Data Mining Techniques and How Businesses Implement Them

Aashish Yadav | March 31, 2022 | 185 views

Data Mining Techniques and How

Businesses have more data than ever in contemporary times because of rapidly evolving technology. Data is one of the most valuable resources available to any business or entrepreneur. Businesses today are overflowing with data from a wide range of sources, including websites, mobile devices, social media, and other digital channels, but they don’t know where to start.


It doesn't matter if you have massive data; what matters is what you do with it. This is where data mining comes into play. Turning unstructured data into insights helps companies accomplish their goals and improvement strategy for the future. These days, data mining techniques are widely used by organizations from different industries.

“With data collection, ‘the sooner the better’ is always the best answer.”

- Marissa Mayer, Ex-CEO of Yahoo!

Let’s explore some of the top data mining techniques used by businesses.


What are the Must-Have Data Mining Techniques?

Data mining is a successful process that uses a variety of data mining approaches. The problem is determining which data mining techniques are most appropriate for your situation and business. Even though many data mining techniques are often required to uncover insights hidden inside massive datasets, it's wise to use more than one.


Data Cleaning

When it comes to data mining, data cleaning is a must-have approach. For raw data to be used for various sorts of analytical techniques, it must first be cleaned, formatted, and analyzed. This data mining technique is used in data modeling, transformation, aggregation, and migration processes.


Clustering Analysis

Clustering analysis is the recognition and clustering of related data from an big data collection. Clustering analysis can assist an organization in evaluating the differences and similarities in data from the perspective of a company. This can help them develop customer personas, similar segment leads, and others.


Association Rule Discovery

This is a technique for discovering interesting connections and interdependencies among variables in big datasets. This data mining technique can help decipher hidden data patterns that would otherwise go unnoticed. Association rule discovery a term that's frequently used in machine learning.


Classification Analysis

The technique of extracting information about the data is called classification analysis. It is the most complicated data mining technique. Data classification involves splitting data into categories that have similarities in their context. As a result, classification analysis is useful in combination with clustering analysis. The structure or recognition of the data is known as classification.


Data Visualization

Data visualization uses real-time graphs and charts to provide users with extra insights into their data and help them better grasp performance targets. Data visualization is a popular data mining technique because it can get data from any source, such as file uploads, database queries, and application connections.


Top Industries Using Data Mining

Data mining in business can help you manage risk by detecting fraud, errors, and discrepancies that can result in revenue loss and reputation damage. Data mining is used in various industries to gain a deeper understanding of their customers and businesses. Many brands in various industries perfectly portray how data mining is used in business.


Finance and Banking

Using data mining approaches, financial organizations gather information about loans and credit reports. Financial institutions can evaluate if a lender has a good or bad credit score by using an analysis model based on historical financial data. Banks can also keep a watch on suspicious or fraudulent transactions with the help of data mining techniques.


E-commerce

E-commerce platforms are among the most well-known examples of data mining and business analytics. Many e-commerce websites utilize data mining and business intelligence to provide cross-sells and up-sells. Amazon is, of course, one of the most popular users of data mining and business intelligence.


Retail

Customers are segmented into 'recency, frequency, and monetary' (RFM) categories by retailers, focusing on marketing to those segments. A consumer who spends little but frequently and recently will be treated differently from one who spends a lot just once, which was some time ago. Loyalty, up-sell, and cross-sell offers may be made to the frequent buyer, while the big spender may provide a win-back deal.


Top 3 Companies Leveraging Data Mining Techniques

Businesses use data mining to boost revenue, save expenses, locate consumers, improve customer experience, listen to what others have to say, and conduct competitive intelligence. These are just a few ways of using data mining techniques.
Here are the top three examples of data mining in business.


Amazon

Amazon is gathering competitive intelligence and pricing data from its competitors. Consumers who use the Amazon Price Check Mobile App to scan items in-store, capture an image of the product, or do a text search to find the best price will get a $5 discount. The application also encourages customers to report the in-store price.


Netflix

House of Cards – the American thriller series was an ideal entertainment experience for creating data models and discovering what makes a show or movie successful among viewers based on the insights data gathered. They went all out for the license, winning a bidding battle with rival businesses and instantly scheduling two seasons before presenting a single episode. It was a super hit, and the best thing is that they had a pretty good idea of what it would be.


