One of the most exciting fields to be in right now is machine learning. But starting your journey there can be quite intimidating at first. With the internet containing so much information, the amount of content can be overwhelming for someone, especially at the initial stages of learning. Getting access to the right kind of resources when one is starting out sets the foundation right for growing in the domain.

Here is the list of books that you should read as a beginner just starting out in machine learning:

This is a good book as an introductory text to machine learning. It teaches you how to download data sets and what kind of tools and ML libraries one needs. It introduces you to data scrubbing techniques, including one-hot encoding, binning and dealing with missing data, preparing data for analysis, including k-fold validation, regression analysis to create trend lines, and clustering. The book also contains the basics of neural networks, decision trees, and bias/variance. It does not require prior coding experience to understand the concepts of the book.

This book is written by two data scientists and introduces anyone who wants to use machine learning techniques for practical tasks. It makes the reader understand the meaning of programming languages and the tools needed to make ML-based turns work in reality. It also helps comprehend how daily activities are powered by machine learning and introduces R and Python to perform pattern-oriented tasks and data analysis.

Even if one uses Python as a beginner, the book will help the reader build machine learning solutions. The reader will learn about the basic concepts and applications, the advantages and pitfalls of popularly used machine learning algorithms, and how to represent data processed by machine learning. This will include which aspects of data to focus on, advanced methods for model evaluation and parameter tuning, pipelines for chaining models and encapsulating the workflow and methods for working with text data, including text-specific processing techniques and suggestions for improving your machine learning and data science skills.

This book is a good start for newcomers to machine learning. It contains topics starting with ML basics, classifying with k-nearest neighbours, splitting datasets one feature at a time, decision trees, logistic regression, tree-based regression, using principal component analysis to simplify data, simplifying data with the singular value decomposition and big data and MapReduce. Most of the examples use Python; hence, familiarity in Python will be desirable.

The book is for developers and does not use academic language but takes the reader through techniques used in daily work. It contains examples in Python that bring out the core algorithms of statistical data processing, data analysis, and data visualization in code that one can reuse.

It is a popular choice among machine learning enthusiasts. A newbie in machine learning will find this book comfortable to comprehend, setting the scene for their machine learning journey. Experienced people will use this book as a collection of pointers to the directions of further self-improvement. It comes with a wiki that contains pages that extend some book chapters with additional information, Q&A, code snippets, further reading, tools, etc.

This book gives a good start to someone interested in the field of statistical learning. It includes topics like linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, and clustering while citing real-world examples. Each chapter contains a tutorial on implementing the analyses and methods shown in R. It is a combined work of a group of authors with experience teaching machine learning and working with predictive analysis.

If you desire to enter the exciting field of machine learning and build algorithms, these books can act as a stepping stone in your journey.

Sreejani Bhattacharyya is a journalist with a postgraduate degree in economics. When not writing, she is found reading on geopolitics, economy and philosophy. She can be reached at [emailprotected]

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Books To Read To Start Your ML Journey - Analytics India Magazine

A recent study in Nature Machine Intelligence by researchers at Carnegie Mellon sought to investigate the impact that mitigating bias in machine learning has on accuracy.

Despite what researchers referred to as a "commonly held assumption" that reducing disparities requires either accepting a drop in accuracy or developing new, complex methods, they found that the trade-offs between fairness and effectiveness can be "negligible in practice."

"You actually can get both. You don't have to sacrifice accuracy to build systems that are fair and equitable," said Rayid Ghani, a CMU computer science professor and an author on the study, in a statement.

At the same time, Ghani noted, "It does require you to deliberately design systems to be fair and equitable. Off-the-shelf systems won't work."

WHY IT MATTERS

Ghani, along with CMU colleagues Kit Rodolfa and Hemank Lamba, focused on the use of machine learning in public policy contexts specifically with regard to benefit allocation in education, mental health, criminal justice and housing safety programs.

The team found that models optimized for accuracy could predict outcomes of interest, but showed disparities when it came to intervention recommendations.

But when they adjusted the outputs of the models with an eye toward improving their fairness, they discovered that disparities based on race, age or income depending on the situation could be successfully removed.

