Unraveling Insights from Big Data for Better Decision Making – By Bhanuka- eLanka In today’s digital age, data has become the lifeblood of businesses across industries. With the exponential growth of data, organizations are increasingly turning to big data analytics to derive valuable insights that drive informed decision-making. In this article, we delve into the world of big data analytics, exploring its significance, methodologies, and the transformative impact it has on businesses. The Significance of Big Data Analytics: In a world inundated with data, the ability to extract actionable insights is paramount. Big data analytics offers the capability to process vast volumes of structured and unstructured data, uncovering patterns, trends, and correlations that traditional analytics methods may overlook. By harnessing the power of big data, organizations can gain a comprehensive understanding of their operations, customers, and market dynamics. Methodologies of Big Data Analytics: Big data analytics encompasses a diverse range ...
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Is ModelOps important to manage AI/ML models? – By Aditya Abeysinghe What is ModelOps? Machine learning (ML) is used in many platforms today. ML modelling algorithms and ML-based programming have made it easier over the last decade to find the best ML model for a given app by training data. Different stages are used in the deployment of a ML model or an Artificial Intelligence (AI) model. Finding suitable datasets, preparing data chosen to train models, training models, testing models, deploying the model, and monitoring the deployed model are some common stages used. These stages of deploying and monitoring models is called Model Operationalization (ModelOps). Why ModelOps? As each phase of the deployment of models is monitored by ModelOps, issues in a phase can be identified earlier and mitigated before they are passed to other phases. This could reduce risks and costs associated with rectifying issues in later phases. Also, ...
Is ML changing process analytics? – By Aditya Abeysinghe Process analytics Process analytics uses data from current processes of a business to analyze and identify how it could predict future processes of a business. For analytics, different sources of data and different types of data could be used. Insights can be derived using different types of process analysis. Business decisions can be made using output of these analytics which help businesses to improve their business processes. Why use analytics? There are different types of business process analytics. One type of analytics is descriptive analytics and this type analyzes historical data to describe current trend of a business process. This way a business can find where issues have occurred, the historical trends of a business process, and how current business trends have changed from that of past trends. This method enhances businesses to gain information from data and make decisions. Another ...
Emerging marketing trends with ML – By Aditya Abeysinghe Marketing has improved its reach to target customers over the last decade with the use of newer technologies. While marketing using online websites and mobile applications are the widely used methods, new methods of online marketing are rising that use machine learning models. These emerging marketing methods have enhanced marketing to reach a large volume and variety of market types. Analyzing target market segments Machine learning (ML) is used to analyze target markets. The common method of finding target markets is to group people of similar characteristics such as by location, gender and purchasing methods. However, this is often difficult for large businesses that market services or products due to the large variety of customers. ML is used to identify people with similar behavior based on methods such as classification and clustering. ML could also be used with other systems to ...
The changing nature of large scalable AI and ML models – By Aditya Abeysinghe Distributed AI Artificial Intelligence (AI) models are often costly to operate due to high Central Processing Unit (CPU) power required to process data. AI models also depend on additional constraints such as volatile memory, secondary memory and power. Most medium and large scale AI models are processed on cloud or remote data center servers which have high CPU power and memory compared to small scale processing devices due to these issues. However, sending data to a remote server is often not viable due to extra costs of data communication, costs of maintaining and monitoring servers in cloud and due to issues with privacy. Distributed AI is a method to distribute AI models outside a server to a location close to a data source. With distributed AI, AI models are deployed at either the source or at ...
Changing cybersecurity use with AI models – By Aditya Abeysinghe Use of AI and Machine learning There are many uses of Artificial Intelligence (AI) and machine learning (ML) in cybersecurity. The advantage of using AI and ML for cybersecurity is that AI and ML models can learn and then identify attacks which are known and/or unknown in a system. Models which are based on AI and ML can identify anomalies when there is a change of usual traffic. In classification, content is classified as attacks based on the output of a model. Benefits of AI models to identify attacks AI models can be used to automate detection of attacks more effectively than by using other methods. AI models are used to enhance anomaly identification or classifications in networks with less time than manual identification based on analysis. Unlike other attack classification and anomaly detection methods, methods that use AI ...
How vision is shaping retail: A booster of consumers? By Aditya Abeysinghe Retail is everything from simple grocery stores to shopping malls. The growth of transportation, production of items, and related stack of tasks has helped retail to expand exponentially in the recent past. Today, retail stores use technology for requirements from scanning goods to logistics. AI (Artificial Intelligence) is a new addition to technologies used in retail. Several advantages of using AI in shopping can be seen in the past few years by several retailers. In this article I will discuss about use of vision in retail with AI. What is vision? Computer vision includes methods used to identify, analyze, and predict an object from an image or any other graphic media. A popular use of vision is autonomous vehicles. Autonomous vehicles use sensors and cameras to detect objects and then apply the action for that identified instance. ...
Ensuring protection in data sharing: Privacy Enhancing Computation By Aditya Abeysinghe Data privacy has become a much debated issue with a plethora of personal and business communication applications, websites and mobile apps available today. Who intercepts data, who has authority over data and what is done to these stored data without user consent has caused users to concern about their privacy when communicating over third party applications. While many applications today ensure encryption, masking and other techniques to hide the original form during transmission between two users many say that these techniques are easily decodable by eavesdropping middle parties during transmission. Therefore, research has focused on a new avenue to solve this issue by enforcing technologies that ensure privacy of data. These collections of technologies used for ensuring consumer privacy is called Privacy Enhancing Computation (PEC). How does PEC ensure privacy? Data used at present is mainly of two ...
Hyperautomation: Article 3 of a series of articles on Hyperautomation By Aditya Abeysinghe This article series discussed about what is automation, how to identify which processes are to be automated and how robotic process automation can be used to automate processes. Robotic process automation (RPA) used robots to automate tasks which are usually performed by humans using multiple processes and tools. Using Artificial Intelligence (AI) and Machine Learning (ML) processes can be further automated, so that the dependency on humans is further minimized. This type of combining automations using AI and ML to automate processes is called hyperautomation. Why hyperautomation? Before addressing why hyperautomation is necessary, we must understand the difference between automation and hyperautomation. Consider the same example, I used in the RPA article *. A robot is used to read a list of contacts and send invoices to these contacts via email. In this process, the list ...