Walmart

The current search engine of Walmart contains semantic data. Polaris, an in-house platform, uses text analysis, machine learning, and even synonym mining to provide relevant search results. Walmart says that integrating semantic search has led to a 10% to 15% increase in the number of people who buy things online.


Conclusion

Data mining's ultimate significance for data-driven growth and progress cannot be overstated. Using the right data mining technique will give you unparalleled insight into your massive data. Data mining will only get better as technology improves, which will allow for more in-depth analysis.


FAQ


What are the 3 types of data mining?

Pictorial data mining, text mining, social media mining, online mining, and audio and video mining are only a few examples of data mining.


What are the 6 processes of data mining?

Data mining is both an analytical process and a collection of algorithms and models. The CRISP-DM process model has been broken down, just like the CIA Intelligence Process. These parts are business understanding, data understanding, data preparation, modeling, evaluation, and deployment.


What are the most commonly used data mining processes?

Clustering, data cleansing, association, data warehousing, machine learning, data visualization, classification, neural networks, and prediction are just a few of the important data mining techniques to consider when starting out in the industry.

Spotlight

Data Analytics Labs

At Data Analytics Labs we specialse in data. Structured, unstructured and of a variety of types. From genomic, to image, to sentiment, to financial, we specialise in applying the latest techniques to data analysis. “Big Data” is data whose scale, distribution, diversity, and/or timeliness require the use of new technical architectures and analytics to enable insights that unlock new sources of business value. Organisations worldwide are deriving business benefit from analysing ever larger and more complex datasets that increasingly require real-time or near real-time capabilities. “Big Data” is data whose scale, distribution, diversity, and/or timeliness require the use of new technical architectures and analytics to enable insights that unlock new sources of business value. Organisations worldwide are deriving business benefit from analysing ever larger and more complex datasets that increasingly require real-time or near real-time capabilities. Head quartered in Belfast, N.Ireland,

OTHER ARTICLES
DATA SCIENCE

How is Data Virtualization Shifting the Tailwind in Data Management?

Article | February 16, 2022

Over the past couple of years, a significant rise in the trend of digitalization has been witnessed across almost all industries, resulting in the creation of large volumes of data. In addition, an unprecedented proliferation of applications and the rise in the use of social media, cloud and mobile computing, the Internet of Things, and others have created the need for collecting, combining, and curating massive amounts of data. As the importance of data continues to grow across businesses, companies aim to collect data from the web, social media, AI-powered devices, and other sources in different formats, making it trickier for them to manage this unstructured data. Hence, smarter companies are investing in innovative solutions, such as data virtualization, to access and modify data stored across siloed, disparate systems through a unified view. This helps them bridge critical decision-making data together, fuel analytics, and make strategic and well-informed decisions. Why is Data Virtualization Emerging as A New Frontier in Data Management? In the current competitive corporate world, where data needs are increasing at the same rate as the volume of data companies hold, it is becoming essential to manage and harness data effectively. As enterprises focus on accumulating multiple types of data, the effort of managing it has outgrown the capacity of traditional data integration tools, such as data warehouse software and Extract Transform Load (ETL) systems. With the growing need for more effective data integration solutions, high-speed information sharing, and non-stop data transmission, advanced tools such as data virtualization are gaining massive popularity among corporate firms and other IT infrastructures. Data virtualization empowers organizations to accumulate and integrate data from multiple channels, locations, sources, and formats to create a unified stream of data without any redundancy or overlap, resulting in faster integration speeds and enhanced decision-making. What are the key features that make data virtualization a new frontier in data management? Let's see: Modernize Information Infrastructure With the ability to hide the underlying systems, data virtualization allows companies to replace their old infrastructure with cutting-edge cloud applications without affecting day-to-day business operations. Enhance Data Protection Data virtualization enables CxOs to identify and isolate vital source systems from users and applications, which assists organizations in preventing the latter from making unintended changes to the data, as well as allowing them to enforce data governance and security. Deliver Information Faster and Cheaper Data replication takes time and costs money; the "zero replication" method used by data virtualization allows businesses to obtain up-to-the-minute information without having to invest in additional storage space, thereby saving on the operation cost. Increase Business Productivity By delivering data in real time, the integration of data virtualization empowers businesses to access the most recent data during regular business operations. In addition, it enhances the utilization of servers and storage resources and allows data engineering teams to do more in less time, thereby increasing productivity. Use Fewer Development Resources Data virtualization lowers the need for human coding, allowing developers to focus on the faster delivery of information at scale. With its simplified view-based methodology, data virtualization also enables CxOs to reduce development resources by around one-fourth. Data Virtualization: The Future Ahead With the growing significance of data across enterprises and increasing data volume, variety, complexity, compliance requirements, and others, every organization is looking for well-governed, consistent, and secure data that is easy to access and use. As data virtualization unifies and integrates the data from different systems, providing new ways to access, manage, and deliver data without replicating it, more and more organizations are investing in data virtualization software and solutions and driving greater business value from their data.