In other words, by defining the fairness goal upfront in the machine learning process and making design choices to achieve that goal, they could address slanted outcomes without sacrificing accuracy.

"In practice, straightforward approaches such as thoughtful label choice, model design or post-modelling mitigation can effectively reduce biases in many machine learning systems," read the study.

Researchersnotedthat a wide variety of fairness metrics exists, depending on the context, and a broader exploration of the fairness-accuracy trade-offs is warranted especially when stakeholders may want to balance multiple metrics.

"Likewise, it may be possible that there is a tension between improving fairness across different attributes (for example, sex and race) or at the intersection of attributes," read the study.

"Future work should also extend these results to explore the impact not only on equity in decision-making, but also equity in longer-term outcomes and implications in a legal context," it continued.

The researchers noted that fairness in machine learning goes beyond the models predictions; it also includes how those predictions are acted on by human decision makers.

"The broader context in which the model operates must also be considered, in terms of the historical, cultural and structural sources of inequities that society as a whole must strive to overcome through the ongoing process of remaking itself to better reflect its highest ideals of justice and equity," they wrote.

THE LARGER TREND

Experts and advocates have sought to shine a light on the ways that bias in artificial intelligence and ML can play out in a healthcare setting. For instance, a study this past August found that under-developed models may worsen COVID-19 health disparities for people of color.

And as Chris Hemphill, VP of applied AI and growth at Actium Health, told Healthcare IT News this past month, even innocuous-seeming data can reproduce bias.

"Anything you're using to evaluate need, or any clinical measure you're using, could reflect bias," Hemphill said.

ON THE RECORD

"We hope that this work will inspire researchers, policymakers and data science practitioners alike to explicitly consider fairness as a goal and take steps, such as those proposed here, in their work that can collectively contribute to bending the long arc of history towards a more just and equitable society," said the CMU researchers.

Kat Jercich is senior editor of Healthcare IT News.Twitter: @kjercichEmail: kjercich@himss.orgHealthcare IT News is a HIMSS Media publication.

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Attendees will gain insights into the vital role human intelligence plays in developing machine learning data operations and AI data solutions

Published: Oct. 21, 2021 at 1:00 PM EDT

LOS GATOS, Calif., Oct. 21, 2021 /PRNewswire/ --iMerit, a leading AI data solutions company, today announced its inaugural conference, the iMerit ML DataOps Summit, which is a live virtual event taking place on December 2, 2021 at 9 a.m. PDT. Hosted in partnership with TechCrunch, the iMerit ML DataOps Summit will bring together innovators at the forefront of data operations, machine learning, and artificial intelligence. Register here.

Attendees will gain insights on the importance of leveraging human intelligence to advance AI, how to solve edge cases with high quality data, scaling data pipelines for rapid deployment and more. Through engaging keynotes, panels, and fireside chats, participants will hear the challenges and opportunities of machine learning data operations trending across a variety of industries, including autonomous mobility, medical AI, geospatial, technology, and more.

Some of this year's featured speakers include:

"A staggering number of companies have accelerated their AI adoption initiatives, with many incorporating AI as a mainstream technology within their business," said Radha Basu, CEO and Founder of iMerit. "As a leader in end-to-end AI data solutions, iMerit looks forward to gathering the top minds in artificial intelligence to discuss strategies around machine learning data operations and unveiling why leveraging human intelligence is the critical path to advancing AI."

Accelerated by COVID-19, digital innovation has put AI and analytics at the forefront of many business operations. The iMerit ML DataOps Summit will provide insights on how businesses can find efficient methods, tools, processes and principles to prepare the data needed to conquer AI at the edge.

"We're excited to host this conference in partnership with iMerit," said Joey Hinson, Senior Director of Operations at TechCrunch. "This dynamic speaker panel will deliver the compelling discussions around AI and machine learning that our audience expects."

Additionally, the iMerit ML DataOps Summit will host a virtual expo showcasing data annotation and automation tool providers that are building the future of ML DataOps.

For more information or to register for the free virtual event, click here.

