5 Predictive Data Analytics Applications

SHAIVI CHAPALGAONKAR | May 31, 2021 | 1197 views

According to Google trends, predictive data analytics has gained a significant amount of popularity over the last few years. Many businesses have implemented predictive analytics applications to increase their business reach, gain new customers, forecast sales, and more.

Predictive Analytics is a type of data analytics technology that makes predictions with the help of data sets, statistical modeling, and machine learning. Predictive analytics uses historical data. This historical data is fed into a mathematical model that recognizes patterns and trends that are then applied to current data to forecast trends, practices, and behaviors from milliseconds to days and even years.

Based on the parameters supplied to them, organizations find patterns within that data to detect risks, opportunities, forecast conditions, and events that would occur at a particular time. At its heart, the use of predictive analytics answers a simple question, “What would happen based on my current data and what can be done to change the outcome.”

In the current times, businesses have multiple products offerings at their disposal to choose from vendors of big data predictive analytics in different industries. They can help these businesses leverage historical data discovering complex data correlation, recognizing patterns, and forecasting.

Organizations are turning to predictive analytics to increase their bottom line and gain advantages against their competition. Some of those reasons are listed below:

• With the growing amount and types of data, there is more interest in utilizing it to produce valuable insights
• Better computers
• An abundance of easy to use software
• Need of competitive differentiation due to tougher
  economic conditions

As more and more easy-to-use software have been introduced, businesses no longer need statisticians and mathematicians for predictive analytics and forecasting.

Benefits of Predictive Analytics

Competitive edge over other businesses

The most common reason why multiple companies picked up predictive analytics was to gain an advantage over their competitors. Customer trends and buying patterns keep changing from time to time. The ones who can identify it first will go ahead in the game. Embracing predictive analytics is how you will stay ahead of your competition. Predictive analytics will aid in qualified lead generation and give you an insight into the present and potential customers.

Business growth

Businesses opt for predictive analytics to predict customer behavior, preferences, and responses. Using this information, they attract their target audience and entice them into becoming loyal customers. Predictive analytics gives valuable information about your customers such as which of them are likely to lapse, how to retain them, whether you should market directly at them, etc. The more you know about them, the stronger your marketing will become. Your business will become the leader in predicting your customer’s exact needs.

Customer satisfaction

Retaining existing customers is almost five times more difficult than acquiring new ones. The most successful company is the one that invests money in retaining those customers as much as acquiring new ones.

Predictive analytics helps in directing marketing strategies towards your existing customers and get them to return frequently. The analytics tool will make sure your marketing strategy caters to the diverse requirements of your customers.

Personalized services

Earlier marketing strategies revolved around the ‘one size fits all’ approach, but gone are those days. If you want to retain and acquire new customers, you have to create personalized marketing campaigns to attract customers.

Predictive analytics and data management help you to get new information about customer expectations, previous purchases, buying behaviors, and patterns. Using this data, you can create these personalized marketing strategies that will help keep up the engagement and acquire new customers.

Application of Predictive Analytics

Customer targeting

Customer targeting divides the customer base into different demographic groups according to age, gender, interests, buying, and spending habits. It helps companies to create tailored marketing communications specifically to the customers who are likely to buy their products. Traditional techniques do not even come close to identifying potential customers as well as predictive analytics does.

The major constituents that create these customer groups are:

• Socio-demographic factors: age, gender, education, and marital status
• Engagement factors: recent interaction, frequency, spending habits, etc.
• Past campaign response: contact response, type, day, month, etc.

The customer-specific targeting for the company is highly advantageous. They can:

• Better communicate with the customers
• Save money on marketing
• Increase profits


Customer churn prevention

Customer churn prevention creates major hurdles in a company’s growth. Although it has been proven that retaining customers is cheaper than gaining new ones, it can become a problem. Detecting a client’s dissatisfaction is not an easy task as they can abruptly stop using your services without any warning.
Here, churn prevention comes into the picture. Churn prevention aims to predict who will end their relationship with the company, when, and why. The existing data sets can help develop predictive models so companies can be proactive to prevent the fallout.

Factors that can influence the churn are as follows:

• Customer variables
• Service use
• Engagement
• Technicalities
• Competitor variables

Using these variables, companies can then take necessary steps to avoid the churn by offering customers personalized services or products.

Risk management

Risk assessment and management processes in many companies are antiquated. Even though customer information is abundantly available for evaluation, it is still antiquated.