Read More
DATA SCIENCE

How Artificial Intelligence Is Transforming Businesses

Article | January 12, 2022

Whilst there are many people that associate AI with sci-fi novels and films, its reputation as an antagonist to fictional dystopic worlds is now becoming a thing of the past, as the technology becomes more and more integrated into our everyday lives.AI technologies have become increasingly more present in our daily lives, not just with Alexa’s in the home, but also throughout businesses everywhere, disrupting a variety of different industries with often tremendous results. The technology has helped to streamline even the most mundane of tasks whilst having a breath-taking impact on a company’s efficiency and productivity.However, AI has not only transformed administrative processes and freed up more time for companies, it has also contributed to some ground-breaking moments in business, being a must-have for many in order to keep up with the competition.

Read More
DATA SCIENCE

DRIVING DIGITAL TRANSFORMATION WITH RPA, ML AND WORKFLOW AUTOMATION

Article | March 31, 2022

The latest pace of advancements in technology paves way for businesses to pay attention to digital strategy in order to drive effective digital transformation. Digital strategy focuses on leveraging technology to enhance business performance, specifying the direction where organizations can create new competitive advantages with it. Despite a lot of buzz around its advancement, digital transformation initiatives in most businesses are still in its infancy.Organizations that have successfully implemented and are effectively navigating their way towards digital transformation have seen that deploying a low-code workflow automation platform makes them more efficient.

Read More

AI and Predictive Analytics: Myth, Math, or Magic

Article | February 10, 2020

We are a species invested in predicting the future as if our lives depended on it. Indeed, good predictions of where wolves might lurk were once a matter of survival. Even as civilization made us physically safer, prediction has remained a mainstay of culture, from the haruspices of ancient Rome inspecting animal entrails to business analysts dissecting a wealth of transactions to foretell future sales. With these caveats in mind, I predict that in 2020 (and the decade ahead) we will struggle if we unquestioningly adopt artificial intelligence (AI) in predictive analytics, founded on an unjustified overconfidence in the almost mythical power of AI's mathematical foundations. This is another form of the disease of technochauvinism I discussed in a previous article.

Read More

Spotlight

Data Analytics Labs

At Data Analytics Labs we specialse in data. Structured, unstructured and of a variety of types. From genomic, to image, to sentiment, to financial, we specialise in applying the latest techniques to data analysis. “Big Data” is data whose scale, distribution, diversity, and/or timeliness require the use of new technical architectures and analytics to enable insights that unlock new sources of business value. Organisations worldwide are deriving business benefit from analysing ever larger and more complex datasets that increasingly require real-time or near real-time capabilities. “Big Data” is data whose scale, distribution, diversity, and/or timeliness require the use of new technical architectures and analytics to enable insights that unlock new sources of business value. Organisations worldwide are deriving business benefit from analysing ever larger and more complex datasets that increasingly require real-time or near real-time capabilities. Head quartered in Belfast, N.Ireland,

