Machine learning algorithms developed by researchers can predict the presence of any nanoparticle in most plant species.

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While crop yield has achieved a substantial boost from nanotechnology in recent years, alarms over the health risks posed by nanoparticles within fresh produce and grains have also increased. In particular, nanoparticles entering the soil through irrigation, fertilizers and other sources have raised concerns about whether plants absorb these minute particles enough to cause toxicity.

In a new study published online in the journalEnvironmental Science and Technology,researchers at Texas A&M University have used machine learning to evaluate the salient properties of metallic nanoparticles that make them more susceptible for plant uptake. The researchers said their algorithm could indicate how much plants accumulate nanoparticles in their roots and shoots.

Nanoparticles are a burgeoning trend in several fields, including medicine, consumer products and agriculture. Depending on the type of nanoparticle, some have favorable surface properties, charge and magnetism, among other features. These qualities make them ideal for a number of applications. For example, in agriculture, nanoparticles may be used as antimicrobials to protect plants from pathogens. Alternatively, they can be used to bind to fertilizers or insecticides and then programmed for slow release to increase plant absorption.

These agricultural practices and others, like irrigation, can cause nanoparticles to accumulate in the soil. However, with the different types of nanoparticles that could exist in the ground and a staggeringly large number of terrestrial plant species, including food crops, it is not clearly known if certain properties of nanoparticles make them more likely to be absorbed by some plant species than others.

As you can imagine, if we have to test the presence of each nanoparticle for every plant species, it is a huge number of experiments, which is very time-consuming and expensive, said Xingmao Samuel Ma, associate professor in the Zachry Department of Civil and Environmental Engineering. To give you an idea, silver nanoparticles alone can have hundreds of different sizes, shapes and surface coatings, and so, experimentally testing each one, even for a single plant species, is impractical.

Instead, for their study, the researchers chose two different machine learning algorithms, an artificial neural network and gene-expression programming. They first trained these algorithms on a database created from past research on different metallic nanoparticles and the specific plants in which they accumulated. In particular, their database contained the size, shape and other characteristics of different nanoparticles, along with information on how much of these particles were absorbed from soil or nutrient-enriched water into the plant body.

Once trained, their machine learning algorithms could correctly predict the likelihood of a given metallic nanoparticle to accumulate in a plant species. Also, their algorithms revealed that when plants are in a nutrient-enriched or hydroponic solution, the chemical makeup of the metallic nanoparticle determines the propensity of accumulation in the roots and shoots. But if plants are grown in soil, the contents of organic matter and the clay in soil are key to nanoparticle uptake.

Ma said that while the machine learning algorithms could make predictions for most food crops and terrestrial plants, they might not yet be ready for aquatic plants. He also noted that the next step in his research would be to investigate if the machine learning algorithms could predict nanoparticle uptake from leaves rather than through the roots.

It is quiteunderstandable that people are concerned about the presence of nanoparticles in their fruits, vegetables and grains, said Ma. But instead of not using nanotechnology altogether, we would like farmers to reap the many benefits provided by this technology but avoid the potential food safety concerns.

Other contributors include Xiaoxuan Wang, Liwei Liu and Weilan Zhang from the civil and environmental engineering department.

This research is partly funded by the National Science Foundation and the Ministry of Science and Technology, Taiwan under the Graduate Students Study Abroad Program.

Originally posted here:
Machine Learning Can Reduce Worry About Nanoparticles In Food - Texas A&M Today - Texas A&M University Today

Table of contents:

What Is MLOps + How Does It Work?Why Do You Need MLOps?What Problems Does MLOps Solve?How Do You Implement MLOps In Your Organization?How Do I Learn MLOps?Want to Learn Even More About MLOps?

Machine learning operations, or MLOps, is the term given to the process of creating, deploying, and maintaining machine learning models. It's a discipline that combines machine learning, DevOps, and data engineering with the goal of finding faster, simpler, and more effective ways to productize machine learning. When done right, MLOps can help organizations align their models with their unique business needs, as well as regulatory requirements. Keep reading to find out how you can implement MLOps with your team.

What Is MLOps + How Does It Work?

A typical MLOps process looks like this: a business goal is defined, the relevant data is collected and cleaned, and then a machine learning model is built and deployed. Or maybe we should say that's what a typical MLOps process is supposed to look like, but many organizations are struggling to get it down.

