insideBIGDATA Latest News – 1/26/2022

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In this regular column, we’ll bring you all the latest industry news centered around our main topics of focus: big data, data science, machine learning, AI, and deep learning. Our industry is constantly accelerating with new products and services being announced everyday. Fortunately, we’re in close touch with vendors from this vast ecosystem, so we’re in a unique position to inform you about all that’s new and exciting. Our massive industry database is growing all the time so stay tuned for the latest news items describing technology that may make you and your organization more competitive.

Domino Data Lab Unveils Platform to Accelerate Model Velocity for the Model-Driven Business 

Domino Data Lab, provider of a leading Enterprise MLOps platform trusted by over 20% of the Fortune 100, introduced Domino 5.0 with groundbreaking new capabilities that unleash model velocity, a metric of how fast data science teams build and update models, by solving common challenges related to compute infrastructure, data, and productionization of models. Domino 5.0 is also the first Enterprise MLOps solution validated and integrated with NVIDIA AI Enterprise, an end-to-end, enterprise platform optimized for AI workloads.

As companies invest in data science and machine learning, many fail to realize the impact they expect because existing processes, cultures and technologies make it hard for data scientists to rapidly and safely develop and deliver models. New capabilities that unify model development, deployment and monitoring make Domino 5.0 the only platform that facilitates the end-to-end data science lifecycle while giving data scientists the flexibility to use their preferred tools. By making data scientists more productive and increasing collaboration and reuse of work, Domino 5.0 unleashes model velocity for data science teams.

“Over the next decade, winning companies across industries will be the ones that weave data science into the fabric of their business and drive rapid continuous improvement of their models” said Nick Elprin, CEO and co-founder of Domino Data Lab. “Domino 5.0 gives enterprises the modern platform they need to maximize their model velocity and the impact of their data science investment.”

QB Launches Liquidity Tracking Tool for Real-time Analysis to Help Achieve Best Execution

Quantitative Brokers (QB), a leading provider of advanced execution algorithms and data-driven analytics for global futures, options and OTC Fixed Income markets, launched a free online tool that allows institutional traders to track market liquidity and quote size at a level of transparency and ease never before available. The announcement also marks a new direction for QB, who aims to offer an integrative suite of market microstructure analytics soon.

 “The Liquidity Tracker provides guidance and visibility across multiple markets,” said Robert Almgren, QB’s Co-Founder and Chief Scientist. “We are very transparent with our clients and strongly believe the wider trading community can benefit from this open-source tool.”

Fivetran Transformations for dbt Core Accelerates Data Transformations 

Fivetran, a leader in modern data integration, announced product enhancements extending dbt Core* with integrated scheduling and data lineage graphs. Fivetran previously announced support for dbt Core by dbt Labs, one of the most popular open-source transformations frameworks in the data analyst community. Today’s news signals a deeper integration of open-source dbt Core into Fivetran, and delivers new features to help companies simplify the complexities of the modern data stack, cut costs through ELT (extract-load-transform) automation, and accelerate data-driven decisions. 

Transformations are a critical step in the ELT process, as they turn raw data into clean analytics-ready datasets for use in downstream data analytics workflows — from basic reporting to data science. Without an effective and reliable way to sanitize and standardize these datasets, companies are often unable to translate raw data into analytics-ready form. 

“Fivetran’s ability to orchestrate dbt Core models brings the E, L and T together and eliminates previous gaps in the process. Our users are clear about their need for the freshest data while controlling their transformation costs,” said Fraser Harris, Vice President of Product at Fivetran. “With Fivetran Transformations, our complete ELT data pipelines empower our customers to make revenue-impacting, data-driven decisions. This is fulfilling Fivetran’s mission to make access to data as simple and reliable as electricity.”

Deci Launches SuperGradients, an “All-in-One” Open-Source Deep Learning Training Library for Computer Vision Models

Deci, the deep learning development company harnessing Artificial Intelligence (AI) to build AI, announced the launch of SuperGradients – an “all-in-one” deep learning training library for computer vision models. SuperGradients enables developers to train PyTorch-based models for the most common computer vision tasks, including object detection, image classification and semantic segmentation with just one training script. In addition, users can easily load and fine-tune pre-trained state-of-the-art models (YOLOv5, DDRNet, EfficientNet, RegNet, ResNet, MobileNet, etc.), of which many were optimized to deliver higher accuracy compared to existing training libraries.

AI developers face an upward struggle developing production-ready deep learning models for deployment. The overhead of integrating with various existing training tools and the effort to reproduce the training results for state-of-the-art models is time consuming and causes headaches for beginners and experts alike. Developed by Deci’s deep learning experts to tackle these very problems, SuperGradients offers a wide range of pre-trained production-ready deep learning models that were tested in production environments (i.e. converted to optimized deployable runtime executors such as OpenVINO, TensorRT, ONNX). The library also includes proven training recipes for easy reproduction of training results, thus making AI more accessible for everyone.

