Q&A with Gil Eyal, Founder at HYPR & Managing Partner at Starfund

Gil Eyal, Founder at HYPR & Managing Partner at Starfund, has revolutionized the way many of the world’s biggest agencies and brands are running influencer marketing by focusing on the same data, analytics, and audience demographic information relevant to traditional digital marketing. He was recently selected at #30 on the list of the most influential people in Influencer Marketing.

Gil is an accomplished public speaker and has delivered keynotes at notable influencer marketing conferences, including Influencer Marketing Days in New York and Influencer Marketing Hub in London. He was selected as the 2017 recipient of the Digiday Top Boss Award in the technology industry, as one of 10 Israelis impacting the New York Tech Scene, as well as one of 40 must-follow digital media influencers. Gil is also a two-time winner of the MarCom Awards for Excellence in Marketing and Communications.

If you don’t have a clear understanding of who your audience is and which messaging resonates with them, you will never be truly successful.



MEDIA 7: Could you please tell us a little bit about yourself and what made you choose this career path?
GIL EYAL:
I think many of us have a moment that inspires us in a way that changes our direction. For me, it was sometime around the year 2005 when I came across a book titled, “Buzzmarketing” by Mark Hughes, where he detailed a variety of marketing campaigns that leveraged the concept of getting a conversation going around a subject. At the time I was working in a job that bored me to death as an attorney and thought I would be doing it for the rest of my career.

I couldn’t get that book out of my head. Mind you there was no Facebook and there were no viral online channels you could leverage to market. This was old-school marketing and it required a level of creativity and sophistication that fascinated me. 4 years later, I was done with law and studying for my MBA at the Kellogg School of Management where reality hit hard.

No one had any interest in hiring a former attorney for marketing jobs even if they did get their MBA from a respected program. I was probably the last in my class to find an internship after my first year and I was determined to figure out things before I came back for the second. I spent the summer in two places. The first was Austin, Texas, where I worked for Dell. It was a wonderful experience, but the second half of the summer really got me going. I worked for Playdom, at the time, the largest game developer on MySpace and the fourth biggest on Facebook. I learned how meaningful leveraging creative marketing channels and methods (while paying attention to the fundamentals) were to get the company to hundreds of millions of users. More importantly, I got a front seat to a $750M exit and realized I will be in tech for the rest of my life because nothing could compete with the excitement of building something so amazing that someone is willing to pay hundreds of millions of dollars for it. I came back for my second year but I wasn’t interviewing. I was all set to figure things out in the startup world and started working with founders who needed help with marketing. Though it was early, I recognized the opportunity, that visibility and credibility generated by celebrities would provide these companies and quickly carved myself a niche.

Within 3 years, I had done over 200 deals with celebrities such as Leonardo DiCaprio, Lance Armstrong, Serena Williams, Kevin Hart, Zendaya, Lil Wayne, and more. In 2013 I decided to build technology around identifying the influential people online and that led to the birth of HYPR.

M7: What tools do you use for influencer marketing? How do you find influencers in a specific niche?
GE:
I sold HYPR to Julius in April 2020. We combined our platforms to build an amazing tool that helps sift through millions of influencers based on their audience characteristics, demographics, and topics where they are influential. The tool makes discovery easy.


I don’t believe in hiring influencers just because of their audience size. When I work with celebrities or influencers, it's primarily for the credibility they can generate for the product I want to promote.



M7: What are the most promising channels for campaign management and why?
GE:
I think influencer marketing is struggling to make things less manual and the solutions in the market today are limited. I think how you choose to activate influencers really varies based on the type of brand you represent. Certain brands need to vet each post and ensure they work with influencers who are perfectly on-brand. Others just need reach and conversions. The result is that one solution doesn’t fit all. The first type needs vetting tools, content approval, and time management tools while the other needs automation and performance tracking. My guess is that you won’t see one winner in the space – different solutions will prove to be the best for different customers.

M7: How do you define content personalization? Which technologies are garnering the maximum mileage in this sector?
GE:
As privacy awareness is gaining more attention due to explicit violations of privacy rights by some of the largest players in the space, content personalization is becoming more challenging. Requiring users to opt-in is not as simple as it was in the early days of the internet where you just hid code in another program and assumed people won’t read your terms of service. The new regulations are a positive move in my opinion but in the short run, they definitely reduce the quality of user experience.