With advanced analytics, this data can be quickly and accurately analyzed while maintaining customer privacy and boundaries. Risk assessment thus allows companies to analyze problems with any business. Predictive analytics can approximate with certainty which operations are profitable and which are not.

Risk assessment analyzes the following data types:

• Socio-demographic factors
• Product details
• Customer behavior
• Risk metrics


Forecast sales

Evaluating the previous history, seasonality, and market-affecting events make revenue predicting vital for a company’s planning and result in a company’s demand for a product or a service. This can be applied to short-term, medium-term, and long-term forecasting.

Predictive models help in anticipating a customer’s reaction to the factors that affect sales.

Following factors can be used in sales forecasting:

• Calendar data
• Weather data
• Company data
• Social data
• Demand data

Sales forecasting allows revenue prediction and optimal resource allocation.


Healthcare

Healthcare organizations have begun to use predictive analytics as this technology is helping them save money. They are using predictive analytics in several different ways. With the help of this technology, based on past trends they can now allocate facility resources, optimize staff schedules, identify patients at risk, adding intelligence to pharmaceutical and supply acquisition management.

Using predictive analytics in the health domain has also helped in preventing cases and risks of developing health complications like diabetes, asthma, and other life-threatening problems. The application of predictive analytics in health care can lead to making better clinical decisions for patients.

Predictive analytics is being used across different industries and is good way to advance your company’s growth and forecast future events to act accordingly. It has gained support from many different organizations at a global scale and will continue to grow rapidly.


Frequently Asked Questions

What is predictive analytics?

Predictive analytics uses historical data to predict future events. The historical data is used to build mathematical model that captures essential trends. That predictive model is based on current data that predicts what will happen next or suggest steps to take for optimal outcomes.


How to do predictive analytics?

• Define business objectives
• Collect relevant data available from resources
• Improve on collected data by data cleaning methods
• Choose a model or build your own to test data
• Evaluate and validate the predictive model to ensure


How does predictive analytics work for business?

Predictive analytics helps businesses attract, retain, and grow their profitable customers. It also helps them in improving their operations.


What tools are used for predictive analytics?

Some tools used for predictive analytics are:
• SAS Advanced Analytics
• Oracle DataScience
• IBM SPSS Statistics
• SAP Predictive Analytics
• Q Research

Spotlight

Paragyte Technologies

Paragyte is a global IT consulting company operating since 2007, which delivers Technology Solutions to Business Driven Challenges. Having worked with small to large organizations, we are capable of developing innovative solutions in a wide range of technology domain enabling our customers to achieve their business goals. Our flexible, cost-effective services and innovative software solutions can help customers to increase efficiency, reduce costs and maximize their return on investment.

OTHER ARTICLES
BUSINESS INTELLIGENCE, BIG DATA MANAGEMENT, DATA VISUALIZATION

How is Data Virtualization Shifting the Tailwind in Data Management?

Article | November 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
BIG DATA MANAGEMENT

How Artificial Intelligence Is Transforming Businesses

Article | July 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
BUSINESS INTELLIGENCE

DRIVING DIGITAL TRANSFORMATION WITH RPA, ML AND WORKFLOW AUTOMATION

Article | August 4, 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

Paragyte Technologies

Paragyte is a global IT consulting company operating since 2007, which delivers Technology Solutions to Business Driven Challenges. Having worked with small to large organizations, we are capable of developing innovative solutions in a wide range of technology domain enabling our customers to achieve their business goals. Our flexible, cost-effective services and innovative software solutions can help customers to increase efficiency, reduce costs and maximize their return on investment.

