UNSUPERVISED MACHINE LEARNING TO IMPROVE DATA QUALITY

August 3, 2020 | 200 views

When preparing data, I often go through many different approaches to reach a level of quality of data that can provide accurate results. In this article, I describe how unsupervised ML can help in data preparation for machine learning projects and how it helps to get more accurate business insights. What’s Wrong with Traditional Data Preparation Approaches? For accurate predictions, the data must not only be properly labeled, de-deputed, broad, consistent, etc. The point is that the machine learning model should process the “right” data. It is not entirely clear what are the criteria of the “right” data.

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Wipro

Wipro Ltd. (NYSE:WIT) is a leading Information Technology, Consulting and Business Process Services company that delivers solutions to enable its clients do business better. Wipro delivers winning business outcomes through its deep industry experience and a 360 degree view of "Business through Technology" - helping clients create successful and adaptive businesses. A company recognized globally for its comprehensive portfolio of services, a practitioner's approach to delivering innovation, and an organization wide commitment to sustainability, Wipro has a workforce of over 170,000, serving clients in 175+ cities across 6 continents.

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FUTURE TECH

AI's Impact on Improving Customer Experience

Article | July 14, 2022

To enhance the consumer experience, businesses all over the world are experimenting with artificial intelligenace (AI), machine learning, and advanced analytics. Artificial intelligence (AI) is becoming increasingly popular among marketers and salespeople, and it has become a vital tool for businesses that want to offer their customers a hyper-personalized, outstanding experience. Customer relationship management (CRM) and customer data platform (CDP) software that has been upgraded with AI has made AI accessible to businesses without the exorbitant expenses previously associated with the technology. When AI and machine learning are used in conjunction for collecting and analyzing social, historical, and behavioral data, brands may develop a much more thorough understanding of their customers. In addition, AI can predict client behavior because it continuously learns from the data it analyzes, in contrast to traditional data analytics tools. As a result, businesses may deliver highly pertinent content, boost sales, and enhance the customer experience. Predictive Behavior Analysis and Real-time Decision Making Real-time decisioning is the capacity to act quickly and based on the most up-to-date information available, such as information from a customer's most recent encounter with a company. For instance, Precognitive's Decision-AI uses a combination of AI and machine learning to assess any event in real-time with a response time of less than 200 milliseconds. Precognitive's fraud prevention product includes Decision-AI, which can be implemented using an API on a website. Marketing to customers can be done more successfully by using real-time decisioning. For example, brands may display highly tailored, pertinent content and offer to clients by utilizing AI and real-time decisioning to discover and comprehend a customer's purpose from the data they produce in real-time. By providing deeper insights into what has already happened and what can be done to facilitate a sale through suggestions for related products and accessories, AI and predictive analytics are able to go further than historical data alone. This increases the relevance of the customer experience, increases the likelihood that a sale will be made, and increases the emotional connection that the customer has with a brand.

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SOFTWARE

The Evolution of Quantum Computing and What its Future Beholds

Article | August 8, 2022

The mechanism of quantum computers will be entirely different from anything we humans have ever created or constructed in the past. Quantum computers, like classical computers, are designed to address problems in the real world. They process data in a unique way, though, which makes them a much more effective machine than any computer in use today. Superposition and entanglement, two fundamental ideas in quantum mechanics, could be used to explain what makes quantum computers unique. The goal of quantum computing research is to find a technique to accelerate the execution of lengthy chains of computer instructions. This method of execution would take advantage of a quantum physics event that is frequently observed but does not appear to make much sense when written out. When this fundamental objective of quantum computing is accomplished, and all theorists are confident works in practice, computing will undoubtedly undergo a revolution. Quantum computing promises that it will enable us to address specific issues that current classical computers cannot resolve in a timely manner. While not a cure-all for all computer issues, quantum computing is adequate for most "needle in a haystack" search and optimization issues. Quantum Computing and Its Deployment Only the big hyperscalers and a few hardware vendors offer quantum computer emulators and limited-sized quantum computers as a cloud service. Quantum computers are used for compute-intensive, non-latency-sensitive issues. Quantum computer architectures can't handle massive data sizes yet. In many circumstances, a hybrid quantum-classical computer is used. Quantum computers don't use much electricity to compute but need cryogenic refrigerators to sustain superconducting temperatures. Networking and Quantum Software Stacks Many quantum computing software stacks virtualize the hardware and build a virtual layer of logical qubits. Software stacks provide compilers that transform high-level programming structures into low-level assembly commands that operate on logical qubits. In addition, software stack suppliers are designing domain-specific application-level templates for quantum computing. The software layer hides complexity without affecting quantum computing hardware performance or mobility.

