Artificial Intelligence and Deep learning in the Cloud are believed to change the world one day, thanks to the massive Cloud Computing technology power.
For a long time, enterprises have decided to dip a toe into the artificial intelligence, going first with machine learning. However, until today, the demand for such data turns out to be quite in a rush to keep a competitive edge. Therefore, it’s best to learn how his classic machine learning has developed from deep learning based on the great power of cloud computing. And it’s true that deep learning systems have grown up considerably, but have you wonder if these tools are all ready to bring out any business?
Since both the classic machine learning and deep learning can show your computers how to learn and do tasks that used be performed by the human, they’re quite different in complexity. More specifically, when machine learning is known to deal just a small number of data streams, the deep learning can even read much data than that -- besides, that data won’t need to get categorized for the system to comprehend it.
As a result of it, the second system is recognized to be able to make more rich and contextually-based conclusions. So with deep learning in the Cloud, you get to know the specific features of a photo, learn more from them and understand the picture as a whole. Don’t mind feeding it a photo of you standing close to a license plate at a beach in the background. The deep learning probably concludes that you went on a beach somewhere else last summer.
Also, it knows to offer higher level of precision than other techniques for issues that relate to complicated data fusion. A few organizations have already availed these systems to handle any pressing issue. For instance, NASA grew a deep learning network for satellite image classification while Insilico Medicine established a deep learning framework to search the best treatments for serious illnesses like cancer and other diseases.
1/ You need more data: Deep learning can’t work without a big volume of data, and the data should be suitable, precise, and free from bias. And such huge volume of decent data could be hard to come by.
If you’re a huge company, you probably invest in another firm to gain more information. For instance, IBM took Weather company since it likes to offer Watson - an artificial technology with weather-based data. It also purchased many other healthcare enterprises for dollars to gain thousands of patient records along with a lot of information on costs and insurance claims.
2/ You must train a deep learning network, and once you obtain the data, you should feed it to it so that it possibly learn from it. Training serves an essential part of the whole deep learning implementations, which is always a compute-intensive one that takes several days of your computer time.
Apart from this, the training sets are big and exceedingly hard to curate. Any output error driven by errors in the input data is very hard to correct since all correlations can’t be visible to humans.
3/ You must have some tools to carry out your deep learning system, and these enterprise-grade ones are not plentiful enough at this point. There are countless companies need to grow their tools utilizing open source tools as a base layer. In other words, we also see very few organizations having the ability to do so.
For any vendor, this might need more powerful deep learning platforms that enable you to launch AI solutions for the company must be seen as a chance.
The second one is called point methods. In all cases, these solutions are grown by every startup utilizing open source tools, the entire time borrowing well-tested algorithms from the open libraries.
The boutique vendors always deal with a set of issues for one specific sector. The whole intellectual property is not necessarily the algorithm, but the data collected and their understanding of the sector. What they sell is domain knowledge, issue-area expertise and the data sets that can fuel the algorithms.
Conclusion
Deep learning in the Cloud will change the world for sure, but for now, as we see, very few vendors specializing in data collecting and cleansing that the strategists need. What’s else? These tools might need to grow more before any IT pros can find it practical to use such robust algorithms. Until the ecosystem of vendors grows further, deep learning system is a goal for many companies.
For a long time, enterprises have decided to dip a toe into the artificial intelligence, going first with machine learning. However, until today, the demand for such data turns out to be quite in a rush to keep a competitive edge. Therefore, it’s best to learn how his classic machine learning has developed from deep learning based on the great power of cloud computing. And it’s true that deep learning systems have grown up considerably, but have you wonder if these tools are all ready to bring out any business?
Machine learning and deep learning teach humans how to learn and do tasks
A lot of manufacturing firms, for example, have utilized classic machine learning for maintenance and identify tool failure, allowing the technicians to solve possible issues. However, such out-of-date systems as compared with what is done today, availing more advanced algorithms that make use of the modern computing power.Since both the classic machine learning and deep learning can show your computers how to learn and do tasks that used be performed by the human, they’re quite different in complexity. More specifically, when machine learning is known to deal just a small number of data streams, the deep learning can even read much data than that -- besides, that data won’t need to get categorized for the system to comprehend it.
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| Deep learning teaches your computer how to do tasks previously performed by humans |
Deep learning in the Cloud and their app in enterprise AI
All deep learning systems are the topic of several hypes these days. However, under such noises, experts state, is a set of techniques that will certainly change every business. Following Gartner’s 2017 report Innovation Insight for Deep Learning, this is apparently the most potential technology in term of predictive analytics for intractable data for machine learning, from images, speed to video. |
| Deep learning is running on a Cloud platform |
Most common challenges for deep learning in the Cloud
The market is now in quite an early phase and just a few organizations that bear an issue that is truly worth the pioneering costs coming with the complicated technology. Besides, the three challenges here that adopters must go through would be one of the following: |
| Challenges for deep learning in the Cloud |
If you’re a huge company, you probably invest in another firm to gain more information. For instance, IBM took Weather company since it likes to offer Watson - an artificial technology with weather-based data. It also purchased many other healthcare enterprises for dollars to gain thousands of patient records along with a lot of information on costs and insurance claims.
2/ You must train a deep learning network, and once you obtain the data, you should feed it to it so that it possibly learn from it. Training serves an essential part of the whole deep learning implementations, which is always a compute-intensive one that takes several days of your computer time.
Apart from this, the training sets are big and exceedingly hard to curate. Any output error driven by errors in the input data is very hard to correct since all correlations can’t be visible to humans.
3/ You must have some tools to carry out your deep learning system, and these enterprise-grade ones are not plentiful enough at this point. There are countless companies need to grow their tools utilizing open source tools as a base layer. In other words, we also see very few organizations having the ability to do so.
For any vendor, this might need more powerful deep learning platforms that enable you to launch AI solutions for the company must be seen as a chance.
Is deep learning enterprise-ready yet?
When the market grows up, there would be two different approaches starting to show up. The first one will be the platform. Most of these like Google’s TensorFlow, which are quite handy for every researcher, but not as useful for the enterprise. Occasionally, very few available platforms work quite well in the company. Even those still need a lot of tweaking and engineering.The second one is called point methods. In all cases, these solutions are grown by every startup utilizing open source tools, the entire time borrowing well-tested algorithms from the open libraries.
The boutique vendors always deal with a set of issues for one specific sector. The whole intellectual property is not necessarily the algorithm, but the data collected and their understanding of the sector. What they sell is domain knowledge, issue-area expertise and the data sets that can fuel the algorithms.
Conclusion
Deep learning in the Cloud will change the world for sure, but for now, as we see, very few vendors specializing in data collecting and cleansing that the strategists need. What’s else? These tools might need to grow more before any IT pros can find it practical to use such robust algorithms. Until the ecosystem of vendors grows further, deep learning system is a goal for many companies.
Machine Learning and Deep Learning In The Cloud: Are You Ready?
Reviewed by thanhcongabc
on
June 04, 2018
Rating:
Reviewed by thanhcongabc
on
June 04, 2018
Rating:
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