“As deep learning is maturing and becoming more widely adopted, the model development and training processes must be simplified. In order to deliver on the promise of deep learning, teams require tools that can facilitate the architecture selection phase, as well as enable them to achieve better training results faster,” said Yonatan Geifman, co-founder and CEO of Deci. “This is why we decided to release SuperGradients as an open-source solution for the entire AI community to benefit from. By offering developers better tools to build, optimize and deploy models, we help to simplify the entire deep learning lifecycle and enable developers to focus on what they do best- creating innovative AI solutions to solve the world’s most complex problems.” Emerges from Stealth, Introduces Computer Vision Intelligence Platform for Physical Security Industry emerged from stealth to introduce the first computer vision intelligence platform. The company has raised $52 million in venture funding led by a16z and launches with five of the largest US tech companies by market capitalization and multiple Fortune 500 customers across a variety of other industries. Already, large enterprises, schools, and museums use to secure property, people, and assets from today’s most harrowing physical security threats.

With physical security incidents on the rise amidst the COVID-19 pandemic, and physical security organizations left under-resourced, the launch of comes at a time of critical need across enterprises. Physical security today is primarily a reactive process in which accidents, break-ins, assaults, and beyond are identified and investigated only after they have occurred. Despite having thousands of cameras – if not more – deployed to surveil their property, enterprises have been relegated to a reactive posture. At the root of this issue is camera chaos – the flood of video streams security professionals are tasked with monitoring to no avail. With no realistic capability of ingesting and understanding the goings-on across their campuses, security teams are left ill-equipped to quickly and effectively respond to real-time threats. changes the stakes, processing and analyzing these video feeds at scale to automatically identify security incidents of any enterprise, or organization’s campus. In current production implementations, reduces false alarms by 99% while alerting customers to more than 200 dispatch-worthy incidents each week that once required significant human intervention.

“To date, video analytics solutions detect motion and identify objects but fail to improve security operations because they cannot understand the context of human behaviors in a scene, severely limiting the capabilities of these analytics to generate situational awareness. This poses a barrier to automation and has resulted in a purely reactive approach to physical security,” said Shikhar Shrestha, co-founder and CEO of “Today, we’re introducing computer vision intelligence to the world, unlocking near human-level visual perception with a context-aware understanding of scenarios. This technology allows organizations to identify potential incidents before they happen and dispatch security professionals in real time. Our breakthrough computer vision intelligence platform will forever change how security operation centers process and analyze video data, transforming their operations to prevent incidents before they happen.”

Datatron Offers Accelerated AI Model Deployment and AI Governance Program 

To kickstart 2022, Datatron is offering five companies the chance to take part in a hyper-accelerated program that guarantees their AI models will be deployed or governed in less than two weeks, the company announced.

The biggest problem in companies adopting AI is the lack of expertise to tackle the complexity of the machine learning development lifecycle. It’s common to hear even the largest enterprises take nine to 12 months to deploy an AI model once the data scientists have completed their models. According to IDC, an estimated 28% of AI/ML projects fail, in part due to lack of staff with the necessary experience. Furthermore, having a black-box AI running in production and making decisions that are hard to comprehend elicits fears and concerns on whether these AI models could make judgements that would have negative consequences for the enterprise. Therefore, enterprises need proper monitoring and governance to not only ensure the AI models are performing as intended, but also to gain trust in the effectiveness of AI initiatives.

This contest removes these roadblocks to AI success, and leverages the expertise of Datatron founder and CEO, Harish Doddi. As an early AI/ML pioneer, he worked on Snapchat’s highly profitable “stories” and Lyft’s “surge” model to use a proven playbook to profitability and deliver return-on-investment from AI/ML.

“We want to show once and for all how our solution, tested and proven by a Super Bowl sponsor, stands head and shoulders above any other MLOps solution in the market,” said Harish Doddi, CEO, Datatron. “Over the years, there have been a lot of solutions and tools in the marketplace that do not offer real business benefits. We are taking a stance to cut out a lot of the noise and confusion that prevents most companies from being successful with AI.”

Ceremorphic Exits Stealth Mode; Unveils Technology Plans to Deliver a New Architecture Specifically Designed for Reliable Performance Computing

Armed with more than 100 patents and leveraging multi-decade expertise in creating Industry leading silicon systems, Ceremorphic Inc. announced its plans to deliver a complete silicon system that provides the performance needed for next-generation applications such as AI model training, HPC, automotive processing, drug discovery, and metaverse processing. Designed in advanced silicon geometry (TSMC 5nm node), this new architecture was built from the ground up to solve today’s high-performance computing problems in reliability, security and energy consumption to serve all performance-demanding market segments.