Related News

BUSINESS INTELLIGENCE, BIG DATA MANAGEMENT, DATA SCIENCE

Pico Announces Major VoIP Analytics Product Upgrade

Pico | January 27, 2023

Pico is one of the global leaders in providing mission-critical technology services, software, data and analytics to the financial markets. While its Corvil Analytics is best known for ensuring the performance of low-latency electronic trading systems, Corvil VoIP Analytics is used to oversee the operations and performance of some of the world's biggest and most complex VoIP installations. To make sure that VoIP is reliable and of good quality, the Corvil method of evaluating network and application activity at the same time is essential. Chief Product Officer at Pico, Stacie Swanstrom, said, “With 20 years of experience in deriving operational intelligence from demanding and dynamic networks, Corvil Analytics has established a critical role in high-performance trading environments.” She added, “Corvil VoIP Analytics takes the ability to capture and decode lossless granularly timestamped data at 100GB and applies it to VoIP applications, providing the same rich data clients have come to expect in both on-prem and cloud deployments.” (Source – GlobeNewswire) One significant advantage of Corvil's wire data technique is that it offers a vendor-agnostic method of managing VoIP quality. Large enterprises often rely on several suppliers, and depending on vendor-bundled diagnostic tools provides only a partial and fragmented picture. In addition, 60% of current Corvil VoIP consumers have switched suppliers in the last three years. Corvil was important to the success of those initiatives, and it also provided a complete picture before, during, and after the migration. The Corvil solution's value is centered on quick and easy access to rich analytics: A unified interface for summary VoIP performance dashboards highlighting overall KPIs and failure rates When troubleshooting difficulties with providers and suppliers, complete packet capture for each call gives a clear record Drill-down into individual calls with a single click and quick access to both signaling messages and the voice call packet stream Splunk, Orion, Nagios, and other third-party operations management systems benefit from extensive open APIs and live streaming of key metrics About Pico Pico is one of the leaders in providing technological services to the financial market sector. Its technology and services are used for mission-critical power systems for worldwide banks, exchanges, electronic trading businesses, quantitative hedge funds, and financial technology service providers. The firm offers a best-in-class array of market solutions that are creative, transparent, and low-latency, as well as an agile and expert service delivery methodology. PicoNet™, a globally comprehensive network infrastructure instrumented natively with Corvil to provide analytics and telemetry, offers instant access to financial markets. Pico is chosen by clients that desire the ability to react quickly and gain an operational advantage in the fast-paced world of financial markets.

Read More

BIG DATA MANAGEMENT, DATA ARCHITECTURE, DATA SCIENCE

Semarchy, MDM Leader Introduces Data-Driven Workflows

Semarchy | January 25, 2023

On January 24, 2023, Semarchy, an industry leader in master data management and integration, introduced data-driven workflows for its award-winning xDM module. With Semarchy's workflows, businesses can jointly manage, convert, and organize raw data into "single source of truth" golden records for every business use case. In addition, workflows integrate with their unified data platform, significantly improving the whole data management experience with a single, no-code solution that can be installed everywhere. François-Xavier "FX" Nicolas, Semarchy's Chief Product Officer, said, "This new data-driven workflow release is a bridge between the worlds of data and processes." He added, "It brings the best of these two worlds to Semarchy's users, upholding the fundamental principles of our product vision: capabilities unified in a low/no-code platform and best-in-class use of the technologies selected by our customers." (Source – Cision PR Newswire) As businesses grow from silo operations to process-led and ultimately data-driven futures, collaborative governance and dynamic workflows are necessary. Semarchy's workflows are inimitable in their pace, dynamism, and integration, enhancing business and data team cooperation. The data-driven, no-code workflow design minimizes technical resources and deployment time while enhancing end users' adoption rate and experience. xDM's premier master data management platform is natively merged with Semarchy's workflows as a single integrated solution to decrease data ownership overhead. The new workflows maintain Semarchy's commitment to accelerating time to value. Key benefits include enhanced operational productivity by dismantling data silos, optimizing processes, and strengthening organizational collaboration; drastically reduced data management operational overhead; and increased enterprise agility with end-to-end clarity of unified data workflows for improved decision-making. Early Access Program (EAP) feedback demonstrated overwhelming approval for Semarchy's redesigned interface, which speeds end-user adoption for managing and performing assigned workflow tasks. Other feedback indicates industry-specific advantages for firms such as Semarchy's U.S. partner, IMT Healthcare. About Semarchy Semarchy, an industry leader in data integration and master data management, enables businesses to derive commercial value from data swiftly. Its single platform lets enterprises of any size rapidly identify, administer, manage, integrate, and display essential data distributed across disparate applications. The firm is natively accessible on AWS, Google Cloud Platform, and Microsoft Azure, in addition to being offered as an on-premises solution. In addition to being maintained as a service, it is backed by an extensive ecosystem of software-as-a-service (SaaS) and professional service partners. The company has offices in Phoenix, United States; London, United Kingdom; Lyon, France; and Mexico City, Mexico.

