Big Data, ML And Cognitive Services



The term “big data” is being used to describe an increasing range of technologies and techniques.                          In essence big data is data that is valuable but, traditionally, it was not practical to store or analyze it due to limitations of cost or the absence of suitable mechanisms. Big data typically refers to collections of datasets that, due to size and complexity, are difficult to store, query, and manage using existing data management tools or data processing applications.

  • Volume: Big data solutions typically store and query hundreds of terabytes of data, and the total volume is probably growing by ten times every five years. Storage must be able to manage this volume, be easily expandable, and work efficiently across distributed systems. Processing systems must be scalable to handle increasing volumes of data, typically by scaling out across multiple machines.
  • Variety: It’s not uncommon for new data to not match any existing data schema. It may also be semi-structured or unstructured data. This means that applying schemas to the data before or during storage is no longer a practical proposition.
  • Velocity: Data is being collected at an increasing rate from many new types of devices, from a fast-growing number of users, and from an increasing number of devices and applications per user. The design and implementation of storage must be able to manage this efficiently, and processing systems must be able to return results within an acceptable timeframe.

Solution of BIG DATA:- HDInsight

Microsoft Azure HDInsight provides a pay-as-you-go solution for Hadoop-based big data batch processing that is cost-effective because you do not need to commit to installing and configuring on-premises infrastructure. You can instantiate and configure a Hadoop cluster in HDInsight when required, and remove it when it is not required. HDInsight uses a cluster of Azure virtual machines running the Hortonworks Data Platform (HDP), and it integrates with Azure blob storage.



Machine Learning

Machine learning is a data science technique that allows computers to use existing data to forecast future behaviors, outcomes, and trends. Using machine learning, computers learn without being explicitly programmed.

Machine learning is considered a subcategory of artificial intelligence (AI). Forecasts or predictions from machine learning can make apps and devices smarter. When you shop online, machine learning helps recommend other products you might like based on what you’ve purchased. When your credit card is swiped, machine learning compares the transaction to a database of transactions and helps detect fraud. When your robot vacuum cleaner vacuums a room, machine learning helps it decide whether the job is done.


What is Machine Learning in the Microsoft Azure cloud?

Azure Machine Learning is a cloud predictive analytics service that makes it possible to quickly create and deploy predictive models as analytics solutions.

Azure Machine Learning not only provides tools to model predictive analytics, but also provides a fully managed service you can use to deploy your predictive models as ready-to-consume web services.


What is predictive analytics?

Predictive analytics uses math formulas called algorithms that analyze historical or current data to identify patterns or trends in order to forecast future events.

Tools to build complete machine learning solutions in the cloud

Azure Machine Learning has everything you need to create complete predictive analytics solutions in the cloud, from a large algorithm library, to a studio for building models, to an easy way to deploy your model as a web service. Quickly create, test, operationalize, and manage predictive models.

Machine Learning Studio: Create predictive models

In Machine Learning Studio, you can quickly create predictive models by dragging, dropping, and connecting modules. You can experiment with different combinations, and try it out for free.

  • In Cortana Intelligence Gallery, you can try analytics solutions authored by others or contribute your own. Post questions or comments about experiments to the community, or share links to experiments via social networks such as LinkedIn and Twitter.


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