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SOFTWARE

Language Models: Emerging Types and Why They Matter

Article | July 13, 2022

Language model systems, often known as text understanding and generation systems, are the newest trend in business. However, not every language model is made equal. A few are starting to take center stage, including massive general-purpose models like OpenAI's GPT-3 and models tailored for specific jobs. There is a third type of model at the edge that is intended to run on Internet of Things devices and workstations but is typically very compressed in size and has few functionalities. Large Language Models Large language models, which can reach tens of petabytes in size, are trained on vast volumes of text data. As a result, they rank among the models with the highest number of parameters, where a "parameter" is a value the model can alter on its own as it gains knowledge. The model's parameters, which are made of components learned from prior training data, fundamentally describe the model's aptitude for solving a particular task, like producing text. Fine-tuned Language Models Compared to their massive language model siblings, fine-tuned models are typically smaller. Examples include OpenAI's Codex, a version of GPT-3 that is specifically tailored for programming jobs. Codex is both smaller than OpenAI and more effective at creating and completing strings of computer code, although it still has billions of parameters. The performance of a model, like its capacity to generate protein sequences or respond to queries, can be improved through fine-tuning. Edge Language Models Edge models, which are intentionally small in size, occasionally take the shape of finely tuned models. To work within certain hardware limits, they are occasionally trained from scratch on modest data sets. In any event, edge models provide several advantages that massive language models simply cannot match, notwithstanding their limitations in some areas. The main factor is cost. There are no cloud usage fees with an edge approach that operates locally and offline. As significant, fine-tuned, and edge language models grow in response to new research, they are likely to encounter hurdles on their way to wider use. For example, compared to training a model from the start, fine-tuning requires less data, but fine-tuning still requires a dataset.

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SOFTWARE

Low-code and No-code: A Business' New Best Friend

Article | July 5, 2022

Businesses are starting to integrate artificial intelligence (AI) into their workflow in greater numbers as a result of the growth of digital transformation and developments in machine learning (ML). As a result, platforms that need no coding, as well as their low-code counterparts, are becoming more popular. This development is a step toward computer science's long-term objective of automating manual coding. Low-code/no-code AI platforms will be beneficial to businesses in more data-driven industries like marketing, sales, and finance. AI can assist in a variety of ways, including automating invoicing, evaluating reports, making intelligent suggestions, and anticipating churn rates. How Does an Organization Look at Low-code/No-code as the Future? Developers and other tech-related positions are in high demand, particularly in the fields of AI and data science. Organizations have the chance to close the gap with the aid of citizen data scientists who don't require an AI professional to design unique AI solutions for many scenarios, thanks to low-code and no-code AI technologies. The demand for technological solutions and AI technologies is rising significantly as the technological landscape rapidly changes. AI systems, for example, require complex software that uses a lot of code, a variety of frameworks, and the Internet of Things (IoT). One person's capacity to comprehend every technical detail is strained by the array of complicated technology. Software delivery must be timely, effective, and secure while maintaining high standards. Conclusion Low-code AI solutions offer the speed, ease of use, and adaptability of ready-made software solutions while also drastically reducing the time to market for AI solutions and the cost of recruiting software and computer vision engineers. Organizations are free to construct the architecture, functionality, or pipeline that best suits their project, the sky being the limit. However, creating such unique models may be both costly and time-consuming. Therefore, employing low-code/no-code platforms would apply to particular pipeline actions that would streamline and accelerate the processes.

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Spotlight

Wipro

Wipro Ltd. (NYSE:WIT) is a leading Information Technology, Consulting and Business Process Services company that delivers solutions to enable its clients do business better. Wipro delivers winning business outcomes through its deep industry experience and a 360 degree view of "Business through Technology" - helping clients create successful and adaptive businesses. A company recognized globally for its comprehensive portfolio of services, a practitioner's approach to delivering innovation, and an organization wide commitment to sustainability, Wipro has a workforce of over 170,000, serving clients in 175+ cities across 6 continents.

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AI TECH,GENERAL AI

Altair Announces Completion of Acquisition of RapidMiner

Altair | September 17, 2022

Altair , a global leader in computational science and artificial intelligence (AI), has completed the acquisition of RapidMiner, a leader in advanced data analytics and machine learning (ML) software. RapidMiner's well-established desktop platform and new-to-market cloud platform (multi-tenant and SaaS ready) strengthens Altair's current end-to-end data analytics (DA) portfolio, which already offers customers the power to understand, transform, act on, and automate their data. Altair is well positioned to execute on the newly acquired technology and continue to grow its existing business. About Altair Altair is a global leader in computational science and artificial intelligence (AI) that provides software and cloud solutions in simulation, high-performance computing (HPC), data analytics, and AI. Altair enables organizations across all industries to compete more effectively and drive smarter decisions in an increasingly connected world – all while creating a greener, more sustainable future.