Read More

BIG DATA MANAGEMENT, BUSINESS STRATEGY, DATA SCIENCE

Arize Introduces Data Lake Connectors

Arize AI | January 24, 2023

On January 23, 2023, Arize AI, an industry leader in machine learning observability, introduced a connection solution for BigQuery, Redshift, Delta Lake, and Snowflake data lakes. As a result, Arize customers with centrally managed inference stores can rapidly connect their ML table data to Arize via Data Lake Connectors for strong model observability. Arize is the market leader in terms of both volumes of models and predictions tracked, surpassing billions of predictions daily. So far, ML observability platforms have struggled to simplify their deployments while simultaneously managing billions of predictions and complicated monitoring services, such as embedding drift. The latest Arize version for data connectors expands clients' connectivity options with the widely used data lakes. The launch comes as the machine learning community converges on various MLOps designs. A modern machine learning architecture approach involves storing inference data in a data lake. ML teams are designing these ML data lakes to power feature stores for feature serving and an inference store for analysis. Arize Data Lake Connectors are intended for smooth integration with today's data lake architectures. Among the benefits of connecting directly to the ML data store are the following: Financial savings can be substantial as compared to alternative techniques of ML monitoring. Teams can run off of an SSOT Integration and onboarding are accelerated and simplified Arize now connects with cloud storage providers (such as Google Cloud Platform, Microsoft Azure, and Amazon Web Services), Python pipelines through an SDK, and Kafka Streaming. With this release, users of data lakes may get real-time model insights more efficiently than ever before. In addition, the platform provides fully managed built-in connectors as part of its cloud and VPC platform, eliminating the need for users to build and maintain complex data pipelines or use a separate ETL tool and providing real-time model performance analysis and monitoring. About Arize AI Arize AI offers an ML observability platform that monitors models and provides insights for troubleshooting production AI. The firm was established in 2020 in Berkeley, California. It allows corporate clients to monitor the performance of AI models using software that searches for unanticipated biases in algorithms, does root-cause analysis when issues arise, and enhances overall model performance. Arize operates in the background, analyzing internal and external data to forecast demand and eliminate supply chain network errors and sales losses. Adobe, Chick-fil-A, and eBay are among the fifty clients of Arize AI.

Read More

BUSINESS INTELLIGENCE, BIG DATA MANAGEMENT, DATA SCIENCE

Pico Announces Major VoIP Analytics Product Upgrade

Pico | January 27, 2023

Pico is one of the global leaders in providing mission-critical technology services, software, data and analytics to the financial markets. While its Corvil Analytics is best known for ensuring the performance of low-latency electronic trading systems, Corvil VoIP Analytics is used to oversee the operations and performance of some of the world's biggest and most complex VoIP installations. To make sure that VoIP is reliable and of good quality, the Corvil method of evaluating network and application activity at the same time is essential. Chief Product Officer at Pico, Stacie Swanstrom, said, “With 20 years of experience in deriving operational intelligence from demanding and dynamic networks, Corvil Analytics has established a critical role in high-performance trading environments.” She added, “Corvil VoIP Analytics takes the ability to capture and decode lossless granularly timestamped data at 100GB and applies it to VoIP applications, providing the same rich data clients have come to expect in both on-prem and cloud deployments.” (Source – GlobeNewswire) One significant advantage of Corvil's wire data technique is that it offers a vendor-agnostic method of managing VoIP quality. Large enterprises often rely on several suppliers, and depending on vendor-bundled diagnostic tools provides only a partial and fragmented picture. In addition, 60% of current Corvil VoIP consumers have switched suppliers in the last three years. Corvil was important to the success of those initiatives, and it also provided a complete picture before, during, and after the migration. The Corvil solution's value is centered on quick and easy access to rich analytics: A unified interface for summary VoIP performance dashboards highlighting overall KPIs and failure rates When troubleshooting difficulties with providers and suppliers, complete packet capture for each call gives a clear record Drill-down into individual calls with a single click and quick access to both signaling messages and the voice call packet stream Splunk, Orion, Nagios, and other third-party operations management systems benefit from extensive open APIs and live streaming of key metrics About Pico Pico is one of the leaders in providing technological services to the financial market sector. Its technology and services are used for mission-critical power systems for worldwide banks, exchanges, electronic trading businesses, quantitative hedge funds, and financial technology service providers. The firm offers a best-in-class array of market solutions that are creative, transparent, and low-latency, as well as an agile and expert service delivery methodology. PicoNet™, a globally comprehensive network infrastructure instrumented natively with Corvil to provide analytics and telemetry, offers instant access to financial markets. Pico is chosen by clients that desire the ability to react quickly and gain an operational advantage in the fast-paced world of financial markets.