About iMeritiMerit is a leading AI data solutions companyproviding high quality data across computer vision, natural language processing and content services that powers machine learning and artificial intelligence applications for large enterprises. iMerit provides end-to-end data labeling services to Fortune 500 companies in a wide array of industries including agricultural AI, autonomous vehicles, commerce, geospatial, government, financial services, medical AI and technology. iMerit employs more than 5,000 full-time data annotation experts in Bhutan, Europe, India and the United States. Raising $23.5 million in funding to date, iMerit investors are CDC Group, Khosla Impact, Michael and Susan Dell Foundation and Omidyar Network. For more information, visit imerit.net.

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Join our free online course Introduction to Machine Learning and AI to discover the fundamentals of machine learning and learn to train your own machine learning models using free online tools.

Although artificial intelligence (AI) was once the province of science fiction, these days youre very likely to hear the term in relation to new technologies, whether thats facial recognition, medical diagnostic tools, or self-driving cars, which use AI systems to make decisions or predictions.

By the end of this free, online, self-paced course, you will have an appreciation for what goes into machine learning and artificial intelligence systems and why you should think carefully about what comes out.

Youll also often hear about AI systems that use machine learning (ML). Very simply, we can say that programs created using ML are trained on large collections of data to learn to produce more accurate outputs over time. One rather funny application you might have heard of is the muffin or chihuahua? image recognition task.

More precisely, we would say that a ML algorithm builds a model, based on large collections of data (the training data), without being explicitly programmed to do so. The model is finished when it makes predictions or decisions with an acceptable level of accuracy. (For example, it rarely mistakes a muffin for a chihuahua in a photo.) It is then considered to be able to make predictions or decisions using new data in the real world.

But how does all this actually work? If you dont know, its hard to judge what the impacts of these technologies might be, and how we can be sure they benefit everyone an important discussion that needs to involve people from across all of society. Not knowing can also be a barrier to using AI, whether thats for a hobby, as part of your job, or to help your community solve a problem.

For teachers and educators its particularly important to have a good foundational knowledge of AI and ML, as they need to teach their learners what the young people need to know about these technologies and how they impact their lives. (Weve also got a free seminar series about teaching these topics.)

To help you understand the fundamentals of AI and ML, weve put together a free online course: Introduction to Machine Learning and AI. Over four weeks in two hours per week, learning at your own pace, youll find out how machine learning can be used to solve problems, without going too deeply into the mathematical details. Youll also get to grips with the different ways that machines learn, and you will try out online tools such as Machine Learning for Kids and Teachable Machine to design and train your own machine learning programs.

As well as finding out how these AI systems work, youll look at the different types of tasks that they can help us address. One of these is classification working out which group (or groups) something fits in, such as distinguishing between positive and negative product reviews, identifying an animal (or a muffin) in an image, or spotting potential medical problems in patient data.

Youll also learn about other types of tasks ML programs are used for, such as regression (predicting a numerical value from a continuous range) and knowledge organisation (spotting links between different pieces of data or clusters of similar data). Towards the end of the course youll dive into one of the hottest topics in AI today: neural networks, which are ML models whose design is inspired by networks of brain cells (neurons).

Before an ML program can be trained, you need to collect data to train it with. During the self-paced course youll see how tools from statistics and data science are important for ML but also how ethical issues can arise both when data is collected and when the outputs of an ML program are used.

By the end of the course, you will have an appreciation for what goes into machine learning and artificial intelligence systems and why you should think carefully about what comes out.

The Introduction to Machine Learning and AI course is open for you to sign up to now. Sign-ups will pause after 12 December. Once you sign up, youll have access for six weeks. During this time youll be able to interact with your fellow learners, and before 25 October, youll also benefit from the support of our expert facilitators. So what are you waiting for?

As part of our research on computing education, we would like to find out about educators views on machine learning. Before you start the course, we will ask you to complete a short survey. As a thank you for helping us with our research, you will be offered the chance to take part in a prize draw for a 50 book token!

To develop your computing knowledge and skills, you might also want to:

If you are a teacher in England, you can develop your teaching skills through the National Centre for Computing Education, which will give you free upgrades for our courses (including Introduction to Machine Learning and AI) so youll receive certificates and unlimited access.

