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Once upon a time, the data that most businesses had to work with was mostly structured and small in size. This meant that it was relatively easy for it to be analyzed using simple business intelligence (BI) tools. Today, this is no longer the case. Much of the data that organizations are mining is unstructured or semi-structured, and the trend is growing such that more than 80% of corporate data is expected to be unstructured by 2020 [1].
On top of this, the rate at which this data is being created is expected to increase at such an extent that IDC predicts the global datasphere will grow from 33 zettabytes (ZB) in 2018 to 175 ZB by 2025 [2].
Simple BI tools are no longer capable of handling this huge volume and variety of data, so more advanced analytical tools and algorithms are required to get the kind of meaningful, actionable insights that businesses need. To keep pace with demand for insights that can drive quicker, better decision making, data scientists are looking to Artificial Intelligence (AI), Machine Learning (ML) and cognitive computing technologies to take analytics to the next level.
No organization can afford to fall behind. As a result, IDC predicts that worldwide spending on AI and cognitive computing will reach $77.6 billion in 2022, more than three times that in 2018 [3], while the total global business value derived from AI is forecast to reach $3.9 trillion within the same timeframe, according to Gartner [4].
The Advent of MLaaS
Getting started with AI techniques such as ML is challenging. Not only does this require highly specific, high-demand skill sets, it may also call for specialized IT infrastructure and software tools—not to mention a sound data strategy. All this adds up to a significant upfront investment that can be cost-prohibitive for many businesses.
In response to this challenge, vendors have begun offering Machine Learning as a Service (MLaaS).
As the name suggests, MLaaS is a subscription-based model that offers access to AI tools, in the same way that many business applications are now offered in a software-as-a-service (SaaS) model. These AI services can range from developer tools to data preprocessing and model training, through to fully-trained ready-to-use models that can be accessed through an API and integrated into business workflows.
The advent of MLaaS means that, instead of investing in creating their own AI resources, organizations will be able to turn to vendors for an easier, lower-cost ecosystem of offerings that can be customized to their needs. This heralds a new era for data science—one in which AI tools become easier to use and more accessible to a broader range of companies and roles within organizations. Click here to read the full article from HP.
[1] https://www.edureka.co/blog/what-is-data-science/
[2] https://www.seagate.com/files/www-content/our-story/trends/files/idc-seagate-dataage-whitepaper.pdf
[3] https://www.idc.com/getdoc.jsp?containerId=prUS44291818
[4] https://www.gartner.com/en/newsroom/press-releases/2018-04-25-gartner-says-global-artificial-intelligence-business-value-to-reach-1-point-2-trillion-in-2018
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