Related News

BUSINESS INTELLIGENCE, BIG DATA MANAGEMENT

Veritonic Added to List of Acast’s Preferred Audio Attribution Partners

Veritonic | December 09, 2022

Veritonic, the industry’s comprehensive audio analytics and research platform, announced today that they have been approved as an attribution partner by Acast, the world’s largest independent podcast company. As a result, the more than 2,400 advertisers and 88,000 podcasters that use the Acast platform to distribute their podcast content can elect to utilize Veritonic’s robust attribution capabilities to optimize and further increase the ROI of their audio campaigns. “We are pleased to partner with Acast to support brands, agencies, and publishers with the holistic data and analytics they need to increase their reach and ROI with audio. "The powerful combination of our attribution and brand lift technology provides unparalleled and comprehensive measurement of audio campaigns from top to bottom in one unified and intuitive platform.” Scott Simonelli, chief executive officer of Veritonic "Veritonic shares our commitment to arming brands and agencies with actionable and insightful audio performance data,” said Kevin McCaul, Global Head of Ad Operations at Acast. “Our partnership is an important step for the open ecosystem of podcasting as we continue to work together to provide independent measurement insights to prove the effectiveness and efficiency of podcasting as a marketing channel.” Veritonic’s Attribution solution enables users to glean actionable insights from top-of-the-funnel branding initiatives through bottom-of-the-funnel conversions & transactions. Through an intuitive and interactive dashboard, brands can determine which publisher and specific ads had the highest impact and use that data to optimize ad performance. About Veritonic World-renowned brands, agencies, publishers, and platforms rely on Veritonic’s comprehensive audio research and analytics platform to research, test, and measure the ROI of their audio assets and campaigns pre-market, in-market, and post-campaign. The resulting insight enables clients to gain confidence in their audio investment, mitigate risk through optimization, and increase their return as they engage consumers with compelling audio experiences. About Acast Acast is the world’s largest independent podcast company. Founded in 2014, the company has pioneered the open podcast ecosystem ever since – making podcasts available on any listening platform. Acast provides a marketplace, helping podcasters find the right audience to monetize their content. When our podcasters make money, we make money. Today, Acast hosts nearly 88,000 podcasts, with more than 430 million listens every month.

Read More

BUSINESS INTELLIGENCE, BIG DATA MANAGEMENT, DATA SCIENCE

Aporia & ClearML Launch New Full-Stack MLOps Platform Partnership

Aporia | November 07, 2022

Aporia, the customizable ML observability platform, and ClearML, the only unified end-to-end MLOPs platform to develop, orchestrate, and automate ML workflows at scale, announced an end-to-end solution today to help data scientists, ML engineers and DevOps teams perfect their ML pipelines. Through this new partnership, Data scientists and DevOps teams will be able to use the combined power of ClearML and Aporia to significantly shorten their time-to-value and time-to-revenue by ensuring ML projects are executed successfully and make it to commercial production more efficiently – from building to deployment and monitoring. As data scientists and ML engineers work to create applications that drive value, ML teams must jump through numerous hoops – from data experimentation and tracking to data operations and orchestrating – until a model is ready for production. With so many moving parts involved in operationalizing ML at scale, too much time is invested in learning how to use and integrate the required point solution tool for each stage of the process. Data science, ML engineers and DevOps teams need a frictionless one-stop-shop, end-to-end integrated solution that optimizes their entire workflow from training to production throughout the entire ML value chain. "As an open source company dedicated to giving the data science, ML engineering, and DevOps communities the tools they need to do more with their machine and deep learning projects, we're excited to integrate with Aporia to add cloud-native ML observability to our unified, end-to-end MLOPs platform. "The result of this joint effort means that our customers can do even more from the very start without the friction – using ClearML to build, train, orchestrate, and serve their models seamlessly and with just two lines of code and Aporia to monitor, explain, and improve those models once they hit production. Moses Guttman, CEO and Co-founder of ClearML ClearML is an open-source MLOps platform that automates and simplifies developing and managing machine learning solutions for data science, ML engineers and DevOps teams at scale. Designed as a frictionless, unified end-to-end MLOps suite, it brings the CI/CD automation approach into ML development & production allowing customers to focus on developing their ML code and pipelines, while also ensuring their work is automated, reproducible, and scalable. With ClearML for Enterprise, customers significantly shorten their time-to-value and time-to-revenue, ensuring operationalizing ML at scale is executed successfully and make it to production efficiently. In a category dominated by fragmented point solutions and walled garden closed semi-platforms, ClearML delivers an open-sourced, comprehensive offering that enables companies to scale their MLOps while successfully bridging the innovation and revenue gaps with the company's unified end-to-end platform. Once a model is deployed into production by ClearML, Aporia's customizable ML observability solution seamlessly empowers data science and ML teams to trust their AI, enabling them to monitor, explain, investigate and solve issues like data & concept drift, performance degradation and model decay. Aporia does this with customizable model monitoring to trigger live alerts when a model is spiraling, a dashboard that provides visibility of all models under a single pane of glass, and an 'Investigate and Explain' capability that gets to the root cause of any issue delivering explainable AI that gives human-readablemeaning to model predictions for business stakeholders. "As an MLOps leader, ensuring data science and ML teams can trust their ML model predictions, we see immense value for our customers in integrating with ClearML's open source MLOps platform to provide a true end-to-end solution from training to production and beyond," said Liran Hason, CEO and Co-Founder of Aporia. "There are so many different tools and moving parts to pull from when setting off on this hero's path to build, train, serve, monitor, and explain machine learning models. We're excited to team up with ClearML and provide a one-stop-shop MLOps platform to scale ML with confidence." About ClearML Trusted by forward-thinking Data Scientists, ML Engineers, DevOps, and decision-makers at leading Fortune 1000, enterprises, and innovative start-ups worldwide, ClearML is an open source, MLOps platform that helps data science, MLOps, and DevOps teams easily develop, orchestrate, and automate ML workflows at scale. It is designed as a frictionless, unified, end-to-end MLOps suite allowing users and customers to focus on developing their ML code and automation, ensuring their work is reproducible and scalable. To learn more, contact us at info@clear.ml. About Aporia Aporia is a self-serve customizable monitoring platform for machine learning, used by Fortune 500 companies and data science teams around the world to monitor billions of daily predictions and maintain AI responsibility and fairness. Founded in 2019, Aporia is backed by TLV Partners Samsung Next, Tiger Global and Vertex Ventures.