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Learn the fundamentals of AI and machine learning with our free online course - Blogdottv

On this four-week course from the Raspberry Pi Foundation, youll learn about different types of machine learning, and use online tools to train your own AI models. Youll delve into the problems that ML can help to solve, discuss how AI is changing the world, and think about the ethics of collecting data to train a ML model. For teachers and educators its particularly important to have a good foundational knowledge of AI and ML, as they need to teach their learners what the young people need to know about these technologies and how they impact their lives. (Weve also got a free seminar series about teaching these topics.)

The first week of this course will guide you through how you can use machine learning to label data, whether to work out if a comment is positive or negative or to identify the contents of an image. Then youll look at algorithms that create models to give a numerical output, such as predicting house prices based on information about the house and its surroundings. Youll also explore other types of machine learning that are designed to discover connections and groupings in data that humans would likely miss, giving you a deeper understanding of how it can be used.

To register for the course for free jump over to the official course page by following the link below.

Source : RPiF

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Learn about machine learning and the fundamentals of AI with free Raspberry Pi course - Geeky Gadgets

Weve all been in situations where we had to make tough ethical decisions. Why not dodge that pesky responsibility by outsourcing the choice to a machine learning algorithm?

Thats the idea behind Ask Delphi, a machine-learning model from the Allen Institute for AI. You type in a situation (like donating to charity) or a question (is it okay to cheat on my spouse?), click Ponder, and in a few seconds Delphi will give you, well, ethical guidance.

The project launched last week, and has subsequently gone viral online for seemingly all the wrong reasons. Much of the advice and judgements its given have been fraught, to say the least.

For example, when a user asked Delphi what it thought about a white man walking towards you at night, it responded Its okay.

But when they asked what the AI thought about a black man walking towards you at night its answer was clearly racist.

The issues were especially glaring in the beginning of its launch.

For instance, Ask Delphi initially included a tool that allowed users to compare whether situations were more or less morally acceptable than another resulting in some really awful, bigoted judgments.

Besides, after playing around with Delphi for a while, youll eventually find that its easy to game the AI to get pretty much whatever ethical judgement you want by fiddling around with the phrasing until it gives you the answer you want.

So yeah. Its actually completely fine to crank Twerkulator at 3am even if your roommate has an early shift tomorrow as long as it makes you happy.

It also spits out some judgments that are complete head scratchers. Heres one that we did where Delphi seems to condone war crimes.

Machine learning systems are notorious for demonstrating unintended bias. And as is often the case, part of the reason Delphis answers can get questionable can likely be linked back to how it was created.

The folks behind the project drew on some eyebrow-raising sources to help train the AI, including the Am I the Asshole? subreddit, the Confessions subreddit, and the Dear Abby advice column, according to the paper the team behind Delphi published about the experiment.

It should be noted, though, that just thesituations were culled from those sources not the actual replies and answers themselves. For example, a scenario such as chewing gum on the bus might have been taken from a Dear Abby column. But the team behind Delphi used Amazons crowdsourcing service MechanicalTurk to find respondents to actually train the AI.

While it might just seem like another oddball online project, some experts believe that it might actually be causing more harm than good.

After all, the ostensible goal of Delphi and bots like it is to create an AI sophisticated enough to make ethical judgements, and potentially turn them into moral authorities. Making a computer an arbiter of moral judgement is uncomfortable enough on its own, but even its current less-refined state can have some harmful effects.

The authors did a lot of cataloging of possible biases in the paper, which is commendable, but once it was released, people on Twitter were very quick to find judgments that the algorithm made that seem quite morally abhorrent, Dr. Brett Karlan, a postdoctoral fellow researching cognitive science and AI at the University of Pittsburgh (and friend of this reporter), told Futurism. When youre not just dealing with understanding words, but youre putting it in moral language, its much more risky, since people might take what you say as coming from some sort of authority.

Karlan believes that the papers focus on natural language processing is ultimately interesting and worthwhile. Its ethical component,he said, makes it societally fraught in a way that means we have to be way more careful with it in my opinion.

Though the Delphi website does include a disclaimer saying that its currently in its beta phase and shouldnt be used for advice, or to aid in social understanding of humans, the reality is that many users wont understand the context behind the project, especially if they just stumbled onto it.

