Ruminating on Data Drift and Concept Drift

 Quite often, the performance (accuracy of prediction) of a AI model degrades with time. One of the reasons this happens is due to a phenomenon called as "Data Drift". 

So what exactly is Data Drift? 

Data Drift can be defined as any change to the structure, semantics or statistical properties of data - i.e. model input data.  

• Changes to structure of data: New fields being added or old field deleted. This could happen because of a new upgrade to a upstream system, a new sensor, etc. 
• Changes to semantics of data: A new upstream system is sending temperature in F and not C. 
• Changes to statistical properties of data: Changes to the atmospheric pressure threshold levels due to environmental changes. There could be also data quality issues such as a bug in upstream system that delivers junk data. 

To maintain the accuracy of our AI models, it is imperative that we measure and monitor Data Drift. Our machine learning infrastructure needs to have tools that automatically detect data drift and can pin-point the features that are causing the drift. 

Changes to the underlying statistical properties of data is also called as "Concept Drift". A classic example of this is the current pandemic. The "behaviour" or "concept" has changed after the pandemic - e.g. models that predict the amount of commute time are no longer valid. Models that forecasted the number of the cosmetic surgeries in 2021 are no longer valid. 

Most of the hyperscalers provide services that enable us to monitor data drift and take proactive actions. The below links provide some examples of cloud services for data drift:

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-monitor-datasets?tabs=python

https://aws.amazon.com/sagemaker/model-monitor/

https://cloud.google.com/blog/topics/developers-practitioners/event-triggered-detection-data-drift-ml-workflows

Open DataSets

The following sites offer many free data-sets that can be used to train our AI models and learn new stuff on AIML. Happy coding !

• https://www.openml.org/search?type=data
• https://archive.ics.uci.edu/ml/index.php
• https://www.reddit.com/r/datasets/
• http://academictorrents.com/browse.php
• https://www.opendatanetwork.com/
• https://www.data.gov/
• https://github.com/BuzzFeedNews/everything#standalone-datasets
• https://github.com/fivethirtyeight/data
• https://data.world/community/open-community/
• https://www.kaggle.com/datasets

Why GPU computing and Deep Learning are a match made in heaven?

Deep learning is a branch of machine learning that uses multi-layered neural networks for solving a number of challenging AI problems.

GPU (Graphics Processing Unit) architecture are fundamentally different from CPU architecture. A GPU chip would be significantly slower that a CPU, but a single GPU might have thousands of cores while a CPU usually has not more than 12 cores.

Hence any task that can be parallelized over multiple core is a perfect fit for GPUs. Now it so happens, that deep learning algorithms involve a lot of matrix multiplications that are an excellent candidate for parallel processing over the cores of a GPU. Hence GPUs make deep learning algorithms run faster by an order of magnitude. Even training of models is much faster and hence you can expedite the GTM of your AI solutions.

An example of how GPUs are better for AI solutions is considering AlexNet, a well known image classification deep network. A modern GPU costing about $1000 takes 2.5 days to fully train AlexNet on the very large ImageNet dataset. On the otherhand, it takes a CPU costing several thousand dollars nearly 43 days.

A good video demonstrating the difference between GPU and CPU is here: