Hey Readers, today I bring forth an exciting news for you all aspiring data scientists and machine learners!
Something new happened in Microsoft Research Blog :
Something new happened in Microsoft Research Blog :
The Microsoft Research Outreach team has worked extensively with the external research community to enable adoption of cloud-based research infrastructure over the past few years. Through this process, we experienced the ubiquity of Jim Gray’s fourth paradigm of discovery based on data-intensive science – that is, almost all research projects have a data component to them. This data deluge also demonstrated a clear need for curated and meaningful datasets in the research community, not only in computer science but also in interdisciplinary and domain sciences.
Today we are excited to launch Microsoft Research Open Data – a new data repository in the cloud dedicated to facilitating collaboration across the global research community. Microsoft Research Open Data, in a single, convenient, cloud-hosted location, offers datasets representing many years of data curation and research efforts by Microsoft that were used in published research studies.
With data growing at an exponential rate, perceived to be over 150 ZB of data available by 2025, it is now recognized that we need to prioritize bringing processing to data versus relying on data movement through Internet bandwidth that is growing at a much slower pace. We believe that there is real utility in providing an option to bring the processing to the data. Therefore, in addition to providing an option to download the data assets, users can also copy datasets directly to an Azure based Data Science virtual machine.
Now, one may think that Kaggle is still a better source of free datasets, but given Microsoft's new investments in acquiring Github and now competing with Kaggle one can assume that something is brewing in Satya Nadella's mind.
Let me discuss the Pros and Cons of the same.
Pros :
- Extensive Categorisation of the datasets for various research field which is cross referred by PhD scholars.
- Really easy imports in inter microsoft systems.
Cons :
- Less Datasets (Bu quality data)
- Minimal support for local machines with no azure cloud setup.
- Of course, no API present like that of kaggle that could download the dataset for you within seconds!
So, in my opinion, Kaggle is still the best site for practising your machine learning techniques but Microsoft may have plans for expanding the support in future and that , could be trouble for the current dataset hosting giants.
Let's observe how this new initiative unfolds for either parties. Until then, keep discovering ;)
Uddeshya Singh
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