A library that provides high-speed training and inference of popular machine learning models on modern CPU/GPU computing systems. Snap ML has been designed to address some of the biggest challenges that companies and practitioners face when applying machine learning to real use cases. These challenges are illustrated in the graphics below.
IBM Watson AutoAI has recently introduced a new beta feature — time series support. It’s is as easy as a walk in the park: all you need to do is drag & drop your time series data, and then sit back and relax while the best model to is being prepared for you.
In this story I will present how easily IBM AutoAI python API can be applied to COVID19 data to get predicted confirmed cases for the next few days.
To work with AutoAI for time series one needs to have Watson Machine Learning service instance (included with the…
Imagine you are at your favorite restaurant. You just finished eating your favorite dish. Yes, exactly, the one with the secret sauce. Suddenly you look up from your empty plate and see the chef standing next to your table. She tells you to please follow her. As you make your way towards the back of the restaurant the anticipation grows. Finally the chef swings open the doors to the kitchen, waves her hand for you to come in, and says “let me show you how the secret sauce is made!”
I had the pleasure to be part of the jury for this year edition of “Ustawka 2020” hackathon for students in Poland. The hackathon was organised by IBM Poland and University of Warsaw. The topic for this year’s edition was prediction of COVID-19 daily confirmed cases, deaths and recoveries. The predictions had to be made per each day in 14 days period (June 15–26th).
Is it possible to download Watson AutoAI trained model and use it outside Watson Studio ecosystem? The answer is YES.
This short story describes in details how one can download AutoAI trained model and use it on 3rd party environment (local machine, cloud service etc.).
The easiest way to download trained pipeline model is to use python SDK and autogenerated notebook. From the drop down menu next to selected pipeline model click “Save as Notebook”.
The notebook can be run either in Watson Studio runtime or any other notebook server (download it). Notebook installs automatically all required dependencies:
As a continuation of epidemic models comparative analysis we want to examine one more regression model created by Watson Studio AutoAI. We will be using new python API to define and trigger AutoAI experiment. The jupyter notebook with all steps can be found here.
Some time ago I have written a story how to predict incorrect bug fixes. The full story “Adoption of machine learning to software failure prediction” can be found here. Long story short — we have adopted binary classification algorithm to predict if the bug has been correctly or incorrectly fixed. Code change sets predicted as incorrect ones required development (QA) team attention. Ones predicted as correctly fixed were automatically closed. The adopted solution involved data science knowledge.
Data science expertise was required to:
In this story, we would like to share our recent experience of building, serving and integrating COVID-19 models using IBM Cloud.
For the purpose of this experiment, we have tried several epidemic models. Since we are located in Poland we have adopted all tested models to Poland COVID-19 situation and data. However, shared below examples can be easily adapted to other locations and data sources by simply passing country name and other input parameters.
We have evaluated SIR, logistic, double-exponential and Weibull models to predict the number of…
It is extremely hard to measure the impact of the AI to the business. It is even harder to find AI issues that may drive your business indicators down. And the hardest problem is to predict the fix impact on the business indicator before it is even made.
Ability to track the key performance indicators (KPIs) in context of AI system health is one of the new Watson OpenScale components.
Business application monitor:
Watson OpenScale tracks and measures outcomes from AI across its lifecycle, and adapts and governs AI to changing business situations — for models built and running anywhere. You can read more details here.
Watson OpenScale integrates with external ml serve engines (Azure ML Studio, Amazon SageMaker etc.) in the following way:
Automation architect and data scientist at IBM Krakow Software Lab. Currently working on Watson Machine Learning cloud offering.