Ceremorphic was founded in April 2020 by Industry-Veteran Dr. Venkat Mattela, the Founding CEO of Redpine Signals, which sold its wireless assets to Silicon Labs, Inc. in March 2020 for $308 million. Under his leadership, the team at Redpine Signals delivered breakthrough innovations and industry-first products that led to the development of an ultra-low power wireless solution that outperformed products from industry giants in the wireless space by as much as 26 times on energy consumption. Ceremorphic leverages its own patented multi-thread processor technology ThreadArch® combined with cutting-edge new technology developed by the silicon, algorithm and software engineers currently employed by Ceremorphic. This team is leveraging its deep expertise and patented technology to design an ultra-low power training supercomputing chip.

“Having developed many innovations in multi-thread processing, low energy network circuits, analog computing, quantum resistant security microarchitecture, and new device architectures beyond CMOS,  Ceremorphic is well on its way to accomplish our goals,” said Venkat Mattela, Founder and CEO of Ceremorphic. “The challenges this market faces with “reliable performance computing” cannot be solved with existing architectures, but rather needs a completely new architecture built specifically to provide reliability, security, energy efficiency, and scalability.”

Distributed Cloud Infrastructure Innovator Sync Computing Emerges from Stealth, Brings in $6.1 Million

Sync Computing, a deep tech, distributed cloud infrastructure company, came out of stealth mode, revealing its initial products, customer traction, and $6.1 million funding. Moore Strategic Ventures and National Grid Partners led the round, joining existing investor The Engine. Alongside active pilots with both public and private enterprise customers in SaaS, finance, and data sectors, the company recently was awarded a $1M contract from the Department of Defense for large, distributed workload optimization. Sync Computing is using the new capital to advance its leadership in the modern data infrastructure space and support further development of its accelerated data infrastructure engine and solution lines.

“With today’s constantly expanding use of large-scale cloud computing, we’ve seen companies who have only a dozen engineers responsible for managing up to 10,000 data pipelines per day – it’s physically impossible to optimize cloud infrastructure at such large scales – until now,” said Jeff Chou, co-founder and CEO of Sync Computing. “We’ve essentially converted large scale cloud infrastructure into a math problem, and then solve it in seconds. We are also excited to do our part in reducing the wasteful use of cloud resources and its impact on global carbon footprint. We are bullish on what 2022 will bring for us, for our customers, and for the cloud space.”

BrainChip Achieves Full Commercialization of its AKD1000 AIoT Chip with availability of Mini PCIe Boards in high volume

BrainChip Holdings Ltd (ASX: BRN, OTCQX: BRCHF, ADR: BCHPY), a leading provider of ultra-low power high performance artificial intelligence technology and the world’s first commercial producer of neuromorphic AI chips and IP, announced that it has begun taking orders for the first commercially available Mini PCIe board leveraging its Akida™ advanced neural networking processor, rounding out its suite of AKD1000 offerings.

The AKD1000-powered Mini PCIe boards can be plugged into a developer’s existing system to unlock capabilities for a wide array of edge AI applications, including Smart City, Smart Health, Smart Home and Smart Transportation. BrainChip will also offer the full PCIe design layout files and the bill of materials (BOM) to system integrators and developers to enable them to build their own boards and implement AKD1000 chips in volume as a stand-alone embedded accelerator or as a co-processor.

The new boards help usher in a new era of AI at the edge due to their performance, security, low power requirements, and the ability to perform AI training and learning on the device itself, without dependency on the cloud. The production-ready chips provide high-speed neuromorphic processing of sensor data at a low cost, high speed and very low power consumption.
The PCIe boards are immediately available for pre-order on the BrainChip website. Pricing starts at $499.

“I am excited that people will finally be able to enjoy a world where AI meets the Internet of Things,” said Sean Hehir, BrainChip CEO. “We have been working on developing our Akida technology for more than a decade and with the full commercial availability of our AKD1000, we are ready to fully execute on our vision. Other technologies are simply not capable of the autonomous, incremental learning at ultra-low power consumption that BrainChip’s solutions can provide. Getting these chips into as many hands as possible is how the next generation of AI becomes reality.”

Radar and Computer Vision Solution for Retail Environments Now Rolling Out from Ulisse; Transforms Physical Spaces into Intelligent Places

Ulisse, a new physical space analytics platform for connected spaces, is launching the only radar and computer vision-based IoT AI platform to help retailers turn their spaces into intelligent places to improve overall experiences and results. Also appropriate for office buildings and cities, pedestrian areas and public transportation, the camera-free platform includes self-installing sensors and algorithms to quickly adapt to every situation and provide real-time analytics, delivering highly accurate data while maintaining privacy.