Even if you put all of these disclaimers on it, people are going to see Delphi says X and, not being literate in AI, think that statement has moral authority to it, Karlan said.

And, at the end of the day, it doesnt. Its just an experiment and the creators behind Delphi want you to know that.

It is important to understand that Delphi is not built to give people advice, Liwei Jiang, PhD student at the Paul G. Allen School of Computer Science & Engineering and co-author of the study, told Futurism. It is a research prototype meant to investigate the broader scientific questions of how AI systems can be made to understand social norms and ethics.

Jiang added the goal with the current beta version of Delphi is actually to showcase the reasoning differences between humans and bots. The team wants to highlight the wide gap between the moral reasoning capabilities of machines and humans,Jiang added, and to explore the promises and limitations of machine ethics and norms at the current stage.

Perhaps one of the most uncomfortable aspects about Delphi and bots like it is the fact that its ultimately a reflection of our own ethics and morals, with Jiang adding that it is somewhat prone to the biases of our time. One of the latest disclaimers added to the website even says that the AI simply guesses what an average American might think of a given situation.

After all, the model didnt learn its judgments on its own out of nowhere. It came from people online, who sometimes do believe abhorrent things. But when this dark mirror is held up to our faces, we jump away because we dont like whats reflected back.

For now, Delphi exists as an intriguing, problematic, and scary exploration. If we ever get to the point where computers are able to make unequivocal ethical judgements for us, though, we hope that it comes up with something better than this.

Follow Tony Tran on Twitter.

More on AI: Scientists Use AI, 3D Printing to Uncover Hidden Picasso Painting

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TRI Publishes Six Research Papers Pushing Boundaries of Scalable Learning at the Premier International Conference on Computer Vision

LOS ALTOS, Calif., Oct. 11, 2021 /PRNewswire/ -- Today, the Toyota Research Institute (TRI) announced the acceptance of six research papers in the field of machine learning at the International Conference on Computer Vision (ICCV). The research advances understanding across various tasks crucial for robotic perception, including semantic segmentation, 3D object detection and multi-object tracking.

TRIs research on multi-object tracking reveals that synthetic data could endow machines with fundamental human cognitive abilities, like object permanence, that are historically hard for machine learning models but second nature for humans.

Over the last six years, TRI's researchers have made significant strides in robotics, automated driving and materials science in large part due to machine learning the application of computer algorithms that constantly improve with experience and data.

"Machine learning is the foundation of our research," said Dr. Gill Pratt, CEO of TRI. "We are working to create scientific breakthroughs in the discipline of machine learning itself and then apply those breakthroughs to accelerate discoveries in robotics, automated driving, and battery testing and development."

As the International Conference on Computer Vision (ICCV) started, TRI shared six papers demonstrating TRI's robust research in machine learning, including geometric deep learning for 3D vision, self-supervised learning and simulation to real or "sim-to-real" transfer.

"Within the field of machine learning, scalable supervision is our focus," said Adrien Gaidon, head of TRI's Machine Learning team. "It is impossible to manually label everything you need at Toyota's scale, yet this is the state-of-the-art approach, especially for Deep Learning and Computer Vision. Thankfully, we can leverage Toyota's domain expertise in vehicles, robots or batteries to invent alternative forms of scalable supervision, whether via simulation or self-supervised learning from raw data. This approach can boost performance in a wide array of tasks important for automated cars to be safer everywhere anytime, robots to learn faster and battery development to speed up lengthy testing cycles."

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In the six papers accepted at ICCV, TRI researchers report several key findings. Notably, they show that geometric self-supervised learning significantly improves sim-to-real transfer for scene understanding. The resulting unsupervised domain adaptation algorithm enables recognizing real-world categories without requiring any expensive manual real-world labels.

In addition, TRI's research on multi-object tracking reveals that synthetic data could endow machines with fundamental human cognitive abilities, like object permanence, that are historically hard for machine learning models but second nature for humans. This new development increases the robustness of computer vision algorithms, making them more aligned with people's visual common sense.

Finally, TRI's research on pseudo-lidar shows that large-scale self-supervised pre-training considerably boosts performance of image-based 3D object detectors. The proposed geometric pre-training enables training powerful 3D Deep Learning models from limited 3D labels, which are expensive or sometimes impossible to get from images alone.

