June 21, 2021 Hewlett Packard Enterprise today announced that it has acquired Determined AI, a San Francisco-based startup that delivers a powerful and robust software stack to train AI models faster, at any scale, using its open source machine learning (ML) platform.

HPE will combine Determined AIs unique software solution with its world-leading AI and high performance computing (HPC) offerings to enable ML engineers to easily implement and train machine learning models to provide faster and more accurate insights from their data in almost every industry.

As we enter the Age of Insight, our customers recognize the need to add machine learning to deliver better and faster answers from their data, said Justin Hotard, senior vice president and general manager, HPC and Mission Critical Solutions (MCS), HPE. AI-powered technologies will play an increasingly critical role in turning data into readily available, actionable information to fuel this new era. Determined AIs unique open source platform allows ML engineers to build models faster and deliver business value sooner without having to worry about the underlying infrastructure. I am pleased to welcome the world-class Determined AI team, who share our vision to make AI more accessible for our customers and users, into the HPE family.

Determined AI accelerates innovation with open source AI solutions to build and train models faster and easier

Building and training optimized machine learning models at scale is considered the most demanding and critical stage of ML development, and doing it well increasingly requires researchers and scientists to face many challenges frequently found in HPC. These include properly setting up and managing a highly parallel software ecosystem and infrastructure spanning specialized compute, storage, fabric and accelerators. Additionally, users need to program, schedule and train their models efficiently to maximize the utilization of the highly specialized infrastructure they have set up, creating complexity and slowing down productivity.

Determined AIs open source machine learning training platform closes this gap to help researchers and scientists to focus on innovation and accelerate their time to delivery by removing the complexity and cost associated with machine learning development. This includes making it easy to set-up, configure, manage and share workstations or AI clusters that run on-premises or in the cloud.

Determined AI also makes it easier and faster for users to train their models through a range of capabilities that significantly speed up training, which in one use case related to drug discovery, went from three days to three hours. These capabilities include accelerator scheduling, fault tolerance, high speed parallel and distributed training of models, advanced hyperparameter optimization and neural architecture search, reproducible collaboration and metrics tracking.

HPC the foundation for delivering speed-to-insight and AI at scale

AI training is continuing to fuel projects and innovation with intelligence, and to do so effectively, and at scale, will require specialized computing. According to IDC, the accelerated AI server market, which plays an integral role in providing targeted capabilities for image and data-intensive training, is expected to grow by 38% each year and reach $18B by 2024.

The massive computing power of HPC is also increasingly being used to train and optimize AI models, in addition to combining with AI to augment workloads such as modeling and simulation, which are well-established tools to speed time-to-discovery. Intersect360 Research notes that the HPC market will grow by more than 40%, reaching almost $55 billion in revenue by 2024.

To tackle the growing complexity of AI with faster time-to-market, HPE is committed to continue delivering advanced and diverse HPC solutions to train machine learning models and optimize applications for any AI need, in any environment. By combining Determined AIs open source capabilities, HPE is furthering its mission in making AI heterogeneous and empowering ML engineers to build AI models at a greater scale.

Additionally, through HPE GreenLake cloud services for High Performance Computing (HPC), HPE is making HPC and AI solutions even more accessible and affordable to the commercial market with fully managed services that can run in a customers data center, in a colocation or at the edge using the HPE GreenLake edge to cloud platform.

The Determined AI team will join HPEs High Performance Computing (HPC) & Mission Critical Solutions (MCS) business group

Determined AI was founded in 2017 by Neil Conway, Evan Sparks, and Ameet Talwalkar, and based in San Francisco. It launched its open-source platform in 2020, and as a result of its focus on model training, Determined AI has quickly emerged as a leading player in the evolving machine learning software ecosystem. Its solution has been adopted by customers across a wide range of industries, such as biopharmaceuticals, autonomous vehicles, defense contracting, and manufacturing.

