System Components
The ease.ml system is made up of a collection of components, each one targeting a specific pain-point commonly observed in machine learning workflows.
DataMagic
Modern ML applications are often data hungry --- sometimes it is caused by the extensive process of data collection, and sometimes it is caused by the striking diversity of the data format that makes it hard to construct a homogeneous large dataset. Given an input dataset from the user, the first step of the ease.ml process, ease.ml/DataMagic, aims at providing functionalities of automatic data augmentation (adding new data examples automatically) and automatic data ingestion (automatically normalizing data into the same, machine readable, format).
Modern ML applications are often data hungry --- sometimes it is caused by the extensive process of data collection, and sometimes it is caused by the striking diversity of the data format that makes it hard to construct a homogeneous large dataset. Given an input dataset from the user, the first step of the ease.ml process, ease.ml/DataMagic, aims at providing functionalities of automatic data augmentation (adding new data examples automatically) and automatic data ingestion (automatically normalizing data into the same, machine readable, format).
Snoopy
Machine learning is no panacea — if your data are 20% wrong but you are hoping for 90% accuracy to make a profit out of your model, it is highly likely that it is doomed to fail. In our experience, we were surprised by how often an end user has a staggering mismatch between the quality of their data an the expectation of the accuracy that an ML model can achieve.
Machine learning is no panacea — if your data are 20% wrong but you are hoping for 90% accuracy to make a profit out of your model, it is highly likely that it is doomed to fail. In our experience, we were surprised by how often an end user has a staggering mismatch between the quality of their data an the expectation of the accuracy that an ML model can achieve.
CPClean
What if ease.ml/snoopy thinks your data is not good enough for ML to reach your quality target? In this case, it might be counterproductive to fire up an expensive ML training process, instead, we hope to help the user to understand their data better and make a more informative decision.
What if ease.ml/snoopy thinks your data is not good enough for ML to reach your quality target? In this case, it might be counterproductive to fire up an expensive ML training process, instead, we hope to help the user to understand their data better and make a more informative decision.
DataScope
If data cleaning won’t increase your accuracy by too much, another potential reason of unsatisfactory ML quality is simply that you don’t have enough amount of data. If CPClean advices the user against data cleaning, she needs to acquire more data. Market is the next ease.ml component that helps the user with this.
If data cleaning won’t increase your accuracy by too much, another potential reason of unsatisfactory ML quality is simply that you don’t have enough amount of data. If CPClean advices the user against data cleaning, she needs to acquire more data. Market is the next ease.ml component that helps the user with this.
AutoML
If ease.ml/snoopy said “Yes” and we can finally fire up our ML training process! Given a dataset, ease.ml contains an AutoML component that outputs a ML model without any user intervention. There are three aspects of ease.ml/AutoML that makes it special.
If ease.ml/snoopy said “Yes” and we can finally fire up our ML training process! Given a dataset, ease.ml contains an AutoML component that outputs a ML model without any user intervention. There are three aspects of ease.ml/AutoML that makes it special.
CI
Machine learning models are software artefacts. Among the stream of models generated by ease.ml/AutoML, not all of them satisfy the based requirement of real-world deployment. Can we continuously test ML models in the way we are testing traditional softwares?
Machine learning models are software artefacts. Among the stream of models generated by ease.ml/AutoML, not all of them satisfy the based requirement of real-world deployment. Can we continuously test ML models in the way we are testing traditional softwares?
ModelPicker
All models that pass ease.ml/ci, in principle, can be deployed. This gives a pool of candidate models at any given time, each of which can be developed under different hypotheses (e.g., different slices of data). While real-world data distribution keeps changing rapidly (e.g., every day), how can we pick the best model, i.e. the one that fits our latest data distribution?
All models that pass ease.ml/ci, in principle, can be deployed. This gives a pool of candidate models at any given time, each of which can be developed under different hypotheses (e.g., different slices of data). While real-world data distribution keeps changing rapidly (e.g., every day), how can we pick the best model, i.e. the one that fits our latest data distribution?