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.
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
- Google Photos 1m
- Google Translate 2m
- Replacing heuristics 5m
- It's all about data 3m
- Lab-Framing an ML problem 2m
- 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 Introduction 2m
- Cloud Datalab 1m
- Demo- Cloud Datalab 3m
- Development process 2m
- Computation and storage 5m
- Lab: [ML on GCP C1] Rent-a-VM to process earthquake data 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: [ML on GCP C1] Analyzing data using Datalab 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: [ML on GCP C1] Invoking Machine Learning APIs 0m
- Lab Solution 8m