Companies like Target have been so focused on the personalization of marketing content that they sometimes forget it can be really creepy or even harmful. In Target’s case, there was a story of a father who found out his daughter was pregnant because Target sent her emails targeted at moms-to-be. I invested in a company called itsmydata which helps consumers protect their privacy when online stores don’t.

The result is that personalization is going to take a backseat until brands can really figure it out. Technologies that can get people to willingly opt into data sharing and content optimization will come out victorious here but I’m still not sure I have seen the proper solution.


As privacy awareness is gaining more attention due to explicit violations of privacy rights by some of the largest players in the space, content personalization is becoming more challenging.



M7: What do you believe are the top three product marketing challenges in the post COVID-19 era?
GE: 1. A change in consumer behavior. If I’m not going out, do I really need to dress nicely? Do I need to smell good? Should I be spending a lot of money on new shoes? Which products are gone for good and which are just taking a hiatus until we are all vaccinated?

2. With people spending even more time online, are marketers even more confined to the programmatic marketing channels that companies like Facebook and Google provide? If so, how do you deal with the price increases as these companies optimize their process to make sure they can squeeze as much money as possible out of marketers?

3. As companies like Hulu and Netflix demonstrate that people are willing to pay for content, often pay in order not to see your ads, how do you gain their attention? Can you afford to remain uncreative with your marketing attempts?

M7: In the era of Fake News and Malvertising, how do you stay on top of your business model?
GE:
I don’t believe in hiring influencers just because of their audience size. When I work with celebrities or influencers, it's primarily for the credibility they can generate for the product I want to promote. When Michael Jordan wore his first Nike shoes, the message was broader than just reaching all of the people who were interested in him. It was about the fact that these shoes were so good that he would wear them in a professional game against the best athletes in the world and he would come out on top. I want to work with influencers and celebrities that convey an authentic and believable story of why this product is superior.

M7: What is your marketing mantra to stand out in an overly saturated MarTech space?
GE:
Traditional fundamentals still apply. It doesn’t matter what kind of marketing you are doing, if you don’t have a clear understanding of who your audience is, how to reach them and which messaging resonates with them, you will never be truly successful. Influencers are just another channel but they need to be able to do more than just reach an audience. They need to be able to believably convey a message that will resonate with an audience. When you see an influencer marketing campaign fail, it’s almost always because the influencers do not do that, and not because they’re being fraudulent or have a weak following.

ABOUT HYPR

HYPR is an influencer marketing platform focused on leveraging data to automate smart decision making and identify micro-influencers and activate them at scale. HYPR was sold to Julius in April 2020.

More THOUGHT LEADERS

Q&A with Charles Southwood, Vice President, N. Europe and MEA at Denodo

Media 7 | September 15, 2021

Charles Southwood, Regional VP at Denodo Technologies is responsible for the company’s business revenues in Northern Europe, Middle East and South Africa. He is passionate about working in rapidly moving and innovative markets to support customer success and to align IT solutions that meet the changing business needs. With a degree in engineering from Imperial College London, Charles has over 20 years of experience in data integration, big data, IT infrastructure/IT operations and Business Analytics....

Read More

Q&A with Vishal Srivastava, Vice President (Model Validation) at Citi

Media 7 | September 8, 2021

Vishal Srivastava, Vice President (Model Validation) at Citi was invited as a keynote speaker to present on Fraud Analytics using Machine Learning at the International Automation in Banking Summit in New York in November 2019. Vishal has experience in quantitative risk modeling using advanced engineering, statistical, and machine learning technologies. His academic qualifications in combination with a Ph.D. in Chemical Engineering and an MBA in Finance have enabled him to challenge quantitative risk models with scientific rigor. Vishal’s doctoral thesis included the development of statistical and machine learning-based risk models—some of which are currently being used commercially. Vishal has 120+ peer-reviewed citations in areas such as risk management, quantitative modeling, machine learning, and predictive analytics....

