670
 767/
CATEGORIES:palaner,saved
STATUS:TENTATIVE
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