| layout | post |
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| title | Blogging Like a Hacker |
This is second of the series of articles discussing the elements required to make locally developed data science models production-ready
An analytical service that aims to be deployed should have a well thought-out strategy for its data storage. Manually locating an input and pipelining it to output isn’t a pragmatic approach.
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| title | 3 |
This is third article in the series discussing the elements required to make a locally developed data science model to production-ready machine
Choosing models that meet a business objective is the most fundamental aspect of analytics after understanding the business itself. In this regards, distributed deep-learning frameworks have fundamentally transformed the way analytics is done at a scale. But it should also be noted that it is not a panacea, sometimes traditional ML techniques like logistics regression, k-means clustering can be more effective in a manageable dataset and provides easy interpretability to business. But nevertheless, where the dataset is ever growing, ever varying, it is paramount to consider deep-learning platforms as a serious option.