-Workflow of projects -Selecting AI projects -Organizing data and team for the projects
ie speech recognition or self-driving car tools:Amazon Echo/Alexa keywords
Step1: Collect data Step2: Train model (You have to iterate many times until, hopefully, the model looks like is good enough.) Step3: Deploy model (incl. maitain/update)
ie Optimize a sales funnel (of an onlines market) or automize a manufacturing line or ML to prioritize the leads of customers/collections or Automated visual inspections or Automated resume screening or Recommending personalized ads/services
Step1: Collect data Step2: Analyze data Step3: Suggest hypotheses/actions
**Every job function needs to learn how to use data
Interception of “what AI can do/AI experts” and “Valuable for your business/domain experts”
…much more useful to think about automating tasks rather than automating jobs… …you can make progress even without big data, even without tons of data…
1st due diligence (technical/business/ethical) the project before starting 1-Technical: -Consider AI performance -How much data needed -Engineering timeline 2-Busines: -Costs -Revenue -Launch new product/business? 3-Ethical diligence
ML can be >inhouse/outsource Data Science >inhouse if an industry standard >avoid building it
-specify an acceptance criteria for the project
Open Source: PyTorch, TensorFlow, Hugging Face PaddlePaddle, Scikit-Learn, or R Academic: Arxiv Open Repo: GitHub edge deployments:the computation has to happen usually in a computer right there inside the car, that’s called an edge deployment. edge/cloud/on-prem