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AI TECH,GENERAL AI,SOFTWARE

CloudFactory Acquires Hasty, a Leading Platform to Accelerate Vision AI

CloudFactory | September 12, 2022

CloudFactory, a global leader in human-in-the-loop artificial intelligence (AI), today announced that it has completed the acquisition of Hasty, a data-centric machine learning (ML) platform that allows companies to build and deploy vision AI applications faster and more reliably. Combined with CloudFactory’s best-in-class workforce, the acquisition will provide clients with all the essential components needed to accelerate the development of high-performing models. “We're seeing our most successful clients shift from manual data labeling to AI-assisted labeling, and from a model-centric to a data-centric approach. “This shift requires best-in-class labeling automation and tools to work on the model and the data in tandem - enabling rapid feedback and fast iterations." Mark Sears, CEO of CloudFactory Hasty's platform provides users with natively built, AI-assisted automation capabilities that reduce manual data work and maximize workforce productivity. Hasty brings agile methodologies to vision AI by retraining models during labeling. This agility provides the insight needed for data scientists and engineers to focus on the data–enabling teams to optimize models iteratively throughout the entire development process. The acquisition of Hasty fits into CloudFactory's strategy to lead in more aspects of the AI development lifecycle. Integrating Hasty's AI-assisted labeling and data-centric platform with CloudFactory's human-in-the-loop AI solutions will bring ML models into production faster. About CloudFactory CloudFactory is a global leader in combining people and technology to support the AI development lifecycle. Their human-in-the-loop AI solutions, powered by more than 7,000 expertly trained and managed data analysts, are trusted by AI leaders at 700+ companies, including Microsoft, Mitsubishi, Ibotta, Expensify, and Matterport. Founded in 2010 and with offices on four continents, CloudFactory is on a mission to create economic and leadership opportunities for talented people in developing nations.

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AI TECH,GENERAL AI,MACHINE LEARNING

SpringML Renews its Machine Learning Specialization in the Google Cloud Partner Advantage Program

SpringML | September 07, 2022

SpringML, Inc. , a leader in machine learning and advanced data analytics services, today announced that it has achieved the renewed Machine Learning Partner Specialization in the Google Cloud Partner Advantage Program. By earning the Machine Learning Partner Specialization, SpringML has shown its expertise and success in building customer solutions in the Machine Learning field using Google Cloud technology. Partners achieving this specialization have demonstrated success with data exploration, preprocessing, model training, model evaluation, model deployment, online prediction, and Google pretrained Machine Learning APIs. This specialization will further enable customers to leverage Google Cloud AI and machine learning services to analyze data, use speech and image recognition applications, and more. "Artificial intelligence and machine learning is one of the core foundations of a successful digital transformation. By using the Google Cloud ML tools & technologies, SpringML has helped enterprises in their digital transformation journey. "This is our third renewal which showcases our maturity and strength in providing end-to-end AI/ML support and services. SpringML's domain expertise, along with Google Cloud's capabilities, is well positioned to help our joint customers in their AI journey." Prabhu Palanisamy, President and Chief Strategy Officer at SpringML "AI/ML technologies are driving digital transformation across industries, and having access to experts with competencies in these areas is important to continued innovation,' said Derrick Thompson, Global Head of Partner Differentiation, Google Cloud. "SpringML's renewed Machine Learning Specialization is proof of its dedication to helping customers transform their business with cloud technologies like ML." At SpringML, we have earned various Google Cloud specializations, from Data Analytics, Data Management, Application Development, and Security, demonstrating our commitment to the success of our customers. SpringML is a trusted partner for data-driven digital transformation projects with our agile, iterative, and incremental delivery model that helps enterprises build future-ready applications. About SpringML, Inc. SpringML delivers data-driven digital transformation outcomes with an experimentation and design thinking mindset. We provide Google Cloud consulting and implementation services and industry-specific analytics solutions that deliver high-impact business value from data. SpringML is a Google Cloud partner with capabilities to plan, assess, deploy, and manage data-driven engagements. We have received Google Cloud specialization based on our expertise and customer portfolio for Data Management, Application Development, Data Analytics, Machine Learning, Marketing Analytics, and Security.

