How We Can Get Started Implementing A Machine Learning Solution

Below is the continuation of the transcript of a Webinar hosted by InetSoft on the topic of Enabling the Intelligent Enterprise with Machine Learning. The presenter is Abhishek Gupta, Product Manager at InetSoft.

This brings us to the next point about how we can get started implementing machine learning solutions. First I think we've seen increasing machine learning processing power that's really required, and it enables you to increase your computing power.

Additionally enterprises can implement machine learning when they're dealing with very specific scenarios and very specific conditions. We were discussing already a ton of examples for this. Machine learning is able to make predictions on new data and automate repetitive tasks like the support ticket classification I mentioned earlier, which is a good way to start.

We see also enterprises need to properly prepare the data by minimizing their information silos and developing a real time modern data analytics infrastructure. Now we see many organizations are organized by department and have data silos. They need to integrate the data from all the different sources, such as customers and their supplier sources because otherwise you cannot fully use the algorithms if you don't have the relevant data and if you don't have the right quality of data.

I think what we see as well is a movement towards the cloud for data storage. To have your data all in the cloud in order to process these high volumes of data that are integrated from all various sources. Moreover, enterprises need to align, and this I think is very important because it's not only technology. They need to align their people, the processes and the technology to create a different and a better organizational foundation that supports this digital core and data driven thinking.

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You must have a realtime modern infrastructure that is able to connect, to handle security, to handle the governance, to handle more or less in real time the data quality, the processes and the storage. This will provide you with the ability to gain the insights from the historical data but also the ability to look forward and predict in real time and define actions live in real time.

All this gives enterprises the basis to become data driven and to apply the latest machine learning technologies, but it's really a prerequisite. To expand upon how enterprises can get started with machine learning solutions, what are components for building machine learning products within the enterprise?

I think it differs by what you're trying to address. If you're looking to drive improvements in horizontal functions in areas that your business and other businesses have in common and that are maybe not 100% competitively differentiating for what you do. Finance and human resources and procurement and many other horizontal functions come to mind here.

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You should expect from your standard packaged software provider that they make applications intelligent which enable you apply machine learning to these business scenarios. However, if you're looking to build out highly specific and competitively differentiating solutions you should look to your platform provider to provide you with an array of functionality tools and techniques to build and train your own models.

All of these rest on the foundation of data, so freeing your data from transactional silos, making it available at the detailed granular transaction level so that you can learn from history and have machines understand the patterns and replicate them and drive forward new ways of process improvement or acceleration. It's critical that you unlock this data. Any good ML BI software vendor of course has all the answers for this. Look to the vendor of your choice of trust to help obtain the benefit of business intelligence and machine learning into your organization.

Example of Machine Learning Is Used in Drone Manufacturing

Machine learning plays a pivotal role in revolutionizing drone manufacturing processes, offering innovative solutions across various stages of production. Firstly, in the design phase, machine learning algorithms analyze vast amounts of data from simulations, prototypes, and historical performance metrics to optimize drone designs for aerodynamics, stability, and efficiency. By leveraging generative design techniques, machine learning models can explore a myriad of design possibilities and identify the most optimal configurations based on predefined objectives and constraints. This enables manufacturers to create drones that are not only lighter and more durable but also exhibit superior flight characteristics and energy efficiency, ultimately enhancing overall performance and reliability.

Secondly, machine learning algorithms are utilized in quality control and defect detection during the manufacturing process. Through computer vision and image analysis techniques, these algorithms inspect components, assemblies, and finished products for defects, deviations, or anomalies that may impact performance or safety. By automatically flagging discrepancies or irregularities in real-time, machine learning models enable manufacturers to identify and address quality issues promptly, thereby reducing rework, minimizing scrap, and ensuring compliance with stringent quality standards. Additionally, by analyzing historical quality data, machine learning algorithms can identify patterns and root causes of defects, enabling manufacturers to implement preventive measures and continuous improvement initiatives to enhance overall manufacturing efficiency and product quality.

Lastly, machine learning is employed in predictive maintenance and lifecycle management of drones. By analyzing sensor data, telemetry signals, and operational parameters collected during flights, machine learning models can predict potential failures, malfunctions, or degradation in drone components before they occur. This proactive approach to maintenance enables manufacturers to schedule maintenance activities more efficiently, minimize downtime, and extend the lifespan of drones. Moreover, by leveraging predictive analytics, manufacturers can optimize spare parts inventory, forecast demand for maintenance services, and develop predictive maintenance schedules tailored to specific usage profiles and environmental conditions. Overall, machine learning empowers drone manufacturers to deliver high-quality, reliable products while maximizing operational efficiency and customer satisfaction.

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