Rather than cameras which invade privacy (capturing faces and identities) and require stable lighting, Ulisse incorporates a unique radar-based approach which increases the overall accuracy of the analytics and reduces the required density of sensors in the physical environment. This also makes the entire solution less expensive. The Ulisse RF (radio frequency)-based sensors–which act like radar–illuminate the target with reflection point-clouds, not a true color image, resulting in unobtrusive and privacy-friendly technology.

“As people begin to emerge post-pandemic and seek experiences outside of their homes, it will be obvious that the real estate industry has not fully understood people’s experiences and behaviors,” said Luca Nestola, CEO, Ulisse. “Retailers can use data about how people interact so they understand how best to design physical spaces and make them most efficient. Human experience is a crucial aspect to create the best designs. Ulisse does that through its technology and analytics.” 

Lucidworks Solves the Next Generation of Search with New SaaS Platform, Springboard, and Launches the First Publicly Available Application

Lucidworks, the provider of next-generation AI-powered search applications and pioneer of the Connected Experience Cloud, announced a new SaaS platform, Springboard, and a roadmap for new applications and updates to Fusion. Connected Search is the first application now publicly available on Springboard. This application makes site search easy for customers who want highly relevant, cost-effective, on-site search at scale with no operational burden. Lucidworks built Springboard to answer the market need for a cloud-native search platform that is scalable, easy for any business user to set-up and manage, and outcomes-driven.

Springboard is a multi-tenant SaaS platform that powers applications for search, browse, and discovery. Applications built on Springboard improve time-to-value, simplify maintenance, and deliver relevancy out-of-the-box so customers can use search to solve their most challenging business problems. Springboard is the only platform that offers outcome-optimized solutions for non-technical users who are deploying search to improve interactions in the customer lifecycle.

“Lucidworks has been solving complex search challenges for the past decade, and Springboard is our answer to what the market and our customers need in today’s digital-first environment,” said Will Hayes, CEO, Lucidworks. “The Springboard design philosophy is that high-quality search at scale should be easy for anyone to deploy and cost efficient. Customer feedback drove our roadmap of applications for specific solutions, including our first publicly available application, Connected Search. We’re making it easy for customers to create connections between people, capture and understand signals that show preference and intent, and improve the total experience for customers, service agents, and employees without requiring search or development experience.”

Ambarella Announces Breakthrough AI-Based Image Signal Processing

Ambarella, Inc. (NASDAQ: AMBA), an AI vision silicon company, announced its new Artificial Intelligence Image Signal Processor (AISP). Drawing on its 17 years of experience in ISP processing and best-in-class CVflow® AI engine, Ambarella’s new AI based ISP architecture uses neural networks to augment the image processing done by the hardware ISP integrated into its SoCs. This approach enables color imaging with low light at very low lux levels and minimal noise, a 10 to 100X improvement over state-of-the-art traditional ISPs, and new levels of high dynamic range (HDR) processing with more natural color reproduction and higher dynamic range.

“Being able to see clearly in low-light or high-contrast conditions is key to robust camera systems,” said Les Kohn, Ambarella’s CTO and co-founder. “Traditional camera systems have had to live with noisy or dark black and white video in low-light conditions, and dark shadows or blown-out highlights in high contrast conditions. Both of these cases result in the loss of details that are detrimental to both human viewing and AI applications. With our new AISP technology, we increase the useful range of camera systems while reducing the total system cost to build high quality cameras.”

Provectus Unveils the Redesign of UI for Apache Kafka

Provectus, a Silicon Valley artificial intelligence (AI) consultancy, released the redesign of UI for Apache Kafka service as a part of its v0.3 update. The update also includes optimizations of the product’s performance and security, and presents new features, such as LDAP authentication and Docker ARM & Apple M1 support.

The clean UI of the tool makes it easy to keep track of such metrics as Brokers, Topics, Partitions, Production, and Consumption, without having to use additional CLI tools. Thanks to UI for Apache Kafka, developers enjoy faster reporting, reduced time to resolution and minimal engineering silos, making the development process more efficient. 

“The Provectus team is excited to launch the redesigned version of our UI,” says German Osin, Chief Product Owner of UI for Apache Kafka. “In this release, our goal was to provide the best possible user experience while enhancing the functionality and performance of the service. I am inviting visitors to explore the new UI, designed to provide the ultimate user-friendly experience while retaining all the critical features for monitoring and management of Apache Kafka clusters.”

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