You can learn more about all six papers and TRI's machine learning work on TRI's Medium page or attend TRI's presentations at ICCV.

About Toyota Research Institute Toyota Research Institute (TRI) conducts research to advance robotics, energy and materials, machine learning, and human-centered artificial intelligence. Led by Dr. Gill Pratt, TRI's team of world-class researchers are developing technologies to amplify human ability, focused on making our lives safer and more sustainable. Established in 2015, TRI has offices in Los Altos, California; Cambridge, Massachusetts; and Ann Arbor, Michigan. For more information about TRI, please visit http://tri.global.

Media Contact:Wendy RosenDirector of CommunicationsToyota Research InstituteWendy.Rosen@tri.global

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TEL AVIV, Israel, Oct. 11, 2021 /PRNewswire/ -- Cybersixgill today announced its Investigative Portal and Darkfeed have been recognized as the best machine learning autonomous solution by the 2021 Tech Ascension Awards.

Cybersixgill Logo

Cybersixgill autonomous threat intelligence solutions provide real-time contextual intelligence and the necessary insight into the nature and source of each threat. Analysts can leverage the best-in-market data collection of hundreds of millions of intelligence items from the deep, dark and clear web, including historical data dating back to the 90s, deleted posts, invite-only messaging groups, and millions of threat actors.

With custom alerting and monitoring tailored to each organization's assets and needs, Cybersixgill eliminates the information overload - empowering security teams by delivering relevant and actionable intel to create faster security processes, break organizational silos, reduce operational costs while increasing return on security investment.

The Tech Ascension Awards recognized the very best innovations in cybersecurity. The Tech Ascension awards judged over 500 cybersecurity applicants based on technology innovation, market research, and competitive differentiators. The class-leading vendors that received recognition from the Tech Ascension Awards showcased technology that solves critical industry challenges and produces invaluable business outcomes for their customers.

"The only way cybersecurity can stay ahead of the threat curve is by leveraging autonomous technology that can deliver relevant intelligence in real time." said Sharon Wagner, CEO, Cybersixgill. "By understanding threat actors' network, expertise, and motivations, teams can build a complete intelligence picture and defend against data leaks, fraud, ransomware attacks."

"The proliferation of ransomware, nation-state threats, and an uptick in cybercriminal activity due to COVID-19 are just some of the factors that have made a strong cybersecurity defense paramount for every business that touches sensitive data," said David Campbell, CEO, Tech Ascension Awards. "We're honored to recognize these industry leaders that have demonstrated their ability to defend organizations with unique approaches, innovative technology, and world-class talent."

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To learn more about Cybersixgill, please visit http://www.cybersixgill.com and follow us on LinkedIn.

For more information about the Tech Ascension Awards, please visit http://www.techascensionawards.com

About Cybersixgill

Cybersixgill's fully automated threat intelligence solutions help organizations fight cyber crime, detect phishing, data leaks, fraud and vulnerabilities as well as amplify incident response in real-time. The Cybersixgill Investigative Portal empowers security teams with contextual and actionable insights as well as the ability to conduct real-time investigations. Rich data feeds such as Darkfeed and CVE insights from DVE Score harness Cybersixgill's unmatched intelligence collection capabilities and deliver real-time intel into organizations' existing security systems. Most recently, Cybersixgill introduced agility to threat intel with their CI/CP methodology (Continuous Investigation/Continuous Protection). Current customers include enterprises, financial services, MSSPs, government and law enforcement entities.

About the Tech Ascension Awards

The Tech Ascension Awards elevate companies that possess cutting-edge, innovative technology that solve critical challenges in their respective markets. Tech Ascension winners rise above the crowded consumer and enterprise technology industries and receive validation from an independent organization. Applicants are judged based on technology innovation and uniqueness, market research (analyst reports, media coverage, customer case studies), hard performance stats, and competitive differentiators. The awards recognize leaders in cybersecurity, DevOps, big data and consumer technology. For information about the Tech Ascension Awards, please visit http://www.techascensionawards.com.