Read More

AI TECH,GENERAL AI

Altair Announces Completion of Acquisition of RapidMiner

Altair | September 17, 2022

Altair , a global leader in computational science and artificial intelligence (AI), has completed the acquisition of RapidMiner, a leader in advanced data analytics and machine learning (ML) software. RapidMiner's well-established desktop platform and new-to-market cloud platform (multi-tenant and SaaS ready) strengthens Altair's current end-to-end data analytics (DA) portfolio, which already offers customers the power to understand, transform, act on, and automate their data. Altair is well positioned to execute on the newly acquired technology and continue to grow its existing business. About Altair Altair is a global leader in computational science and artificial intelligence (AI) that provides software and cloud solutions in simulation, high-performance computing (HPC), data analytics, and AI. Altair enables organizations across all industries to compete more effectively and drive smarter decisions in an increasingly connected world – all while creating a greener, more sustainable future.

Read More

AI TECH,GENERAL AI,SOFTWARE

CloudFactory Acquires Hasty, a Leading Platform to Accelerate Vision AI

CloudFactory | September 12, 2022

CloudFactory, a global leader in human-in-the-loop artificial intelligence (AI), today announced that it has completed the acquisition of Hasty, a data-centric machine learning (ML) platform that allows companies to build and deploy vision AI applications faster and more reliably. Combined with CloudFactory’s best-in-class workforce, the acquisition will provide clients with all the essential components needed to accelerate the development of high-performing models. “We're seeing our most successful clients shift from manual data labeling to AI-assisted labeling, and from a model-centric to a data-centric approach. “This shift requires best-in-class labeling automation and tools to work on the model and the data in tandem - enabling rapid feedback and fast iterations." Mark Sears, CEO of CloudFactory Hasty's platform provides users with natively built, AI-assisted automation capabilities that reduce manual data work and maximize workforce productivity. Hasty brings agile methodologies to vision AI by retraining models during labeling. This agility provides the insight needed for data scientists and engineers to focus on the data–enabling teams to optimize models iteratively throughout the entire development process. The acquisition of Hasty fits into CloudFactory's strategy to lead in more aspects of the AI development lifecycle. Integrating Hasty's AI-assisted labeling and data-centric platform with CloudFactory's human-in-the-loop AI solutions will bring ML models into production faster. About CloudFactory CloudFactory is a global leader in combining people and technology to support the AI development lifecycle. Their human-in-the-loop AI solutions, powered by more than 7,000 expertly trained and managed data analysts, are trusted by AI leaders at 700+ companies, including Microsoft, Mitsubishi, Ibotta, Expensify, and Matterport. Founded in 2010 and with offices on four continents, CloudFactory is on a mission to create economic and leadership opportunities for talented people in developing nations.

Read More

AI TECH,GENERAL AI,MACHINE LEARNING

SpringML Renews its Machine Learning Specialization in the Google Cloud Partner Advantage Program

SpringML | September 07, 2022

SpringML, Inc. , a leader in machine learning and advanced data analytics services, today announced that it has achieved the renewed Machine Learning Partner Specialization in the Google Cloud Partner Advantage Program. By earning the Machine Learning Partner Specialization, SpringML has shown its expertise and success in building customer solutions in the Machine Learning field using Google Cloud technology. Partners achieving this specialization have demonstrated success with data exploration, preprocessing, model training, model evaluation, model deployment, online prediction, and Google pretrained Machine Learning APIs. This specialization will further enable customers to leverage Google Cloud AI and machine learning services to analyze data, use speech and image recognition applications, and more. "Artificial intelligence and machine learning is one of the core foundations of a successful digital transformation. By using the Google Cloud ML tools & technologies, SpringML has helped enterprises in their digital transformation journey. "This is our third renewal which showcases our maturity and strength in providing end-to-end AI/ML support and services. SpringML's domain expertise, along with Google Cloud's capabilities, is well positioned to help our joint customers in their AI journey." Prabhu Palanisamy, President and Chief Strategy Officer at SpringML "AI/ML technologies are driving digital transformation across industries, and having access to experts with competencies in these areas is important to continued innovation,' said Derrick Thompson, Global Head of Partner Differentiation, Google Cloud. "SpringML's renewed Machine Learning Specialization is proof of its dedication to helping customers transform their business with cloud technologies like ML." At SpringML, we have earned various Google Cloud specializations, from Data Analytics, Data Management, Application Development, and Security, demonstrating our commitment to the success of our customers. SpringML is a trusted partner for data-driven digital transformation projects with our agile, iterative, and incremental delivery model that helps enterprises build future-ready applications. About SpringML, Inc. SpringML delivers data-driven digital transformation outcomes with an experimentation and design thinking mindset. We provide Google Cloud consulting and implementation services and industry-specific analytics solutions that deliver high-impact business value from data. SpringML is a Google Cloud partner with capabilities to plan, assess, deploy, and manage data-driven engagements. We have received Google Cloud specialization based on our expertise and customer portfolio for Data Management, Application Development, Data Analytics, Machine Learning, Marketing Analytics, and Security.

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