Read More

Q&A with Sadiqah Musa, Co-Founder at Black In Data

Media 7 | September 1, 2021

Sadiqah Musa, Co-Founder at Black In Data, is also an experienced Senior Data Analyst at Guardian News and Media with a demonstrated history of working in the energy and publishing sectors. She is skilled in Advanced Excel, SQL, Python, data visualization, project management, and Data Analysis and has a strong professional background with a Master of Science (MSc) from The University of Manchester....

Read More

Q&A with Charles Southwood, Vice President, N. Europe and MEA at Denodo

Media 7 | September 15, 2021

Charles Southwood, Regional VP at Denodo Technologies is responsible for the company’s business revenues in Northern Europe, Middle East and South Africa. He is passionate about working in rapidly moving and innovative markets to support customer success and to align IT solutions that meet the changing business needs. With a degree in engineering from Imperial College London, Charles has over 20 years of experience in data integration, big data, IT infrastructure/IT operations and Business Analytics....

Read More

Q&A with Vishal Srivastava, Vice President (Model Validation) at Citi

Media 7 | September 8, 2021

Vishal Srivastava, Vice President (Model Validation) at Citi was invited as a keynote speaker to present on Fraud Analytics using Machine Learning at the International Automation in Banking Summit in New York in November 2019. Vishal has experience in quantitative risk modeling using advanced engineering, statistical, and machine learning technologies. His academic qualifications in combination with a Ph.D. in Chemical Engineering and an MBA in Finance have enabled him to challenge quantitative risk models with scientific rigor. Vishal’s doctoral thesis included the development of statistical and machine learning-based risk models—some of which are currently being used commercially. Vishal has 120+ peer-reviewed citations in areas such as risk management, quantitative modeling, machine learning, and predictive analytics....

Read More

Q&A with Sadiqah Musa, Co-Founder at Black In Data

Media 7 | September 1, 2021

Sadiqah Musa, Co-Founder at Black In Data, is also an experienced Senior Data Analyst at Guardian News and Media with a demonstrated history of working in the energy and publishing sectors. She is skilled in Advanced Excel, SQL, Python, data visualization, project management, and Data Analysis and has a strong professional background with a Master of Science (MSc) from The University of Manchester....

Read More

Related News

Business Strategy

Devo Security Data Platform Attains FedRAMP Authorization

Devo | January 09, 2024

Devo Technology, the security data analytics company, today announced that the Devo Security Data Platform received Authorization to Operate (ATO) at the Moderate level under the Federal Risk and Authorization Management Program (FedRAMP). The Devo Security Data Platform successfully completed FedRAMP's rigorous accreditation process, enabling federal agencies to secure their environments with a market-leading security information and event management (SIEM). Agencies and their partners can now leverage Devo to solve their toughest IT and security challenges with unparalleled visibility and a unified view of risk posture, security operations and the threat landscape. The demand to keep pace with rapidly evolving cyber threats at cloud speed and scale has never been higher for the U.S. government. New Office of Management and Budget (OMB) regulations require federal agencies to collect and retain logs for long time periods. These requirements strain legacy SIEM and logging solutions, resulting in higher license and maintenance costs and slower query times. The Devo Security Data Platform's massive ingestion capabilities overcome these challenges and enable agencies to manage petabytes of data—from any device or application—cost-effectively and performantly in the cloud. Kayla Williams, CISO, Devo, said: "Devo relentlessly maintains the highest standards of internal security controls to ensure customers can protect themselves from security threats with peace of mind. Commercial customers have used the Devo Security Data Platform in the cloud for years, and this milestone enables us to continue to extend the same seamless experience to federal agencies and their partners." The Small Business Administration sponsored Devo's authorization. FedRAMP was established to provide a cost-effective, risk-based approach for the adoption and use of cloud services by the federal government. FedRAMP empowers agencies to use modern cloud technologies with an emphasis on the security and protection of federal information. The Devo Security Data Platform is also available in the AWS GovCloud Marketplace, an isolated AWS Region designed to host sensitive data and regulated workloads in the cloud, assisting customers with U.S. federal, state and local government compliance requirements. About Devo Devo unleashes the power of the SOC. The Devo Security Data Platform, powered by our HyperStream technology, is purpose-built to provide the speed and scale, real-time analytics, and actionable intelligence global enterprises need to defend expanding attack surfaces. An ally in keeping your organization secure, Devo combines the power of people and AI to augment security teams, leading to better insights and faster outcomes. Headquartered in Cambridge, Massachusetts, with operations in North America, Europe and Asia Pacific, Devo is backed by Insight Partners, Georgian, TCV, General Atlantic, Bessemer Venture Partners, Kibo Ventures and Eurazeo. Learn more at www.devo.com.