Productizing machine learning, or ML, is one of the biggest challenges in AI practices today. Many organizations are desperate to figure out how to convert the insights discovered by data scientists into tangible value for their business-which is easier said than done.

It requires unifying multiple processes across multiple teams-starting with defining business objectives and continuing all the way through data acquisition and model development and deployment.

This unification is achieved through a set of best practices for communication and collaboration between the data engineers who acquire the data, the data scientists who prepare the data and develop the model, and the operations professionals who serve the models.

Why Do You Need MLOps?

Businesses are dealing with more data than ever before. In a recent study, the IBM Institute for Business Value found that 59% of companies have accelerated their digital transformation. This pivot to digital-first enterprise strategy means continued investments in data, analytics, and AI capabilities have never been more critical.

Leveraging data as a strategic asset can lead to accelerated business growth and increased revenue. According to McKinsey, companies with the greatest overall growth in revenue and earnings receive a significant proportion of that boost from data and analytics. If you're hoping to replicate this growth and set your business up for sustainable success, ad hoc initiatives and one-off projects won't cut it. You'll need a well-planned data strategy that brings the best practices of software development and applies them to data science-which is where MLOps comes in.

MLOps bridges the gap between gathering data and turning that data into actionable business value. A successful MLOps strategy leverages the best of data science with the best of operations to streamline scalable, repeatable machine learning from end to end. It empowers organizations to approach this new era of data with confidence and reap the benefits of machine learning and AI in real life.

In addition to increased growth and revenue, benefits include faster go-to-market times and lower operational costs. With a solid framework for your data science and DevOps teams to follow, managers can spend more time thinking through strategy and individual contributors can be more agile.

What Problems Does MLOps Solve?

Let's dig into specifics. Applying MLOps best practices solves a variety of the problems that plague businesses around the globe, including:

Poor Communication

No matter how your company is organized, it's likely that your data scientists, software engineers, and operations managers live in very different worlds. This silo effect kills communication, collaboration, and productivity.

Without collaboration, you can forget about simplifying and automating the deployment of machine learning models in large-scale production environments. MLOps solves this problem by establishing dynamic pipelines and adaptable frameworks that keep everyone on the same page-reducing friction and opening up bottlenecks.

Unfinished Projects

As VentureBeat reports, 87% of machine learning models never make it into production. In other words, only about 1 in 10 data scientists' workdays actually end up producing something of value for the company. This sad statistic represents lost revenue, wasted time, and a growing sense of frustration and fatigue in data scientists everywhere. MLOps solves this problem by first ensuring all key stakeholders are on board with a project before it kicks off. MLOps then supports and optimizes every step of the process, ensuring that each model can journey its way toward production without any lag (and without the never-ending email chains).

Lost Learnings

We already talked about the silo effect, but it rears its ugly head again here. Creating and serving ML models requires input and expertise from multiple different teams, with each team driving a different part of the process. Without communication and collaboration between everyone involved, key learnings and critical insights will remain stuck within each silo. MLOps solves this problem by bringing together different teams with one central hub for testing and optimization. MLOps best practices make it easy to share learnings that can be used to improve the model and rapidly redeploy.

Redundancy

Lengthy development and deployment cycles mean that, way too often, evolving business objectives make models redundant before they've even been fully developed. Or the changing business objectives mean that the ML system needs to be retrained immediately after deployment. MLOps solves these issues by implementing best practices across the entire process-making productizing ML faster at every stage. MLOps best practices also build in room for adjustments, so your models can adapt to your changing business needs.

Misuse of Talent

Data scientists are not software engineers and vice versa. They have different focuses, different skill sets, and very different priorities. Expecting one to perform the tasks of the other is a recipe for failure. Unfortunately, many organizations make this mistake while trying to cut corners or speed up the process of getting machine learning models into production. MLOps solves this problem by bringing both disciplines together in a way that lets each use their respective talents in the best way possible-laying the groundwork for long-term success.

Noncompliance

The age of big data is accompanied by the age of intense, ever-changing regulation and compliance systems. Many organizations struggle to meet data compliance standards, let alone remain adaptable for future iterations and addendums. MLOps solves this problem by implementing a comprehensive plan for governance. This ensures that each model, whether new or updated, is compliant with original standards. MLOps also ensures that all data programs are auditable and explainable by introducing monitoring tools.