Read More

BIG DATA MANAGEMENT, DATA ARCHITECTURE, DATA SCIENCE

Semarchy, MDM Leader Introduces Data-Driven Workflows

Semarchy | January 25, 2023

On January 24, 2023, Semarchy, an industry leader in master data management and integration, introduced data-driven workflows for its award-winning xDM module. With Semarchy's workflows, businesses can jointly manage, convert, and organize raw data into "single source of truth" golden records for every business use case. In addition, workflows integrate with their unified data platform, significantly improving the whole data management experience with a single, no-code solution that can be installed everywhere. François-Xavier "FX" Nicolas, Semarchy's Chief Product Officer, said, "This new data-driven workflow release is a bridge between the worlds of data and processes." He added, "It brings the best of these two worlds to Semarchy's users, upholding the fundamental principles of our product vision: capabilities unified in a low/no-code platform and best-in-class use of the technologies selected by our customers." (Source – Cision PR Newswire) As businesses grow from silo operations to process-led and ultimately data-driven futures, collaborative governance and dynamic workflows are necessary. Semarchy's workflows are inimitable in their pace, dynamism, and integration, enhancing business and data team cooperation. The data-driven, no-code workflow design minimizes technical resources and deployment time while enhancing end users' adoption rate and experience. xDM's premier master data management platform is natively merged with Semarchy's workflows as a single integrated solution to decrease data ownership overhead. The new workflows maintain Semarchy's commitment to accelerating time to value. Key benefits include enhanced operational productivity by dismantling data silos, optimizing processes, and strengthening organizational collaboration; drastically reduced data management operational overhead; and increased enterprise agility with end-to-end clarity of unified data workflows for improved decision-making. Early Access Program (EAP) feedback demonstrated overwhelming approval for Semarchy's redesigned interface, which speeds end-user adoption for managing and performing assigned workflow tasks. Other feedback indicates industry-specific advantages for firms such as Semarchy's U.S. partner, IMT Healthcare. About Semarchy Semarchy, an industry leader in data integration and master data management, enables businesses to derive commercial value from data swiftly. Its single platform lets enterprises of any size rapidly identify, administer, manage, integrate, and display essential data distributed across disparate applications. The firm is natively accessible on AWS, Google Cloud Platform, and Microsoft Azure, in addition to being offered as an on-premises solution. In addition to being maintained as a service, it is backed by an extensive ecosystem of software-as-a-service (SaaS) and professional service partners. The company has offices in Phoenix, United States; London, United Kingdom; Lyon, France; and Mexico City, Mexico.

Read More

BIG DATA MANAGEMENT, BUSINESS STRATEGY, DATA SCIENCE

Arize Introduces Data Lake Connectors

Arize AI | January 24, 2023

On January 23, 2023, Arize AI, an industry leader in machine learning observability, introduced a connection solution for BigQuery, Redshift, Delta Lake, and Snowflake data lakes. As a result, Arize customers with centrally managed inference stores can rapidly connect their ML table data to Arize via Data Lake Connectors for strong model observability. Arize is the market leader in terms of both volumes of models and predictions tracked, surpassing billions of predictions daily. So far, ML observability platforms have struggled to simplify their deployments while simultaneously managing billions of predictions and complicated monitoring services, such as embedding drift. The latest Arize version for data connectors expands clients' connectivity options with the widely used data lakes. The launch comes as the machine learning community converges on various MLOps designs. A modern machine learning architecture approach involves storing inference data in a data lake. ML teams are designing these ML data lakes to power feature stores for feature serving and an inference store for analysis. Arize Data Lake Connectors are intended for smooth integration with today's data lake architectures. Among the benefits of connecting directly to the ML data store are the following: Financial savings can be substantial as compared to alternative techniques of ML monitoring. Teams can run off of an SSOT Integration and onboarding are accelerated and simplified Arize now connects with cloud storage providers (such as Google Cloud Platform, Microsoft Azure, and Amazon Web Services), Python pipelines through an SDK, and Kafka Streaming. With this release, users of data lakes may get real-time model insights more efficiently than ever before. In addition, the platform provides fully managed built-in connectors as part of its cloud and VPC platform, eliminating the need for users to build and maintain complex data pipelines or use a separate ETL tool and providing real-time model performance analysis and monitoring. About Arize AI Arize AI offers an ML observability platform that monitors models and provides insights for troubleshooting production AI. The firm was established in 2020 in Berkeley, California. It allows corporate clients to monitor the performance of AI models using software that searches for unanticipated biases in algorithms, does root-cause analysis when issues arise, and enhances overall model performance. Arize operates in the background, analyzing internal and external data to forecast demand and eliminate supply chain network errors and sales losses. Adobe, Chick-fil-A, and eBay are among the fifty clients of Arize AI.

Read More

Events