The future of cloud computing and machine learning: Solution for Africa



The future of cloud computing and machine learning: Solution for Africa

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Cloud computing has come a long way over the last several years. Innovation is increasing at rates never experienced before and will continue to do so.
Cloud computing started as a way to abstract physical infrastructure and data centers. The next generation of cloud computing is focusing on abstracting virtual infrastructure and the operational processes that go along with managing that infrastructure. Through cloud computing, complex technologies like machine learning will be made simple, so data scientists can focus on the data, and developers can enable the data scientists without having to become experts at the underlying machine learning technologies.
Most tech companies have been blazing the path for big data for almost two decades.  Now they are shifting the conversation from big data analytics to deep learning.  I believe that machine learning is the next layer of programming.  It entails the process of data ingestion, storage and training machine models as simple as calling an API. 
Image result for google cloud servicesWe’ve known for years that most big data initiatives fail in the data ingestion phase. Google has made this process very easy with APIs like Pub/Sub, DataFlow, and others. We also see data scientists complaining that they spend up to 80% of their time preparing the data and training the models before they can even begin to extract any value out of the current machine learning technologies. In fact, some data scientists sarcastically call themselves “data janitors” because they spend more time preparing data than they do analyzing it.
Google has speech, sound and image recognition services that can analyse images and categorise into thousands of categories.   Its has developed APIs that can take an image of a person and detect race, gender, and mood. It can also look at the background and possibly determine the location of the person and relevant information about the location. It can also read text within the image itself and return it to the user as metadata.
If we have to practically apply this technology to something of image of value in Africa, we can start seeing results.   Imagine a healthcare company working with images generated from X-rays and MRIs. The Vision API can return various metadata about what it sees within each image. Then machine learning models can be trained to make predictions based on the image metadata. When applied to industries like healthcare, image recognition and machine learning can could lead to new methods of early detection of diseases like cancer. My guess is that these technologies will be used to solve problems and predict outcomes for things that we haven’t even thought about yet.

As machine learning becomes easier to work with, companies will shift from performing analytics to deep learning. These technologies will undercover new information that can transform industries and business models in Africa

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