How Do You Implement MLOps In Your Organization?

Now that you're sold on the benefits of MLOps, it's time to figure out how you can bring the discipline to life at your organization.

The good news is that MLOps is still a relatively new discipline, which means even if you are just now getting started you aren't far behind other organizations. The bad news is that MLOps is still a relatively new discipline, which means there aren't many tried-and-true formulas for success readily available for you to replicate at your organization. However, ModelOps platforms with ready-to-deploy models can accelerate the MLOps process.

That being said, if you are ready to invest in machine learning there are a few ways you can set your organization up for success. Let's dive into how to achieve MLOps success in more detail:

MLOps Teams

Start by looking at your teams to confirm you have the necessary skill sets covered. We've already established that productizing ML models require a set of skills that, up until now, organizations have considered separate. So, it's likely that your data engineers, data scientists, software engineers, and operations professionals will be dispersed throughout various departments.

You don't need to alter your entire organizational structure to create a MLOps team. Instead, consider creating a hybrid team with cross-functionality. This way you can cover a wide range of skills without too much disruption to your organization. Alternatively, you may choose to use a solution like aiWARE that can rapidly deploy and scale AI within your applications and business processes without requiring AI developers and ML engineers.

Your MLOps team will need to cover 4 main areas:

Scoping

The first stage in a typical machine learning lifecycle is scoping. This stage consists of scoping out the project by identifying what business problem(s) you are aiming to solve with AI.

This stage usually involves collaborators with a deep understanding of the potential business problems that can be solved with AI such as d-level managers and above. It also usually includes collaborators that are intimately familiar with the data such as senior data scientists.

Data

The second stage in a typical ML lifecycle is data. This stage starts with acquiring the data and continues through cleaning, processing, organizing, and storing the data.

Stage two usually involves both data engineers and data scientists along with product managers.

Modeling

Stage three in the typical ML lifecycle is modeling. In this stage, the data from stage two is used to train, test, and refine ML models.

This third stage usually involves both data engineers and data scientists (and even ML architects if you have them). It also requires feedback and input from cross-functional stakeholders.

Deployment

The fourth and final stage in the typical machine learning lifecycle is deployment. Trained models are deployed into production.

This stage usually involves collaborators that have experience with machine learning and the DevOps process, such as machine learning engineers or DevOps specialists.

The exact composition and organization of the team will vary depending on your individual business needs, but the essential part is ensuring that each skillset is covered by someone.

MLOps Tools

In addition to having the right team, you'll also need to have the right tools in place to achieve MLOps success. MLOps is a relatively new, rapidly growing field. And, as is often the case in such fields, a large variety of tools have been created to help manage and streamline the processes involved.

When putting together your MLOps toolkit, you'll need to consider a few different factors such as the MLOps tasks you need to address, the languages and libraries your data scientists will be using, the level of product support you'll need, which cloud provider(s) you'll be working with, what AI models and engines to utilize, etc.

Once you build models, you can easily onboard them into a production-ready environment with aiWARE. This option allows you to rapidly deploy models that solve real-world business problems. And flexible API integrations make it easy to customize the solution to your business needs.

How Do I Learn MLOps?

As we've already mentioned, MLOps is a rapidly growing field. And that massive growth is only expected to continue-with 60% of companies planning to accelerate their process automation in the next 2 years, according to the IBV Trending Insights report.

This increased investment has made MLOps, or DevOps for machine learning, a necessary skill set at companies in nearly every industry. According to the LinkedIn emerging jobs report, the hiring for machine learning and artificial intelligence roles grew 74% annually between 2015 and 2019. This makes MLOps the top emerging job in the U.S.

And it's experiencing a talent shortage. There are many factors contributing to the MLOps talent crunch, the biggest being an overwhelming number of platforms and tools to learn, a lack of clarity in role and responsibility, a shortage of dedicated courses for MLOps engineers and an overwhelming number of platforms and tools to learn.

All that to say, if you're looking to get your foot in the MLOps door there's no better time than right now. We recommend checking out some of these great resources:

MLOps Resources

This course, currently available on Coursera, is a great jumping-off point if you're new to MLOps. Primarily intended for data scientists and software engineers that are looking to develop MLOps skills, this course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring, and operating production ML systems on Google Cloud.