Media contact: Laurie Ben-HaimCybersixgill+1-646-300-9549+972-52-7831911laurie@cybersixgill.com

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LOS ANGELES, Oct. 11, 2021 /PRNewswire/ -- KubeCon + CloudNative ConBooth # SU41 -Zebrium, the leader in using machine learning on logs to automatically find the root cause of software problems, today announced a major new release of its SaaS solution and, for the first time, afullyon-premises edition catering to organizations with the most stringentsecurity requirements. The on-premises solution ensuressensitivelog data always remainswithin a company'sprivate network.

The new SaaS edition includes a completely redesigned user interface and delivers a 10x performanceimprovement for finding theroot causein logs. The on-premises editionis packaged as a Kubernetes-deployed application and can be installed with just a single Helm command, making it easy to install, upgrade and manage. It includes open-APIsforeasyintegrationinto existing tools and workflows andis designed to scaleto meet the needs of the largest enterprise customers.It is based on the same proven Zebrium machine learning technology that is deployed in customerenvironments around the world.

Today's distributed applications generate huge volumes of software logs collected frommany different applications and microservices. Some of the log streams can intentionally or inadvertently contain sensitive PersonallyIdentifiable Information (PII), such as names, addresses and even credit card numbersor other details. For this reason, some companies, particularly those in regulated industries or in certain geographical locations,are not able to send log data to the cloud.The new on-premises edition satisfies their requirementsby keepingall logdatawithin an organization's own network.

"Quickly and accurately resolving application failures,or preventing them in the first place,is a top goal forall organizations," said Ajay Singh, CEO, Zebrium. "With this release, any type of company can achieve this goal, by deploying a SaaS solutionor an on-premises version wherethere is a strictrequirement to keep log data within a private network."

Newfeaturesand enhancements in SaaS and on-premises editions:

The new on-premisesand improved SaaS editionsare now in general availability.

For more information about Zebrium, please contact [emailprotected], visit the websiteor stop by Booth #SU41 at KubeCon + CloudNative Con at the Los Angeles Convention Center fromOctober 13to 15.

Media Contact:Kira WojackMerritt Public Relations[emailprotected] (415) 419-4062

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Zebrium Releases New SaaS and On-Premises Editions that use Machine Learning to Quickly and Accurately Find the Root Cause of Software Problems -...

ScaleOut Software is adding major extensions to its ScaleOut Digital Twin Streaming Service that enable real-time digital twin software to implement and host machine learning and statistical analysis algorithms.

Real-time digital twins can now make extensive use of Microsofts ML.NET machine learning library to implement these groundbreaking capabilities for virtually any IoT device or source object.

Integration of machine learning with real-time digital twins offers powerful new options for real-time monitoring across a wide variety of applications, according to the vendor.

For example, cloud-based real-time digital twins can track a fleet of trucks to identify subtle changes in key engine parameters with predictive analytics that avoid costly failures. Security monitors tracking perimeter entrances and sound sensors can use machine learning techniques to automatically identify unexpected behaviors and generate alerts.

By harnessing the no-code ScaleOut Model Development Tool, a real-time digital twin can easily be enhanced to automatically analyze incoming telemetry messages using machine learning techniques.

Machine learning provides important real-time insights that enhance situational awareness and enable fast, effective responses.

The tool provides three configuration options for analyzing numeric parameters contained within incoming messages to spot issues as they arise:

Once configured through the ScaleOut Model Development Tool, the ML algorithms run automatically and independently for each data source within their corresponding real-time digital twins as incoming messages are received.

Each real-time digital twin can automatically capture anomalous events for follow-up analysis and generate alerts to popular alerting providers, such as Splunk, Slack, and Pager Duty, to support remediation by service or security teams.

We are excited to offer powerful machine learning capabilities for real-time digital twins that will make it even easier to immediately spot issues or identify opportunities across a large population of data sources, said Dr. William Bain, ScaleOut Softwares CEO and founder. ScaleOut Software has built the next step in the evolution of the Microsoft Azure IoT and ML.NET ecosystem, and we look forward to helping our customers harness these technologies to enhance their real-time monitoring and streaming analytics.

For more information about this news, visit?www.scaleoutsoftware.com.

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