Read More

BUSINESS INTELLIGENCE, BIG DATA MANAGEMENT, DATA SCIENCE

Quantiphi announces partnership with Databricks to help drive enterprise-wide AI adoption

Quantiphi | November 15, 2022

Quantiphi, an AI-first digital engineering company, today announced its partnership with Databricks, the lakehouse company and pioneer of this new data paradigm. Together, Quantiphi and Databricks will focus on helping enterprise customers to optimize their business workflows with AI which will be enabled by a strong foundation of the Lakehouse platform. Businesses today are embracing multi-cloud and hybrid cloud environments. As a result, it has become imperative to modernize their data foundation for seamless operations across different environments, and deploy advanced cloud-based technologies – including analytics tools with advanced machine learning (ML) and MLOps capabilities. As Databricks Consulting Partners, Quantiphi will accelerate AI-driven innovation for clients across industries. Quantiphi's in-depth knowledge and deep expertise in helping enterprise customers modernize and democratize their Data and AI footprint at scale are valuable for the partnership with Databricks. Databricks' Lakehouse Platform is cloud-agnostic and enables users to unify their data warehousing and AI use cases on a consistent platform across multiple infrastructures simultaneously. "We are delighted to enter into a strategic partnership with Databricks. "By combining the power of Databricks' Lakehouse Platform and Quantiphi's advanced AI/ML capabilities, our teams will empower customers to leverage data-driven MLOps and enable enterprise AI success." Asif Hasan, Co-founder, Quantiphi The collaboration is set to actively support customers with various data and AI services such as digital transformation strategy, AI innovation roadmap, MLOps, data modernization and migration, data management, security, and governance implementations. Quantiphi's team of dedicated applied AI Databricks experts will further help customers implement and scale data engineering, collaborative data science, full-lifecycle machine learning, and business analytics initiatives. "Today, there is a rising need for every business to have a strong foundation of data and AI. By combining the strength of Databricks' Lakehouse Platform in data engineering, data science and analytics and Quantiphi's AI-first digital engineering capabilities, we can help companies transform their businesses through the power of data", said Mohak Moondra, Practice Leader - Applied AI, Quantiphi. About Quantiphi Quantiphi is an award-winning AI-first digital engineering company driven by the desire to reimagine and realize transformational opportunities at the heart of the business. Quantiphi solves the toughest and most complex business problems by combining deep industry experience, disciplined cloud, and data-engineering practices, and cutting-edge artificial intelligence research to achieve quantifiable business impact at unprecedented speed. We are passionate about our customers and obsessed with problem-solving to make products smarter, customer experiences frictionless, processes autonomous and businesses safer by detecting risks, threats, and anomalies. Together with partners and customers, we embark on a data and AI-led transformation journey that delivers impactful and measurable results.