This course, currently available on Coursera, is for those that have already nailed the fundamentals. It covers deep MLOps concepts as well as production engineering capabilities. You'll learn how to use well-established tools and methodologies to conceptualize, build and maintain integrated systems that continuously operate in production.

This book, by Mark Treveil and the Dataiku Team, was written specifically for the people directly facing the task of scaling ML in production. It's a guide for creating a successful MLOps environment, from the organizational to the technical challenges involved.

This seminar series takes a look at the frontier of ML. It aims to drive research focus to interesting questions and stir up conversations around ML topics. Every seminar is live-streamed on YouTube, and they encourage viewers to ask questions in the live chat. Videos of the talks are available on YouTube afterward as well. Past seminars are available for viewing on YouTube as well.

This book, by Andriy Burkov, offers a 'theory of the practice' approach. It provides readers with an overview of the problems, questions, and best practices of machine learning problems.

We also highly recommend joining the MLOps community on slack. An open community for all enthusiasts of ML and MLOps, you can learn many interesting things and broaden your knowledge. Both amateurs and professionals alike are welcome to join the conversation.

Want to Learn Even More About MLOps?

In the coming weeks, we'll be digging into some core MLOps topics that may interest you. If you're interested in diving deeper, keep an eye on our blog. We'll publish more in-depth content that covers MLOps best practices, ModelOps, MLOps tools, and MLOps versus AIOps.

Ready to dig into another MLOps resource right away? Check out this on-demand webinar: MLOps Done Right: Best Practices to Deploy. Integrate, Scale, Monitor, and Comply.

Continued here:
Veritone : What Is MLOps? | A Complete Guide to Machine Learning Operations - Marketscreener.com

This article was originally published here

Nat Commun. 2021 Jun 17;12(1):3726. doi: 10.1038/s41467-021-24001-2.

ABSTRACT

High-throughput, high-accuracy detection of emerging viruses allows for the control of disease outbreaks. Currently, reverse transcription-polymerase chain reaction (RT-PCR) is currently the most-widely used technology to diagnose the presence of SARS-CoV-2. However, RT-PCR requires the extraction of viral RNA from clinical specimens to obtain high sensitivity. Here, we report a method for detecting novel coronaviruses with high sensitivity by using nanopores together with artificial intelligence, a relatively simple procedure that does not require RNA extraction. Our final platform, which we call the artificially intelligent nanopore, consists of machine learning software on a server, a portable high-speed and high-precision current measuring instrument, and scalable, cost-effective semiconducting nanopore modules. We show that artificially intelligent nanopores are successful in accurately identifying four types of coronaviruses similar in size, HCoV-229E, SARS-CoV, MERS-CoV, and SARS-CoV-2. Detection of SARS-CoV-2 in saliva specimen is achieved with a sensitivity of 90% and specificity of 96% with a 5-minute measurement.

PMID:34140500 | DOI:10.1038/s41467-021-24001-2

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Combining machine learning and nanopore construction creates an artificial intelligence nanopore for coronavirus detection - DocWire News

1. Diet optimization. A machine learning model for predicting blood sugar levels after people eat a meal was significantly better at the task than conventional carbohydrate counting, the authors report. The algorithms creators used the tool to compose good (low glycemic) and bad (high glycemic) diets for 26 participants.

For the prediction arm, 83% of participants had significantly higher post-prandial glycemic response when consuming the bad diet than the good diet, Limketkai and colleagues note. This technology has since been commercialized with the Day Two mobile application on the front.

2. Food image recognition. A primary challenge in alerting dieters to likely nutritional values and risks going by photos snapped on smartphones is the sheer limitlessness of possible foods, the authors point out. An early neural-network model developed at UCLA by Limketkai and colleagues achieved impressive performance in training and validating 131 predefined food categories from more than 222,000 curated food images.

However, in a prospective analysis of real-world food items consumed in the general population, the accuracy plummeted to 0.26 and 0.49, respectfully, write the authors of the present paper. Future refinement of AI for food image recognition would, therefore, benefit on training models with a significantly broader diversity of food items that may have to be adapted to specific cultures.

3. Risk prediction. Machine learning algorithms beat out conventional techniques at predicting 10-year mortality related to cardiovascular disease in a densely layered analysis of the National Health and Nutrition Examination Survey (NHANES) and the National Death Index.

