How Google does Machine Learning
What is machine learning, and what kinds of problems can it solve? Google thinks about machine learning slightly differently -- of being about logic, rather than just data. We talk about why such a framing is useful when thinking about building a pipeline of machine learning models. Then, we discuss the five phases of converting a candidate use case to be driven by machine learning, and consider why it is important the phases not be skipped. We end with a recognition of the biases that machine learning can amplify and how to recognize this.
What you'll learn
What is machine learning, and what kinds of problems can it solve? Google thinks about machine learning slightly differently -- of being about logic, rather than just data. We talk about why such a framing is useful when thinking about building a pipeline of machine learning models. Then, we discuss the five phases of converting a candidate use case to be driven by machine learning, and consider why it is important the phases not be skipped. We end with a recognition of the biases that machine learning can amplify and how to recognize this.
Table of contents
- Module 2 Slides 0m
- What it means to be AI first 1m
- Two stages of ML 4m
- ML in Google products 5m
- Google Photos 1m
- Google Translate 2m
- Replacing heuristics 5m
- It's all about data 3m
- Lab-Framing an ML problem 2m
- Question: Framing an ML Problem 0m
- Lab debrief 4m
- ML in Applications 3m
- Pre-trained models 3m
- The ML marketplace is evolving 2m
- A data strategy 6m
- Training-serving skew 6m
- A ML strategy 2m
- Transform your business 2m
- Lab Intro: Non-traditional ML use case 0m
- Module 5 Slides 0m
- Module Introduction 2m
- Cloud Datalab 1m
- Demo- Cloud Datalab 3m
- Development process 2m
- Computation and storage 5m
- Intro to Qwiklabs from Lak 5m
- Lab: Rent-a-VM to process earthquake data MLGCP 0m
- Lab debrief 11m
- Cloud shell 2m
- Third Wave of Cloud_3 2m
- Third Wave of Cloud_3 2m
- Third Wave of Cloud_4 1m
- Lab Intro 1m
- Lab: Analyzing data using AI Platform Notebooks and BigQuery 0m
- Lab debrief 7m
- ML - not rules 3m
- Cloud Vision API 4m
- Video intelligence API 4m
- Cloud Speech API 3m
- Translation and NL 5m
- Lab- Pretrained ML APIs Intro 1m
- Lab Solution 8m
- Lab: Invoking Machine Learning APIs 0m