tokyo azure meetup #11 introduction to azure machine learning

Introduction to Azure Machine Learning Kanio Dimitrov

Use Cases

• Hand written address recognition

• Personal loan approval

• Product recommendation

Machine Learning

Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can teach themselves to grow and change when exposed to new data.

Major Components

Data Algorithm

Data Analysis Model

• Data drives the prediction logic, not the logic written by developer

Supervised vs Unsupervised • Supervised• Each row has all features• Each row has the value we want to predict

• Unsupervised•We look for clusters of similar data • Algorithm analyzed the data• Algorithm finds similar data

Technique ComparisonSupervised Unsupervised

Value prediction Identify cluster of like dataNeeds training data

containing value being predicted

Data does not contain cluster membership

Trained model predicts value in new data

Model provides access to data by cluster

Machine Learning Workflow

Guidance• Early steps are most important

• Going backwards is typical

• Data always needs “massaging”

• The more data you have, the better

• Never release bad solution with hope to be fixed later

• Analyze results before changing model or dataset

Account OptionsTrial Azure accountNo Azure account required Azure account requiredAuto ML Workspace setup Manual ML Workspace setupAccess only to Experiments and Services

Access to all Azure resources (SQL, Web services mgmt. and more)

Trial account only Permanent accountFree Payment method requiredTrail usage Production usageRetraining not supported Required to retrain model

DEMO – Car Price Prediction

DEMO – German Credit Risk Prediction

Thank you!