A conventional model based on proportional hazards, which included age, sex, Black race, Hispanic ethnicity, total cholesterol, high-density lipoprotein cholesterol, systolic blood pressure, antihypertensive medication, diabetes, and tobacco use appeared to significantly overestimate risk, Limketkai and co-authors comment. The addition of dietary indices did not change model performance, while the addition of 24-hour diet recall worsened performance. By contrast, the machine learning algorithms had superior performance than all [conventional] models.

Read the rest here:
4 ways machine learning is fixing to finetune clinical nutrition AI in Healthcare - AI in Healthcare

In a digital age, it only takes a few years for research into cutting-edge areas like predictive analytics modelling and artificial intelligence to find practical uses in everyday business contexts. Areas like general usability, user interface, and semantics that are changed to empower a broader cross-section of potential users.

Business-focused users keen to leverage statistical methods may not be capable of or comfortable with interacting in a pure text terminal in Python or R, for example. After all, open-source code is exactly that: can you blame someone for not wanting to make an investment decision based on code found freely on the internet? Business users need an easy way to interface with powerful analytics and need a trusted brand that stands behind them. Thats exactly why Minitab has changed the game, making machine learning easy for everyone.

Today, business users do not have to compromise to do advanced analytics. For statistical analysis, predictive analytics, and machine learning, there is a 50-year-old powerhouse called Minitab.

Over the past couple of years, Minitab has revolutionized the market by bringing the worlds most advanced data gathering, processing, visualizations, and analysis to the masses. Most recently, Minitab broke the barrier to putting Data Science into the hands of business professionals. And unlike others who promised this in the past, Minitab delivers.

Thats because, across the companys product portfolio, there is a strong emphasis on usability and business outcomes Minitab is deployed by a broad cross-section of the business community.

For decision-makers tasked with a business process or operational improvements, access to data and the ability to use it to achieve clear goals is critical. The lifeblood of todays organizations is information, so using it to examine what was, what is, and what might happen can result in in lowered costs, higher revenues, and more efficient, timely actions for long-term strategy.

The talk may be of variables and predictors in mathematics and statistics for the businesss Change Manager, its data sources, outcomes, and results. The phraseology might be different, but the required data processing and analysis remain the same. Dont be intimidated by the term Machine Learning. All it refers to is learning from your data, which is effectively what data analysis is. Dont believe us? Try it yourself.

With Minitab at the core of your data operations, there is immediate plug and play access to hundreds of data sources via included connectors that allow companies to access the data silos, repositories and applications across the network and in the cloud. Its not surprising that Minitab was the highest-rated data integration tool according to Gartner Peer Insights.

Coupled with the statistical core of the Minitab data analysis platform, professionals can get a full picture of their data by leveraging archived information and real-time data streams as they happen in any organization.

Data is cleaned, transformed, and presented, providing the basis for predictive analytics modelling and insights into existing work processes.

As companies begin to scratch the surface of the data resources they have, hidden relationships between events and variables are uncovered. Factors that were never apparent can surface, even to the objective observer, and visual relationships and correlations emerge. Insights gained help both data specialists and line-of-business experts to determine how best to achieve the companys objectives.

For line-of-business managers and non-data scientists, the visual language of Minitab helps show and correlate the various factors at play. It allows them to create predicted outcomes to proposed changes to operations in safe modelling environments.

The beauty of the Minitab portfolio is its design for use in practical settings. The platforms openness and user interface mean it can be used in multiple verticals and unexpected use-cases: manufacturer Tate & Lyle used AI techniques and plotted thousands of variables to refine its sweetener consistency for better customer experience, for example. Where one might least expect it, Minitabs statistical power is creating change.

In a vast range of industries, advanced analytical modelling, analysis, and machine learning algorithms are being deployed by organizations to improve outcomes in thousands of scenarios. At one time, this type of statistical analysis was only seen in finance and high-end medical research and pharma, but not today. Minitab is making real, meaningful differences in thousands of settings.

It integrates with both cloud and on-premises applications and services, from marcomms to stream processors. Minitab installs locally or is now available as a SaaS, ready to be accessed from anywhere with an internet connection.

To learn more about the Minitab suite of offerings and begin leveraging its accessible power to affect change, start your journey here.