Read More

Data Architecture

SingleStore Announces Real-time Data Platform to Further Accelerate AI, Analytics and Application Development

SingleStore | January 25, 2024

SingleStore, the database that allows you to transact, analyze and contextualize data, today announced powerful new capabilities — making it the industry’s only real-time data platform. With its latest release, dubbed SingleStore Pro Max, the company announced ground-breaking features like indexed vector search, an on-demand compute service for GPUs/ CPUs and a new free shared tier, among several other innovative new products. Together, these capabilities shrink development cycles while providing the performance and scale that customers need for building applications. In an explosive generative AI landscape, companies are looking for a modern data platform that’s ready for enterprise AI use cases — one with best-available tooling to accelerate development, simultaneously allowing them to marry structured or semi-structured data residing in enterprise systems with unstructured data lying in data lakes. “We believe that a data platform should both create new revenue streams while also decreasing technological costs and complexity for customers. And this can only happen with simplicity at the core,” said Raj Verma, CEO, SingleStore. “This isn’t just a product update, it’s a quantum leap… SingleStore is offering truly transformative capabilities in a single platform for customers to build all kinds of real-time applications, AI or otherwise.” “At Adobe, we aim to change the world through digital experiences,” said Matt Newman, Principal Data Architect, Adobe. “SingleStore’s latest release is exciting as it pushes what is possible when it comes to database technology, real-time analytics and building modern applications that support AI workloads. We’re looking forward to these new features as more and more of our customers are seeking ways to take full advantage of generative Al capabilities.” Key new features launched include: Indexed vector search. SingleStore has announced support for vector search using Approximate Nearest Neighbor (ANN) vector indexing algorithms, leading to 800-1,000x faster vector search performance than precise methods (KNN). With both full-text and indexed vector search capabilities, SingleStore offers developers true hybrid search that takes advantage of the full power of SQL for queries, joins, filters and aggregations. These capabilities firmly place SingleStore above vector-only databases that require niche query languages and are not designed to meet enterprise security and resiliency needs. Free shared tier. SingleStore has announced a new cloud-based Free Shared Tier that’s designed for startups and developers to quickly bring their ideas to life — without the need to commit to a paid plan. On-demand compute service for GPUs and CPUs. SingleStore announces a compute service that works alongside SingleStore’s native Notebooks to let developers spin up GPUs and CPUs to run database-adjacent workloads including data preparation, ETL, third-party native application frameworks, etc. This capability brings compute to algorithms, rather than the other way around, enabling developers to build highly performant AI applications safely and securely using SingleStore — without unnecessary data movement. New CDC capabilities for data ingest and egress. To ease the burden and costs of moving data in and out of SingleStore, SingleStore is adding native capabilities for real-time Change Data Capture (CDC) in for MongoDB®, MySQL and ingestion from Apache Iceberg without requiring other third party CDC tools. SingleStore will also support CDC out capabilities that ease migrations and enable the use of SingleStore as a source for other applications and databases like data warehouses and lakehouses. SingleStore Kai™. Now generally available, and ready for both analytical and transactional processing for apps originally built on MongoDB. Announced in public preview in early 2023, SingleStore Kai is an API to deliver over 100x faster analytics on MongoDB® with no query changes or data transformations required. Today, SingleStore Kai supports BSON data format natively, has improved transactional performance, increased performance for arrays and offers industry-leading compatibility with MongoDB query language. Projections: To further advance as the world’s fastest HTAP database, SingleStore has added Projections. Projections allow developers to greatly speed up range filters and group by operations by introducing secondary sort and shard keys. Query performance improvements range from 2-3x or more, depending on the size of the table. With this latest release, SingleStore becomes the industry’s first and only real-time data platform designed for all applications, analytics and AI. SingleStore supports high-throughput ingest performance, ACID transactions and low-latency analytics; and structured, semi-structured (JSON, BSON, text) and unstructured data (vector embeddings of audio, video, images, PDFs, etc.). Finally, SingleStore’s data platform is designed not just with developers in mind, but also ML engineers, data engineers and data scientists. “Our new features and capabilities advance SingleStore’s mission of offering a real-time data platform for the next wave of gen AI and data applications,” said Nadeem Asghar, SVP, Product Management + Strategy at SingleStore. “New features, including vector search, Projections, Apache Iceberg, Scheduled Notebooks, autoscaling, GPU compute services, SingleStore Kai™, and the Free Shared Tier allow startups — as well as global enterprises — to quickly build and scale enterprise-grade real-time AI applications. We make data integration with third-party databases easy with both CDC in and CDC out support.” "Although generative AI, LLM, and vector search capabilities are early stage, they promise to deliver a richer data experience with translytical architecture," states the 2023 report, “Translytical Architecture 2.0 Evolves To Support Distributed, Multimodel, And AI Capabilities,” authored by Noel Yuhanna, Vice President and Principal Analyst at Forrester Research. "Generative AI and LLM can help democratize data through natural language query (NLQ), offering a ChatGPT-like interface. Also, vector storage and index can be leveraged to perform similarity searches to support data intelligence." SingleStore has been on a fast track leading innovation around generative AI. The company’s product evolution has been accompanied by high-momentum growth in customers and surpassing $100M in ARR late last year. SingleStore also recently ranked #2 in the emerging category of vector databases, and was recognized by TrustRadius as a top vector database in 2023. Finally, SingleStore was a winner of InfoWorld’s Technology of the year in the database category. To learn more about SingleStore visit here. About SingleStore SingleStore empowers the world’s leading organizations to build and scale modern applications using the only database that allows you to transact, analyze and contextualize data in real time. With streaming data ingestion, support for both transactions and analytics, horizontal scalability and hybrid vector search capabilities, SingleStore helps deliver 10-100x better performance at 1/3 the costs compared to legacy architectures. Hundreds of customers worldwide — including Fortune 500 companies and global data leaders — use SingleStore to power real-time applications and analytics. Learn more at singlestore.com. Follow us @SingleStoreDB on Twitter or visit www.singlestore.com.

