Future of IoT Using Machine Learning

Future of IoT Using Machine Learning

Future of IoT Using Machine Learning
Future of IoT Using Machine Learning

Abbreviation of IoT is Now a days common and known to all as “Internet of Thing”. IoT has a very good future in India by 2030 we have lot of jobs in the same field. This is an opportunity in the Indian Market and the same is misused by Many …Future of IoT Using Machine Learning

In terms of Teaching ,Learning and Training for the name sake some private startups teach how to lit the LED using Arduino and connecting some sensors using Bluetooth, WiFi etc and by doing this , they are not only wasting the time of young generation but also increased resistance in the growth of the country.

To understand IoT one should have to learn the concept of IoT broadly including What are the problems that industry facing due to which it is not completely feasible yet.

Earlier we have a problem related to limited IP addresses which is now overcome as IPv6 comes into existences. The only hurdle left is sensor technology, which is very costly and not much accurate.

According to me one should have to look into some another dimensions for the complete feasibility of IoT. One of the possible way is, What I called Super AI a combination of AI and ML.

Reason Why it so because earlier we have limited data sets due to which machines are not much accurate to predict. With the advancement in the Super AI which probably replace Sensor technology in upcoming years.

The era of Super AI in which the new transformation occur in the digital technologies and where complete IoT is possible using Super AI.

The Internet of Things generates massive volumes of data from millions of devices.    ML is powered by data and generates insight from it.    ML uses past behavior to identify patterns and builds models that help predict future behavior and events.

What is ML?

Internet of Things and    ML deliver insights otherwise hidden in data for rapid, automated responses and improved decision making.    ML for Internet of Things can be used to project future trends, detect anomalies, and augment intelligence by ingesting image, video and audio.

Why use ML for IoT?

ML can help demystify the hidden patterns in Internet of Things data by analyzing massive volumes of data using sophisticated algorithms.    ML inference can supplement or replace manual processes with automated systems using statistically derived actions in critical processes.

Sample use cases
Companies are utilizing    ML for Internet of Things to perform predictive capabilities on a wide variety of use cases that enable the business to gain new insights and advanced automation capabilities.

With    ML for IoT, you can:

  • Ingest and transform data into a consistent format
  • Build a  ML model
  • Deploy this    ML model on cloud, edge and device

For example, using  ML, a company can automate quality inspection and defect tracking on its assembly line, track activity of assets in the field and forecast consumption & demand patterns.

Benefits of ML inference for IoT

ML is a key component of Software Internet of Things low-code, self-service Internet of Things platform. The platform comes ready to go with the tools you need for fast results: device connectivity and management, application ennoblement and integration, as well as streaming analytics,    ML, and    ML model deployment. The platform is available on the cloud, on-premises and/or at the edge. Uniquely with     IoT, standalone, edge-only solutions are also supported.

Simplify    ML model training
    Internet of Things   ML is designed to help you quickly build new    ML models in an easy manner. Auto ML support allows the right    ML model to be chosen for you based on your data, whether that be operational device data captured on the     Internet of Things platform or historical data stored in big data archives.

Flexibility to use your data science library of choice
There are a wide variety of data science libraries available (e.g., Tensorflow®, Keras, Scikit-learn) for developing    ML models.    ML allows models to be developed in data science frameworks of your choice. These models can be transformed into industry-standard formats using open source tools.

Rapid model deployment to operationalize    ML quickly
Whether created within     Internet of Things   ML itself or imported from other data science frameworks, model deployment into production environments is possible wherever needed in one click, either in the cloud or at the edge. Operationalized models can be easily monitored and updated if underlying patterns shift. Additionally, pretrained and verified models are available for immediate model deployment to accelerate adoption.

Pre built connectors for operational & historical data stores
    Internet of Things   ML provides easy access to data residing in operational and historical datastores for model training. It can retrieve this data on a periodic basis and route it through an automated pipeline to transform the data and train a    ML model. Data can be hosted on Amazon® S3 or Microsoft® Azure® Data Lake Storage, as well as local data storage, and retrieved using prebuilt  Internet of ThingsDataHub connectors.

Integration with     Internet of ThingsStreaming Analytics
    Internet of Things   ML enables high-performance scoring of real-time Internet of Things data within     Internet of Things Streaming Analytics.     Internet of Things Streaming Analytics provides a “   ML” building block in its visual analytics builder that allows the user to invoke a specified    ML model to score real-time data. This provides a no-code environment to integrate    ML models with streaming analytics workflows.

Notebook integration
Jupyter Notebook, a de facto standard in data science, provides an interactive environment across programming languages. They can be used to prepare and process data, train, deploy and validate    ML models. This open-source web application is integrated with     Internet of Things   ML.

I already started working on this and will soon update my findings.


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