Read the rest here:
Improve Your Business's Processes with Predictive Analytics and Machine Learning - Tech Wire Asia

While the maritime industry has been hesitant engaging in use of data insight and machine learning, the table is now about to turn. Today, an increasing number of maritime companies actively use data insights to improve sales, supply chain activities, and increase revenues among these the worlds largest ship supplier, Wrist Ship Supply.

The need for efficiency in the maritime sector has led companies to actively use data as a measure to optimize the supply chain. This has paved the way for new ship supply services centered around data insights and machine learning to increase top and bottom-line figures.

According to the leading data and analytics firm, GateHouse Maritime, data insights can make a noticeable difference in the maritime sector. With a combination of historic and real-time ocean data, machine learning, and smart algorithms, maritime supply companies can predict vessel destinations and arrivals with high precision.

Traditionally, vessel tracking has been a time consuming, manual process characterized by imprecise predictions and uncertainty. But today, the process can be automated and turn large amounts of data into tangible leads and sales:

With the help of data insights, it is possible to predict arrivals several days in advance with almost 100 percent accuracy. This allows maritime supply companies to obtain an obvious competitive advantage, as they can operate proactively and sell services to potential customers days before a given vessel calls into port, says CEO at GateHouse, Maritime, Martin Dommerby Kristiansen.

Data analytics strengthen the worlds largest ship supplierFour years ago, the worlds largest ship supplier, Wrist Ship Supply, realized a strategy that would integrate data analytics in numerous business areas. The global ship supplier is a full-service provider, providing service for marine, offshore and navy operations, such as supplying consumables, handling of owners goods and spare parts storage and forwarding.

Today, Wrist Ship Supply works strategically with data analytics and business intelligence to improve internal processes and increase value for customers:

In recent years, we have experienced an increasing pull from the market and as a market leader within ship supply, we feel obliged to take part in the digital transformation. Data analysis has proven to be a cornerstone and a very important tool for measuring and improving performances across our own as well as customers supply chain. Now, our business model is infused with data analytics and business intelligence that strengthen efficiency and reliability in both internal and external operations, explains Business Analysis Director at Wrist Ship Supply, Birthe Boysen.

For Birthe Boysen and Wrist Ship Supply, data analytics has especially proven its worth within sales:

It is crucial for us to know where potential customer vessels are heading and when they arrive in different ports. This allows us to coordinate our sales efforts and establish contact in advance. Not only does this make us more efficient, but it also creates value for customers, because all service activities can be planned several days ahead of arrival.

While the data-driven sales approach has increased the focus on KPIs, it has also become an important part of budgeting. Therefore, it has been a key priority for Wrist Ship Supply to be able to navigate in the ocean of available data:

We have an almost endless amount of data available, and it easily becomes exhausting to keep track of numbers and figures. Therefore, we prioritize to make sure that both internal and external stakeholders can make sense of the conclusions in our data insights. If employees or customers cannot fathom the overall lines in our data results, it will be difficult to use analytics in any way, Nadia Hay Kragholm, Senior Business Analyst in Wrist remarks.

According to Martin Dommerby Kristiansen, data insight has the potential to transform the entire maritime industry because efficiency has never been more important:

The maritime industry is indeed reliant on efficiency across the value chain. Recently, we have seen how a vessel stuck in the Suez Canal for only a few days can impact not only the maritime industry, but the entire transportation and logistics sector. This goes to show how important data insight and analytics can prove to be for companies that wish to operate proactively and minimize disorder in the supply chain.

GateHouse Maritime is a leader in Ocean Visibility solutions. We help global maritime service providers, cargo owners and logistic companies with transparent and accurate location data and predictions, cargo transport status, and offshore asset protection and surveillance. Our powerful maritime data foundation consists of 273 billion datapoints and +30 analysis and predictive models used for data-driven decisions by maritime operators worldwide. GateHouse Maritime is a subsidiary of GateHouse Holding, founded in 1992 and headquartered in Denmark, and which also holds the subsidiaries GateHouse SatCom and GateHouse Igniter.Source: GateHouse Maritime A/S

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Data Insights and Machine Learning Take Charge of the Maritime Sales Process - Hellenic Shipping News Worldwide

CAMBRIDGE, Mass., June 16, 2021 /PRNewswire/ -- Akamai Technologies, Inc. (NASDAQ: AKAM), the world's most trusted solution for protecting and delivering digital experiences, today announces platform security enhancements to strengthen protection for web applications, APIs, and user accounts. Akamai's machine learning derives insight on malicious activity from more than 1.3 billion daily client interactions to intelligently automate threat detections, time-consuming tasks, and security logic to help professionals make faster, more trustworthy decisions regarding cyberthreats.