Read More

Data Visualization

SensiML Unveils Data Studio - Next-Generation Sensor Data Management for AI / ML

SensiML | December 20, 2023

SensiML Corporation, a leader in AI software for IoT and a subsidiary of QuickLogic, announced the launch of Data Studio, a ground-breaking platform designed to redefine the landscape of sensor data management. With a focus on practicality and efficiency, Data Studio empowers engineers and data scientists by offering an integrated solution that addresses the most time-consuming tasks in AI engineering projects - creating high-quality datasets for evaluating and developing ML models. According to Cognilytica, a well-respected AI / ML consulting firm, approximately 80% of the total time for machine learning (ML) projects is allocated to data preparation. These tasks include data identification, aggregation, cleansing, labeling, and augmentation – all of which are supported in SensiML's collaborative development environment. SensiML Data Studio significantly improves productivity and simplifies dataset management for anyone working on sensor data ML projects. With real-time connectivity, intuitive visualization tools, sensor data video synchronization, and robust support for large-scale collaborative projects, it offers a seamless experience for developers on edge devices, gateways, PCs, and cloud platforms. A comprehensive overview of all the features of Data Studio can be found on the SensiML website. The primary features are highlighted below: Effortless Data Capture and Import - Capture live sensor data, analyze it instantly, and label any data for seamless insights. Collaboratively Label Sensor Data - Employ flexible labeling methodologies for sensor data, including manual, AI-assisted, and custom – and sync video for effortless complex labeling. Store and analyze data locally on your computer or remotely. Data Analysis and Model Evaluation - Visually compare ML models, filter, transform, and fuse sensor data – all with built-in tools and your own Python expertise. Label and Data Versioning – Keep track of your labels and model results with versioned labels. Easily export your project to an open format. "SensiML Data Studio makes sensor data management and analysis more accessible and efficient, empowering developers to build better, more impactful applications using sensor data across a wide range of industries," said Chris Knorowski, CTO of SensiML. SensiML Data Studio is poised to transform sensor data analysis, offering a valuable resource for researchers, engineers, and data scientists across diverse sectors from agriculture and consumer wearables to medical devices, smart buildings, and factory maintenance. About SensiML SensiML, a subsidiary of QuickLogic (NASDAQ: QUIK), offers cutting-edge software that enables ultra-low power IoT endpoints that implement AI to transform raw sensor data into meaningful insight at the device itself. The company's flagship solution, the SensiML Analytics Toolkit, provides an end-to-end development platform spanning data collection, labeling, algorithm and firmware auto-generation, and testing. The SensiML Toolkit supports Arm® Cortex®-M class and higher microcontroller cores, Intel® x86 instruction set processors, and heterogeneous core QuickLogic SoCs and QuickAI platforms with FPGA optimizations.

