This section details all the requirements needed to use this solution.

# Instance Requirements
This solution is compatible with **Dataiku DSS V12+**


# Data Sources requirements
This solution requires two input datasets : 
 1 .  **[Sensor data](dataset:input_sensor_data)**  this dataset should contain all the sensors values from all the machines in the following format : 
- `equipment_id`  (_string_ ) : ID of the machine
- `sensor_id`( _string_ ) : ID of the sensor
- `timestamp`( _date_ ) : timestamp when this value has been emitted
- `sensor_value` (_double_ ) : sensor value  

 ![Capture d’écran 2022-12-02 à 11.31.32.png](mUIQYUSrS7pX)  
  
  
   2 .  **[Batch data](dataset:input_batch_data)**  this dataset should contain the information about batch general parameters in the following format : 
-  `batch_id`   ( _string_ ): ID of the batch
- `equipment_id` ( _string_ ):  ID of the machine
- `start_time` ( _date_ ): timestamp of the start time of the batch
- `end_time` ( _date_ ): timestamp of the end time of the batch
- `failure`  ( _bigint_ ): value of the outcome: 1 if it is a failure, 0 if it was a success  

In this dataset,  **you can add as many columns as you have batch parameters**  (string type). These parameters will be used to analyze your batches and predict failure. In the example below, a `recipe` column has been added : 

![Capture d’écran 2022-12-02 à 11.31.16.png](5Z4QbQFmA3nq)

Note: for all those mandatory columns, the names and the types have to be respected.