About Determined AI

Determined AI is an early stage company at the forefront of machine learning technology by helping customers reap benefits of high-performance computing without the required expertise or staffing. Determined AI provides an open source machine learning solution that speeds up time-to-market by increasing developer productivity, improving resource utilization and reducing risk. The company is headquartered in San Francisco. For more information, visit: http://www.determined.ai

About Hewlett Packard Enterprise

Hewlett Packard Enterprise (NYSE: HPE) is a global edge-to-cloud company that helps organizations accelerate outcomes by unlocking value from all of their data, everywhere. Built on decades of reimagining the future and innovating to advance the way people live and work, HPE delivers unique, open and intelligent technology solutions delivered as a service spanning Compute, Storage, Software, Intelligent Edge, High Performance Computing and Mission Critical Solutions with a consistent experience across all clouds and edges, designed to help customers develop new business models, engage in new ways, and increase operational performance. For more information, visit: http://www.hpe.com

Source: HPE

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HPE Acquires Determined AI to Accelerate Machine Learning Training - HPCwire

ZEISS is introducing a new ground breaking solution for petrographic analysis. ZEISS Axioscan 7 expands the possibilities of automated microscopy by combining unique motorized polarization acquisition modes with unprecedented speed and a rich software ecosystem for visualization, analysis, and collaboration.

The most challenging research tasks as well as your routine scanning applications are supported by powerful hardware and perfectly featured software. The ZEISS Axioscan 7 captures virtual slides quickly with high-speed scanning, while retaining consistently high quality, whether needing to capture brightfield, fluorescence or polarized light images.

Fully automated acquisition now comes with unprecedented speed across even the largest sample collections. Coupled with trusted ZEISS optical quality this ensuresconsistent and reproducible imaging and analysis. The ZEISS ZEN Pol Viewer allows for complex multichannel polarization data to be visualized and interrogated in an intuitive environment as a virtual petrographic microscope. Data can be stored locally or automatically uploaded to the cloud for online visualization, distribution and collaboration, allowing researchers to share their images online and organize entire projects on the go.

Polarization Images With Unprecedented Flexibility

Speed, efficiency, and multichannel viewing are critical when it comes to petrographic dataset analysis. ZEISS Axioscan 7 employs swift and reproducible LED illumination and a sophisticated filter concept to efficiently separate a broad range of channels, using the new motorized stage and image acquisition system. Aperture settings are automatically adjusted and optimized to the numerical aperture of the selected objective.

Visualize Complex Digitized Petrographic Data

Geological teaching and research require specialized visualization solutions and global collaboration. Remote access to fully digitized thin section collections augments traditional teaching approaches through blended learning, allowing for a flexible approach to tackling complex ideas.

ZEISS Axioscan 7, the innovative digitization platform, goes beyond collecting multiple polarization modes brightfield, plane polarization, cross polarization, or circular polarization. It automatically simulates stage rotation such that the sample appears exactly as it would in a traditional petrographic light microscope, even whilst observing multiple image types. This facilitates the learning process in a teaching environment in the lab and online and creates a unique and immersive petrographic experience.

Automated Quantitative Petrography

Multimodal acquisition and digitization allow for fully automated quantification and analysis of complex optical mineralogy data. Machine learning and advanced image analysis can be used to classify mineralogy and porosity, as well as identify individual mineral grains directly from the unique rotating cross polarized data. Birefringence analysis allows for fabrics and anisotropy to be quantified using mineral-by-mineral identification of extinction orientation, and subsequent grain identification used for grain size, shape and orientation analysis. The extremely reproducible nature of ZEISS Axioscan 7 data allow for large batch analysis to be interrogated and reports generated with only minimal user interaction.

The demand for higher throughput and screening capability drives the need for automated instruments. ZEISS Axioscan 7 provides automation without sacrificing flexibility or the high quality of images needed to attract a very wide range of users to core imaging facilities. With approaches as varied as visualization for students to advanced analytics in industry, the new slide scanner attracts users from the full range of geoscience workflows. The powerful combination of accommodating a broad user base with robust design places ZEISS Axioscan 7 as a top performer when it comes to utilization & productivity, and a rapid return on investment.

For more information: http://www.zeiss.com/microscopy/

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Edge platform enhancements increase responsiveness and augment security decision-making

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.

Akamai Technologies, Inc. logo (PRNewsfoto/Akamai Technologies, Inc.)

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.

Story continues

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 twhitman@akamai.com

Tom Barth Investor Relations 617-274-7130 tbarth@akamai.com

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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.

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