Read More

Business Strategy

Devo Security Data Platform Attains FedRAMP Authorization

Devo | January 09, 2024

Devo Technology, the security data analytics company, today announced that the Devo Security Data Platform received Authorization to Operate (ATO) at the Moderate level under the Federal Risk and Authorization Management Program (FedRAMP). The Devo Security Data Platform successfully completed FedRAMP's rigorous accreditation process, enabling federal agencies to secure their environments with a market-leading security information and event management (SIEM). Agencies and their partners can now leverage Devo to solve their toughest IT and security challenges with unparalleled visibility and a unified view of risk posture, security operations and the threat landscape. The demand to keep pace with rapidly evolving cyber threats at cloud speed and scale has never been higher for the U.S. government. New Office of Management and Budget (OMB) regulations require federal agencies to collect and retain logs for long time periods. These requirements strain legacy SIEM and logging solutions, resulting in higher license and maintenance costs and slower query times. The Devo Security Data Platform's massive ingestion capabilities overcome these challenges and enable agencies to manage petabytes of data—from any device or application—cost-effectively and performantly in the cloud. Kayla Williams, CISO, Devo, said: "Devo relentlessly maintains the highest standards of internal security controls to ensure customers can protect themselves from security threats with peace of mind. Commercial customers have used the Devo Security Data Platform in the cloud for years, and this milestone enables us to continue to extend the same seamless experience to federal agencies and their partners." The Small Business Administration sponsored Devo's authorization. FedRAMP was established to provide a cost-effective, risk-based approach for the adoption and use of cloud services by the federal government. FedRAMP empowers agencies to use modern cloud technologies with an emphasis on the security and protection of federal information. The Devo Security Data Platform is also available in the AWS GovCloud Marketplace, an isolated AWS Region designed to host sensitive data and regulated workloads in the cloud, assisting customers with U.S. federal, state and local government compliance requirements. About Devo Devo unleashes the power of the SOC. The Devo Security Data Platform, powered by our HyperStream technology, is purpose-built to provide the speed and scale, real-time analytics, and actionable intelligence global enterprises need to defend expanding attack surfaces. An ally in keeping your organization secure, Devo combines the power of people and AI to augment security teams, leading to better insights and faster outcomes. Headquartered in Cambridge, Massachusetts, with operations in North America, Europe and Asia Pacific, Devo is backed by Insight Partners, Georgian, TCV, General Atlantic, Bessemer Venture Partners, Kibo Ventures and Eurazeo. Learn more at www.devo.com.

Read More

Data Architecture

SingleStore Announces Real-time Data Platform to Further Accelerate AI, Analytics and Application Development

