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The 5 exciting machine learning, data science and big data trends for 2019

big data trends

The 5 exciting machine learning, data science and big data trends for 2019 Big data and analytics have become crucial to business. But will that spine develop, or will it change the landscape of business yet again? Here’s a sneak peek into what the following months look like. Just a while ago big data was a lucrative new phenomenon promising a smooth business takeover. Now, since data and analytics are imperative to business and deeply embedded, the question arises whether technology will have a growth spurt in the coming year, continue to mold and restructure businesses or be replaced by something else. The answer, it turns out, is a little bit of all. Below are the top predictions of what we can expect from big data in 2019. Tools of Automated machine learning What’s great about automated machine learning? Well, it has pre-existing tools thus making it simple to execute.  AutoML, developed by Google, has which is a set of tools that can be used on the Google Cloud Platform, whereas, Auto-sklearn, developed around the scikit-learn library, dishes out a similar revolutionary solution for automated machine learning. Despite both AutoML and auto-sklearn being new, newer tools are available which could dominate the landscape: AutoKeras and AdaNet. AutoKeras is built on Keras (the Python neural network library), while AdaNet is built on TensorFlow. Both of these could give competition to AutoML as more affordable alternatives. Only time will tell which automated machine learning library stands to gain the most popularity, but one thing is certain: it makes deep learning accessible to many organizations that previously wouldn’t have had the resources or inclination to hire a team of highly qualified PhD computer scientists. But it’s important to remember that automated data science is not implied by automated machine learning. While tools like AutoML will help many organizations build deep learning models for basic tasks, for organizations that require a more complex data strategy, the role of the data scientist will continue to remain vital. After all, automating strategy and decision making is almost impossible. Booming IOT networks Thanks to the revolutionary technology called the internet of things (IoT), it’s becoming quite common that our smartphones are being used to control our home appliances. With smart devices such as Google Assistant and Microsoft Cortana becoming a norm in homes to automate specific tasks, the growing IoT craze is drawing companies to invest in the technology’s development. More organizations will cash in on the opportunity to provide better IoT solutions. This will lead to more methods of collecting data coming up, and along with it, the means to manage and analyze it. The industry response is to push for the creation of more and more new devices that are more capable of collecting, analyzing and processing data. Quantum computing Quantum computing, even as a concept, feels almost like a fantasy. It’s not just pioneering, it’s mind-boggling. But in real-world terms, it also continues to propagate the theme of doing more with less. Explaining quantum computing is not child’s play, but the fundamentals are this: instead of a binary system (the foundation of computing as we currently know it), which can be either 0 or 1, in a quantum system you have qubits, which can be 0, 1 or both simultaneously. (To know more, read this article). All the tech biggies like Microsoft, Intel, Google and IBM are vying for the top slot in quantum computing. So, what’s the great attraction for quantum computing? Apart from seamless encryption of data, weather prediction, solutions to long-standing medical problems, it has a lot more to offer. Quantum computing allows real conversations between customers and organizations. As a plus point, there is the promise of revised financial modeling that helps organizations develop quantum computing components along with applications and algorithms. The harnessing of dark data Gartner defines dark data as “the information assets which organizations collect, process and store during regular business activities, but generally fail to use for other purposes.” Usually, this kind of data is often recorded and stashed away for compliance purposes only, taking up a lot of storage without actually being used  or monetized either directly or through analytics to gain a competitive advantage. But with organizations increasingly leaving no stone unturned in relation to business intelligence, we’re likely to see more emphasis placed on this as-of-yet relatively untapped resource, including the digitization of analog records and items (ranging from dusty old files to fossils in museums) and their integration into the data warehouse. Dataops The emerging concept of DataOps gained momentum this year, and is bound to grow significantly in relevance in 2019; as data pipelines become more complex and would require even more integration and governance tools. The Agile and DevOps methods are applied by DataOps to the entire data analytics lifecycle, from collection, to preparation, to analysis, employing automated testing and delivery for better data quality and analytics. DataOps uses statistical process control to monitor the data pipeline to ensure constant, consistent quality, and promotes quality, collaboration and continuous improvement. When experts predict organizations should be able to handle 1,000 data sources in their data warehouse, what it translates to; is that truly automated and always-on data integration will be the difference between delivering value and sinking. Your thoughts? Well these were a few of my predictions for 2019. It would be interesting to know what you think will be the dominant trend in this market.

Manu Jeevan

Manu Jeevan is a self-taught data scientist and loves to explain data science concepts in simple terms. You can connect with him on LinkedIn, or email him at manu@bigdataexaminer.com.
Manu Jeevan
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