42. STORE &
EXPLORESensors and IoT
(unstructured)
Files (unstructured)
Media (unstructured)
Logs (unstructured)
Business/custom apps
(structured)
Power BIAzure Analysis
Services
Real-time Apps
Cosmos DB
INGEST PREP & TRAIN
MODEL & SERVE
STORE
Azure Data Lake Storage Gen2
SQL Data Warehouse
(Polybase)
Azure Databricks
Cosmos DB
Azure Data Explorer
Azure SQL
Data Warehouse
Azure Data Explorer
Azure Databricks
Azure Machine Learning
Azure Data Factory
Data Lake Storategy
Sensors and IoT
(unstructured)
Azure IoT Hub Kafka
52. Mileage
Condition
Car brand
Year of make
Regulations
…
Parameter 1
Parameter 2
Parameter 3
Parameter 4
…
Gradient Boosted
Nearest Neighbors
SVM
Bayesian Regression
LGBM
…
Mileage Gradient Boosted Criterion
Loss
Min Samples Split
Min Samples Leaf
Others Model
Which algorithm? Which parameters?Which features?
Car brand
Year of make
モデルの開発には、多くの 試行錯誤 が必要…
53. Criterion
Loss
Min Samples Split
Min Samples Leaf
Others
N Neighbors
Weights
Metric
P
Others
Which algorithm? Which parameters?Which features?
Mileage
Condition
Car brand
Year of make
Regulations
…
Gradient Boosted
Nearest Neighbors
SVM
Bayesian Regression
LGBM
…
Nearest Neighbors
Model
繰り返し
Gradient BoostedMileage
Car brand
Year of make
Car brand
Year of make
Condition
54. Mileage
Condition
Car brand
Year of make
Regulations
…
Gradient Boosted
Nearest Neighbors
SVM
Bayesian Regression
LGBM
…
Gradient Boosted
SVM
Bayesian Regression
LGBM
Nearest Neighbors
Which algorithm? Which parameters?Which features?
繰り返し
Regulations
Condition
Mileage
Car brand
Year of make