KUALA LUMPUR, July 29 (Bernama) -- Bursa Malaysia rebounded to close higher on Wednesday on selective buying of defensive stocks after a volatile trading session. IPPFA Sdn Bhd director of investment strategy and country economist Mohd Sedek Jantan said consumer products and services stocks lifted the key index higher, overcoming lingering geopolitical concerns. At 5 pm, the FTSE Bursa Malaysia KLCI (FBM KLCI) rose 3.08 points to 1,715.56 from yesterday’s close of 1,712.48. The benchmark index, which opened 1.91 points higher at 1,714.39, moved between 1,710.79 and 1,720.59 during the day’s trading. In the broader market, gainers outstripped decliners 550 to 476, while 612 counters were unchanged, 1,129 untraded, and 48 suspended. Turnover rose to 2.96 billion units valued at RM2.48 billion from 2.94 billion units valued at RM2.56 billion on Tuesday.
If you think Microsoft's new CEO is making another bold statement without action, think again. Microsoft's new CEO, Satya Nadella is serious about cloud computing and he has a strategy.
A supposedly comprehensive predictive analysis service — and all you have to do is store your data in Azure, the Microsoft cloud.
The service will be known as Microsoft Azure Machine Learning (ML) was announced on Monday but will only be available in June. This is the first time where Microsoft combines their very own software with publicly available open source software, so that it's much more easier for usage than most of the available arcane strategies that are available now.
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| Machine Learning - fixing today's problem yesterday |
The VP for ML at Microsoft proudly said, "This is drag-and-drop software."
This is a big step forward in popularizing what is currently a difficult process in increasingly high demand. It would also further the ambitions of Satya Nadella, Microsoft’s chief executive, of making Azure the center of Microsoft’s future.
Machine learning computers examine historical data through different algorithms and programming languages to make predictions. The process is commonly used in Internet search, fraud detection, product recommendations and digital personal assistants, among other things.
As more data is automatically stored online, there are opportunities to use machine learning for performing maintenance, scheduling hospital services, and anticipating disease outbreaks and crime, among other things. The methods have to become easier and cheaper to be popular, however.
That is the goal of Azure Machine Learning.
Here is a video posted by Microsoft on Youtube:
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