Read More

BUSINESS INTELLIGENCE, BIG DATA MANAGEMENT

Veritonic Added to List of Acast’s Preferred Audio Attribution Partners

Veritonic | December 09, 2022

Veritonic, the industry’s comprehensive audio analytics and research platform, announced today that they have been approved as an attribution partner by Acast, the world’s largest independent podcast company. As a result, the more than 2,400 advertisers and 88,000 podcasters that use the Acast platform to distribute their podcast content can elect to utilize Veritonic’s robust attribution capabilities to optimize and further increase the ROI of their audio campaigns. “We are pleased to partner with Acast to support brands, agencies, and publishers with the holistic data and analytics they need to increase their reach and ROI with audio. "The powerful combination of our attribution and brand lift technology provides unparalleled and comprehensive measurement of audio campaigns from top to bottom in one unified and intuitive platform.” Scott Simonelli, chief executive officer of Veritonic "Veritonic shares our commitment to arming brands and agencies with actionable and insightful audio performance data,” said Kevin McCaul, Global Head of Ad Operations at Acast. “Our partnership is an important step for the open ecosystem of podcasting as we continue to work together to provide independent measurement insights to prove the effectiveness and efficiency of podcasting as a marketing channel.” Veritonic’s Attribution solution enables users to glean actionable insights from top-of-the-funnel branding initiatives through bottom-of-the-funnel conversions & transactions. Through an intuitive and interactive dashboard, brands can determine which publisher and specific ads had the highest impact and use that data to optimize ad performance. About Veritonic World-renowned brands, agencies, publishers, and platforms rely on Veritonic’s comprehensive audio research and analytics platform to research, test, and measure the ROI of their audio assets and campaigns pre-market, in-market, and post-campaign. The resulting insight enables clients to gain confidence in their audio investment, mitigate risk through optimization, and increase their return as they engage consumers with compelling audio experiences. About Acast Acast is the world’s largest independent podcast company. Founded in 2014, the company has pioneered the open podcast ecosystem ever since – making podcasts available on any listening platform. Acast provides a marketplace, helping podcasters find the right audience to monetize their content. When our podcasters make money, we make money. Today, Acast hosts nearly 88,000 podcasts, with more than 430 million listens every month.

Read More

BUSINESS INTELLIGENCE, BIG DATA MANAGEMENT, DATA SCIENCE

Aporia & ClearML Launch New Full-Stack MLOps Platform Partnership

Aporia | November 07, 2022

Aporia, the customizable ML observability platform, and ClearML, the only unified end-to-end MLOPs platform to develop, orchestrate, and automate ML workflows at scale, announced an end-to-end solution today to help data scientists, ML engineers and DevOps teams perfect their ML pipelines. Through this new partnership, Data scientists and DevOps teams will be able to use the combined power of ClearML and Aporia to significantly shorten their time-to-value and time-to-revenue by ensuring ML projects are executed successfully and make it to commercial production more efficiently – from building to deployment and monitoring. As data scientists and ML engineers work to create applications that drive value, ML teams must jump through numerous hoops – from data experimentation and tracking to data operations and orchestrating – until a model is ready for production. With so many moving parts involved in operationalizing ML at scale, too much time is invested in learning how to use and integrate the required point solution tool for each stage of the process. Data science, ML engineers and DevOps teams need a frictionless one-stop-shop, end-to-end integrated solution that optimizes their entire workflow from training to production throughout the entire ML value chain. "As an open source company dedicated to giving the data science, ML engineering, and DevOps communities the tools they need to do more with their machine and deep learning projects, we're excited to integrate with Aporia to add cloud-native ML observability to our unified, end-to-end MLOPs platform. "The result of this joint effort means that our customers can do even more from the very start without the friction – using ClearML to build, train, orchestrate, and serve their models seamlessly and with just two lines of code and Aporia to monitor, explain, and improve those models once they hit production. Moses Guttman, CEO and Co-founder of ClearML ClearML is an open-source MLOps platform that automates and simplifies developing and managing machine learning solutions for data science, ML engineers and DevOps teams at scale. Designed as a frictionless, unified end-to-end MLOps suite, it brings the CI/CD automation approach into ML development & production allowing customers to focus on developing their ML code and pipelines, while also ensuring their work is automated, reproducible, and scalable. With ClearML for Enterprise, customers significantly shorten their time-to-value and time-to-revenue, ensuring operationalizing ML at scale is executed successfully and make it to production efficiently. In a category dominated by fragmented point solutions and walled garden closed semi-platforms, ClearML delivers an open-sourced, comprehensive offering that enables companies to scale their MLOps while successfully bridging the innovation and revenue gaps with the company's unified end-to-end platform. Once a model is deployed into production by ClearML, Aporia's customizable ML observability solution seamlessly empowers data science and ML teams to trust their AI, enabling them to monitor, explain, investigate and solve issues like data & concept drift, performance degradation and model decay. Aporia does this with customizable model monitoring to trigger live alerts when a model is spiraling, a dashboard that provides visibility of all models under a single pane of glass, and an 'Investigate and Explain' capability that gets to the root cause of any issue delivering explainable AI that gives human-readablemeaning to model predictions for business stakeholders. "As an MLOps leader, ensuring data science and ML teams can trust their ML model predictions, we see immense value for our customers in integrating with ClearML's open source MLOps platform to provide a true end-to-end solution from training to production and beyond," said Liran Hason, CEO and Co-Founder of Aporia. "There are so many different tools and moving parts to pull from when setting off on this hero's path to build, train, serve, monitor, and explain machine learning models. We're excited to team up with ClearML and provide a one-stop-shop MLOps platform to scale ML with confidence." About ClearML Trusted by forward-thinking Data Scientists, ML Engineers, DevOps, and decision-makers at leading Fortune 1000, enterprises, and innovative start-ups worldwide, ClearML is an open source, MLOps platform that helps data science, MLOps, and DevOps teams easily develop, orchestrate, and automate ML workflows at scale. It is designed as a frictionless, unified, end-to-end MLOps suite allowing users and customers to focus on developing their ML code and automation, ensuring their work is reproducible and scalable. To learn more, contact us at info@clear.ml. About Aporia Aporia is a self-serve customizable monitoring platform for machine learning, used by Fortune 500 companies and data science teams around the world to monitor billions of daily predictions and maintain AI responsibility and fairness. Founded in 2019, Aporia is backed by TLV Partners Samsung Next, Tiger Global and Vertex Ventures.