SingleStore | January 25, 2024

SingleStore, the database that allows you to transact, analyze and contextualize data, today announced powerful new capabilities — making it the industry’s only real-time data platform. With its latest release, dubbed SingleStore Pro Max, the company announced ground-breaking features like indexed vector search, an on-demand compute service for GPUs/ CPUs and a new free shared tier, among several other innovative new products. Together, these capabilities shrink development cycles while providing the performance and scale that customers need for building applications. In an explosive generative AI landscape, companies are looking for a modern data platform that’s ready for enterprise AI use cases — one with best-available tooling to accelerate development, simultaneously allowing them to marry structured or semi-structured data residing in enterprise systems with unstructured data lying in data lakes. “We believe that a data platform should both create new revenue streams while also decreasing technological costs and complexity for customers. And this can only happen with simplicity at the core,” said Raj Verma, CEO, SingleStore. “This isn’t just a product update, it’s a quantum leap… SingleStore is offering truly transformative capabilities in a single platform for customers to build all kinds of real-time applications, AI or otherwise.” “At Adobe, we aim to change the world through digital experiences,” said Matt Newman, Principal Data Architect, Adobe. “SingleStore’s latest release is exciting as it pushes what is possible when it comes to database technology, real-time analytics and building modern applications that support AI workloads. We’re looking forward to these new features as more and more of our customers are seeking ways to take full advantage of generative Al capabilities.” Key new features launched include: Indexed vector search. SingleStore has announced support for vector search using Approximate Nearest Neighbor (ANN) vector indexing algorithms, leading to 800-1,000x faster vector search performance than precise methods (KNN). With both full-text and indexed vector search capabilities, SingleStore offers developers true hybrid search that takes advantage of the full power of SQL for queries, joins, filters and aggregations. These capabilities firmly place SingleStore above vector-only databases that require niche query languages and are not designed to meet enterprise security and resiliency needs. Free shared tier. SingleStore has announced a new cloud-based Free Shared Tier that’s designed for startups and developers to quickly bring their ideas to life — without the need to commit to a paid plan. On-demand compute service for GPUs and CPUs. SingleStore announces a compute service that works alongside SingleStore’s native Notebooks to let developers spin up GPUs and CPUs to run database-adjacent workloads including data preparation, ETL, third-party native application frameworks, etc. This capability brings compute to algorithms, rather than the other way around, enabling developers to build highly performant AI applications safely and securely using SingleStore — without unnecessary data movement. New CDC capabilities for data ingest and egress. To ease the burden and costs of moving data in and out of SingleStore, SingleStore is adding native capabilities for real-time Change Data Capture (CDC) in for MongoDB®, MySQL and ingestion from Apache Iceberg without requiring other third party CDC tools. SingleStore will also support CDC out capabilities that ease migrations and enable the use of SingleStore as a source for other applications and databases like data warehouses and lakehouses. SingleStore Kai™. Now generally available, and ready for both analytical and transactional processing for apps originally built on MongoDB. Announced in public preview in early 2023, SingleStore Kai is an API to deliver over 100x faster analytics on MongoDB® with no query changes or data transformations required. Today, SingleStore Kai supports BSON data format natively, has improved transactional performance, increased performance for arrays and offers industry-leading compatibility with MongoDB query language. Projections: To further advance as the world’s fastest HTAP database, SingleStore has added Projections. Projections allow developers to greatly speed up range filters and group by operations by introducing secondary sort and shard keys. Query performance improvements range from 2-3x or more, depending on the size of the table. With this latest release, SingleStore becomes the industry’s first and only real-time data platform designed for all applications, analytics and AI. SingleStore supports high-throughput ingest performance, ACID transactions and low-latency analytics; and structured, semi-structured (JSON, BSON, text) and unstructured data (vector embeddings of audio, video, images, PDFs, etc.). Finally, SingleStore’s data platform is designed not just with developers in mind, but also ML engineers, data engineers and data scientists. “Our new features and capabilities advance SingleStore’s mission of offering a real-time data platform for the next wave of gen AI and data applications,” said Nadeem Asghar, SVP, Product Management + Strategy at SingleStore. “New features, including vector search, Projections, Apache Iceberg, Scheduled Notebooks, autoscaling, GPU compute services, SingleStore Kai™, and the Free Shared Tier allow startups — as well as global enterprises — to quickly build and scale enterprise-grade real-time AI applications. We make data integration with third-party databases easy with both CDC in and CDC out support.” "Although generative AI, LLM, and vector search capabilities are early stage, they promise to deliver a richer data experience with translytical architecture," states the 2023 report, “Translytical Architecture 2.0 Evolves To Support Distributed, Multimodel, And AI Capabilities,” authored by Noel Yuhanna, Vice President and Principal Analyst at Forrester Research. "Generative AI and LLM can help democratize data through natural language query (NLQ), offering a ChatGPT-like interface. Also, vector storage and index can be leveraged to perform similarity searches to support data intelligence." SingleStore has been on a fast track leading innovation around generative AI. The company’s product evolution has been accompanied by high-momentum growth in customers and surpassing $100M in ARR late last year. SingleStore also recently ranked #2 in the emerging category of vector databases, and was recognized by TrustRadius as a top vector database in 2023. Finally, SingleStore was a winner of InfoWorld’s Technology of the year in the database category. To learn more about SingleStore visit here. About SingleStore SingleStore empowers the world’s leading organizations to build and scale modern applications using the only database that allows you to transact, analyze and contextualize data in real time. With streaming data ingestion, support for both transactions and analytics, horizontal scalability and hybrid vector search capabilities, SingleStore helps deliver 10-100x better performance at 1/3 the costs compared to legacy architectures. Hundreds of customers worldwide — including Fortune 500 companies and global data leaders — use SingleStore to power real-time applications and analytics. Learn more at singlestore.com. Follow us @SingleStoreDB on Twitter or visit www.singlestore.com.