In its May 9 report Top Cybersecurity Threats in 2021, Forrester estimates that due to reasons "exacerbated by COVID-19 and the resulting growth in digital interactions, identity theft and account takeover increased by at least 10% to 15% from 2019 to 2020." The leading global research and advisory firm notes that we should "anticipate another 8% to 10% increase in identity theft and ATO [account takeover] fraud in 2021." With threat actors increasingly using automation to compromise systems and applications, security professionals must likewise automate defenses in parallel against these attacks to manage cyberthreats at pace.

New Akamai platform security enhancements include:

Adaptive Security Engine for Akamai's web application and API protection (WAAP) solutions, Kona Site Defender and Web Application Protector, is designed to automatically adapt protections with the scale and sophistication of attacks, while reducing the effort to maintain and tune policies. The Adaptive Security Engine combines proprietary anomaly risk scoring with adaptive threat profiling to identify highly targeted, evasive, and stealthy attacks. The dynamic security logic intelligently adjusts its defensive aggressiveness based on threat intelligence automatically correlated for each customer's unique traffic. Self-tuning leverages machine learning, statistical models, and heuristics to analyze all triggers across each policy to accurately differentiate between true and false positives.

Audience Hijacking Protection has been added to Akamai Page Integrity Manager to detect and block malicious activity in real time from client-side attacks using JavaScript, advertiser networks, browser plug-ins, and extensions that target web clients. Audience Hijacking Protection is designed to use machine learning to quickly identify vulnerable resources, detect suspicious behavior, and block unwanted ads, pop-ups, affiliate fraud, and other malicious activities aimed at hijacking your audience.

Bot Score and JavaScript Obfuscation have been added to Akamai Bot Manager, laying the foundation for ongoing innovations in adversarial bot management, including the ability to take action against bots aligned with corporate risk tolerance. Bot Score automatically learns unique traffic and bot patterns, and self-tunes for long-term effectiveness; JavaScript Obfuscation dynamically changes detections to prevent bot operators from reverse engineering detections.

Akamai Account Protector is a new solution designed to proactively identify and block human fraudulent activity like account takeover attacks. Using advanced machine learning, behavioral analytics, and reputation heuristics, Account Protector intelligently evaluates every login request across multiple risk and trust signals to determine if it is coming from a legitimate user or an impersonator. This capability complements Akamai's bot mitigation to provide effective protection against both malicious human actors and automated threats.

"At Akamai, our latest platform release is intended to help resolve the tension between security and ease of use, with key capabilities around automation and machine learning specifically designed to intelligently augment human decision-making," said Aparna Rayasam, senior vice president and general manager, Application Security, Akamai. "Smart automation adds immediate value and empowers users with the right tools to generate insight and context to make faster and more trustworthy decisions, seamlessly all while anticipating what attackers might do next."

For more information about Akamai's Edge Security solutions, visit our Platform Update page.

About Akamai Akamai secures and delivers digital experiences for the world's largest companies. Akamai's intelligent edge platform surrounds everything, from the enterprise to the cloud, so customers and their businesses can be fast, smart, and secure. Top brands globally rely on Akamai to help them realize competitive advantage through agile solutions that extend the power of their multi-cloud architectures. Akamai keeps decisions, apps, and experiences closer to users than anyone and attacks and threats far away. Akamai's portfolio of edge security, web and mobile performance, enterprise access, and video delivery solutions is supported by unmatched customer service, analytics, and 24/7/365 monitoring. To learn why the world's top brands trust Akamai, visit http://www.akamai.com, blogs.akamai.com, or @Akamai on Twitter. You can find our global contact information at http://www.akamai.com/locations.

Contacts: Tim Whitman Media Relations 617-444-3019 [emailprotected]

Tom Barth Investor Relations 617-274-7130 [emailprotected]

SOURCE Akamai Technologies, Inc.

http://www.akamai.com

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Akamai Unveils Machine Learning That Intelligently Automates Application and API Protections and Reduces Burden on Security Professionals - PRNewswire