Read More

BUSINESS INTELLIGENCE, BIG DATA MANAGEMENT, DATA SCIENCE

Quantiphi announces partnership with Databricks to help drive enterprise-wide AI adoption

Quantiphi | November 15, 2022

Quantiphi, an AI-first digital engineering company, today announced its partnership with Databricks, the lakehouse company and pioneer of this new data paradigm. Together, Quantiphi and Databricks will focus on helping enterprise customers to optimize their business workflows with AI which will be enabled by a strong foundation of the Lakehouse platform. Businesses today are embracing multi-cloud and hybrid cloud environments. As a result, it has become imperative to modernize their data foundation for seamless operations across different environments, and deploy advanced cloud-based technologies – including analytics tools with advanced machine learning (ML) and MLOps capabilities. As Databricks Consulting Partners, Quantiphi will accelerate AI-driven innovation for clients across industries. Quantiphi's in-depth knowledge and deep expertise in helping enterprise customers modernize and democratize their Data and AI footprint at scale are valuable for the partnership with Databricks. Databricks' Lakehouse Platform is cloud-agnostic and enables users to unify their data warehousing and AI use cases on a consistent platform across multiple infrastructures simultaneously. "We are delighted to enter into a strategic partnership with Databricks. "By combining the power of Databricks' Lakehouse Platform and Quantiphi's advanced AI/ML capabilities, our teams will empower customers to leverage data-driven MLOps and enable enterprise AI success." Asif Hasan, Co-founder, Quantiphi The collaboration is set to actively support customers with various data and AI services such as digital transformation strategy, AI innovation roadmap, MLOps, data modernization and migration, data management, security, and governance implementations. Quantiphi's team of dedicated applied AI Databricks experts will further help customers implement and scale data engineering, collaborative data science, full-lifecycle machine learning, and business analytics initiatives. "Today, there is a rising need for every business to have a strong foundation of data and AI. By combining the strength of Databricks' Lakehouse Platform in data engineering, data science and analytics and Quantiphi's AI-first digital engineering capabilities, we can help companies transform their businesses through the power of data", said Mohak Moondra, Practice Leader - Applied AI, Quantiphi. About Quantiphi Quantiphi is an award-winning AI-first digital engineering company driven by the desire to reimagine and realize transformational opportunities at the heart of the business. Quantiphi solves the toughest and most complex business problems by combining deep industry experience, disciplined cloud, and data-engineering practices, and cutting-edge artificial intelligence research to achieve quantifiable business impact at unprecedented speed. We are passionate about our customers and obsessed with problem-solving to make products smarter, customer experiences frictionless, processes autonomous and businesses safer by detecting risks, threats, and anomalies. Together with partners and customers, we embark on a data and AI-led transformation journey that delivers impactful and measurable results.

Read More

Events