Read More

Data Visualization

SensiML Unveils Data Studio - Next-Generation Sensor Data Management for AI / ML

SensiML | December 20, 2023

SensiML Corporation, a leader in AI software for IoT and a subsidiary of QuickLogic, announced the launch of Data Studio, a ground-breaking platform designed to redefine the landscape of sensor data management. With a focus on practicality and efficiency, Data Studio empowers engineers and data scientists by offering an integrated solution that addresses the most time-consuming tasks in AI engineering projects - creating high-quality datasets for evaluating and developing ML models. According to Cognilytica, a well-respected AI / ML consulting firm, approximately 80% of the total time for machine learning (ML) projects is allocated to data preparation. These tasks include data identification, aggregation, cleansing, labeling, and augmentation – all of which are supported in SensiML's collaborative development environment. SensiML Data Studio significantly improves productivity and simplifies dataset management for anyone working on sensor data ML projects. With real-time connectivity, intuitive visualization tools, sensor data video synchronization, and robust support for large-scale collaborative projects, it offers a seamless experience for developers on edge devices, gateways, PCs, and cloud platforms. A comprehensive overview of all the features of Data Studio can be found on the SensiML website. The primary features are highlighted below: Effortless Data Capture and Import - Capture live sensor data, analyze it instantly, and label any data for seamless insights. Collaboratively Label Sensor Data - Employ flexible labeling methodologies for sensor data, including manual, AI-assisted, and custom – and sync video for effortless complex labeling. Store and analyze data locally on your computer or remotely. Data Analysis and Model Evaluation - Visually compare ML models, filter, transform, and fuse sensor data – all with built-in tools and your own Python expertise. Label and Data Versioning – Keep track of your labels and model results with versioned labels. Easily export your project to an open format. "SensiML Data Studio makes sensor data management and analysis more accessible and efficient, empowering developers to build better, more impactful applications using sensor data across a wide range of industries," said Chris Knorowski, CTO of SensiML. SensiML Data Studio is poised to transform sensor data analysis, offering a valuable resource for researchers, engineers, and data scientists across diverse sectors from agriculture and consumer wearables to medical devices, smart buildings, and factory maintenance. About SensiML SensiML, a subsidiary of QuickLogic (NASDAQ: QUIK), offers cutting-edge software that enables ultra-low power IoT endpoints that implement AI to transform raw sensor data into meaningful insight at the device itself. The company's flagship solution, the SensiML Analytics Toolkit, provides an end-to-end development platform spanning data collection, labeling, algorithm and firmware auto-generation, and testing. The SensiML Toolkit supports Arm® Cortex®-M class and higher microcontroller cores, Intel® x86 instruction set processors, and heterogeneous core QuickLogic SoCs and QuickAI platforms with FPGA optimizations.

Read More

Spotlight

HYPR

HYPR is an influencer marketing platform focused on leveraging data to automate smart decision making and identify micro-influencers and activate them at scale. HYPR was sold to Julius in April 2020....

Events

Resources