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
- What it means to be AI first 1m
- Two stages of ML 4m
- ML in Google products 5m
- ML in Google Photos 1m
- Google Translate and Gmail 2m
- Replacing Heuristic Rules 4m
- Pre-trained models 3m
- Machine Learning with Sara Robinson (ML, not rules) 3m
- Vision API in action 4m
- Video intelligence API 4m
- Text-to-Speech API 3m
- Translation and NL4 5m
- Text to Speech 5m
- DialogFlow 9m
- Lab Intro: Pretrained ML APIs Intro 1m
- Getting Started With GCP And Qwiklabs 4m
- Lab: Invoking Machine Learning APIs 0m
- Lab Solution Invoking Machine Learning APIs 9m
- It's all about data 3m
- A data strategy 6m
- Training and serving skew 6m
- ML Training Phases 14m
- Lab Intro - Framing an ML Problem 2m
- Lab Debrief: Framing an ML problem 4m
- Demo: ML in applications 1m
- Reading: What it Means to be AI First 0m
- Introduction 3m
- AI Platform Notebooks 1m
- Reading: AI Platform Notebooks 0m
- Demo AI Platform Notebooks 3m
- Development process 3m
- Computation and storage 5m
- Lab: Analyzing data using AI Platform Notebooks and BigQuery 1m
- Lab: Analyzing data using AI Platform Notebooks and BigQuery 0m
- Lab Debrief Analyzing data using AI Platform Notebooks and BigQuery 7m
- Reading: Python Notebooks in the Cloud 0m