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    Will the future of Artificial Intelligence be in the Cloud, not in your Office?

    Artificial intelligence and machine learning are quickly coming of age. And it’s not just about robots on the factory floor or acting as virtual assistants in the office.

    Instead, companies are finding that this form of Artificial Intelligence offers pain-free ways to take data and convert it into actionable insights. Especially, when it comes to combining the power of Artificial Intelligence with the ease and seamlessness of the cloud.

    This will be the next shift in cloud-based services, from platforms to Infrastructure as a Service, to databases – to harnessing Artificial Intelligence.



    So far, though, many companies have been without the skills and resources to take full advantage. This is understandable. Teams of Artificial Intelligence experts, access to large data sets, and specialized infrastructure and processing power have been needed, after all. And many up-and-coming companies are yet to reach the size to create and allocate all of these resources.

    That’s where the power of the Cloud comes in. A recent study by Deloitte Global shows that soon, companies will quickly increase their adoption of cloud-based artificial intelligence software and services. Seventy percent of these will obtain AI capabilities through cloud-based enterprise software, and 65 percent will create AI applications using cloud-based development services.

    This isn’t surprising. With the growth in open source and commercially available algorithms, it’s important that time spent on data gathering and pre-processing be reduced. Also important is that such processes be scalable and seamless.

    Cloudera has several next-gen platforms that tackle these issues. There’s the Cloudera Big Data Analytics platform which provides a new cloud-native machine learning service. This can work seamlessly across technologies, providing immense benefits.

    Why should you switch to the cloud for Artificial Intelligence and Machine Learning?

    Efficient and scalable use of data science nowadays requires multiple analytics workloads and machine learning algorithms that’s all operating against the same diverse data sets.
    With many enterprises, they find that analytic workloads are in silos, running separately. The new cloud data warehouses and data science tools just didn’t work together.

    It means the data is dispersed: distributed among data centres and public clouds, for example. In these cases, there’s no practical way to efficiently run analytics or apply machine learning algorithms. It’s a frustrating and ultimately pointless exercise.

    What happens is that a streamlined approach is nearly impossible, or one needs controls that limit productivity and increase costs beyond the limits that are practical.

    What benefits does the cloud offer in such cases?

    Switching to the cloud to use algorithms for data processing means collaboration, reuse, transparency, model management, and data platform integration are deeply integrated and focused, for best results.

    Read: Apache Spark as Dominating Force for Data Analysts

    This is how the future-ready enterprises are mining data successfully, and taking full advantage of the networked age. Cloudera Big Data Solutions has helped several do this successfully so far, and this venture is just beginning.

    This new approach doesn’t merely improve data visibility. That’s just the start, and there are many further benefits. There’s also, for example, the ability to apply multiple analytics disciplines against data anywhere, which is a great advantage.

    So many enterprises, for example, need to process and stream real-time data from multiple endpoints. And predict key outcomes, applying machine learning on that same data set.

    An enterprise data cloud empowers enterprises to get clear and actionable insights from complex data anywhere. It also provides the flexibility to run modern analytic workloads anywhere.

    Moreover, you can move workloads to different cloud environments, to avoid any case of lock-in. This is a future-ready and simple solution.

    With the agility, elasticity, and ease of use of public clouds and a common security and governance framework to enable data privacy by design. You can build intelligent applications that span multiple disciplines and delivery models.

    In short, all the barriers are being dismantled:
    • Enterprises don't need proprietary AI applications.
    • End users don’t have to acquire special knowledge to use AI in enterprise applications.
    • New user interfaces, too, are unnecessary.
    This AI data cloud is different from anything experienced before

    The Cloudera Big Data Analytics will offer predictive analysis on structured and unstructured data sets in the same store for real-time risk decisions and compliance requirements.

    Using this manner of turning data into decisions, enterprises can make the process of building, scaling, and deploying enterprise ML and AI solutions seamless and automated.

    This, then, is what the future holds for enterprises that are bold enough to seize the opportunities that AI offers and to migrate to the cloud. The advantages of a phenomenal increase in calculation power when it comes to treating data and intelligence.

    The focus will be not on technology but it will be on outcomes. The process of learning and optimization will be continual. Data can be used to identify hidden patterns, make classifications, and predict future outcomes. And deliver insightful means of action.

    1 comment:

    1. We will learn about the application using the defacto library OpenCV for image processing. How to build machine learning models when we have limited data is explained as part of this module. artificial intelligence course

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