Virtual Community Day: Cloud Migration November 14. 10A - 7PM (ET)
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November 2019 | Volume 1, Issue 4 Spotlight Technique Makes it Easier for AI to ...
As a part of the IBM Data Science Elite ( DSE ) engagements, we've had many conversations with customers about enabling CI/CD in the ...
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As a part of the IBM Data Science Elite (DSE) engagements, we've had many conversations with customers about enabling CI/CD in the machine learning (ML) pipeline - as a result I've decided to summarize the steps in a blog. The blog outlines the steps for implementing CI/CD workflows using IBM Watson Studio, and IBM Watson Machine Learning...Read more
NumPy is a powerful Python library that can greatly increase the speed and efficiency of processing large data sets. Several data science and machine learning frameworks work in conjunction with or are built on top of NumPy.
As Machine Learning (ML) is becoming an important part of every industry, the demand for Machine Learning Engineers (MLE) has grown dramatically. MLEs combine machine learning skills with software engineering knowhow to find high-performing models for a given application and handle the implementation challenges that come up — from building out training infrastructure to preparing models for deployment. New online resources have sprouted in parallel to train engineers to build ML models and solve the various software challenges encountered. However, one of the most common hurdles with new ML teams is maintaining the same level of forward progress that engineers are accustomed to with traditional software engineering.
Time Series is one domain which has been using some form or other of predictive analysis since long before the birth of contemporary machine learning. Once upon a time, our ancestors tracked the location and movement of the moon and the stars to decide when to move from place to place, when to hunt, and when to sow the seeds in the expectation of rain. In doing so they had figured out cycles and seasonality in the flow of time — something we now call the cyclical and seasonal components of a time series.
As a knowledge worker, data scientist, or business analyst you are probably spending a big amount of your time refining and wrangling data before you can use it for further analytics and machine learning.