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Thanks for following ITPro's coverage of the opening keynote at KubeCon 2024 in Paris. Stay tuned for more KubeCon news over the next few days!

The crowd is thanked for joining and, just like that, people begin to filter out the keynote theatre.

And now, as they leave the stage, it looks like it's already time for the opening keynotes closing remarks! The hosts announce some exciting events that are yet to come during the conference.

Managed model training is a topic of the pairs address, and they discuss using Buildpack and Kubeflow, before moving on to talking about multi-cluster scheduling. Knative and KServe are also platforms used at Bloomberg.

To support their features, they need a scalable infrastructure and they need to support a complex machine learning lifecycle. They leverage open source solutions to help with this process.

They talk about the companys flagship product, Bloomberg terminal. The company handles a massive amount of data every day. They talk about how Bloomberg terminal uses AI (unsurprisingly given the focus of the keynotes so far).

Now two more speakers for the next keynote, Yuzhui Liu Team Lead at Bloomberg, and Leon Zhou, Software Engineer at Bloomberg.

After another speaker jumps into the news room, Ibrahim Haddad (via video), its a wrap for the news show!

Now Kakodkar will speak to cloud native and AIs relationship more generally, talking about the relationship of infrastructure and cloud native as symbiotic.

The keynote now cuts to an imitated live report from somewhere outside the conference center.

This just inKubernetes enters its second decade. It learned how to walk and then run, and now will fly into the AI galaxy - it looks like this keynote session will feature several different news broadcasters, or just plain speakers, over its duration.

Next to the stage is the curiously named Cloud Native News Show (complete with faux-news show graphics) headed up by Nikhita Raghunath and Rajas Kakodar.

To close out, the panel host reaffirms Kubernetes as becoming the standard for AI platforms, while also going over some of the panels discussion on resource allocation, data loading, and the importance of choosing the right specialized compute for the right model.

The questions are flying: What can we do to optimize for sustainability? One panelist jumps in to tout lower usage compute systems that, in turn, create lower costs.

Compute power seems to be a running theme in KubeCon 2024s opening keynotes, and the panels attention is turned considerably towards concepts of GPU sharing and efficient GPU use.

The panel starts with a question about how Kubernetes might be simplified, followed by a question about the compute decisions people should make in regard to Kubernetes. GPU-free LLM inference is one response to the latter of the two questions.

Next to the stage are several people for a panel discussion: Lu Qiu from Alluxio, Susan Wu and Clayton Coleman from Google, Victor Jakubiuk from Ampere Computing, and Ricardo Rocha from CERN.

Thats why hes announcing the companys new KAITO architecture, which will deploy and run inference for models. Its a speedy run through of the product and then hes off!

Next to the stage is Jorge Palma, Principal PM Lead at Microsoft, who will be taking the lead for a sponsored keynote on building an open source platform for AI. There are a lot of issues along the way, he says, such as containerizing models.

Topology aware placement is key to avoiding problems, he says, as well as an understanding of fault tolerance and resiliency. Problems need to be diagnosed easily and observability needs to be built in.

Klues hands over to Chatterjee to talk about the issues around scaling out GPUs, particularly with regard to training. To satisfy the demands of generative AI, he says, a vast amount of GPUs are needed.

They talk about time-slicing, a technique used to split the demands of AI workloads, as well as GPU sharing. Kevin gives a little shout out to some other talks at the conference if people want to know more!

They talk about how GPUs are at the heart of the AI revolution and will talk about what it takes to enable GPU support in Kubernetes, support that can help in directing AI workloads.

Two new speakers hit the stage, Kevin Klues, Distinguished Engineer, and Sanjay Chatterjee, Engineering Manager at NVIDIA, to talk about AI workloads with GPUs in Kubernetes.

Sharma brings up the phrase open source washing, which turns the groups attention to the importance of open source in AI. Morgan talks about the open source nature of Kubernetes but then also comments that this is affected by whether a model is open source, while Lacroix says that it would be helpful for all the tooling for AI to be open source.

The group also talk about inference cases, the possibility of smaller model use, and evolution in the space in general. They talk about the different workloads for AI, such as training, fine-tuning, and inference.

Sharmas focusing on linking cloud native and AI together, now bringing to the stage the co-founder of Mistral AI, Timothe Lacroix, lead product manager at Google, Paiege Bailey, and founder of Ollama, Jeffery Morgan. They discuss the relationship between AI and cloud native.

Sharma develops a visual language model that impressively details a picture of the conference hall itself! Everyone was suitably pleased.

Sharma talks about how, together, cloud native has made platforms more resilient and stable, listing some of the big names associated with it - Adidas, Disney, Spotifythe list goes on.

This is apparently the largest KubeCon ever according to Sharma - so big, she jokes, that even her parents are here!

Priyanka Sharma, Executive Director of Cloud Native Computing Foundation (CNCF) has hit stage to wild applause from the audience.

2024-03-20T08:19:57.636Z

The keynote session has begun, kicked off with a promotional video!

Everyone has now been allowed into the keynote theatre and it's filling up quickly.

No one's allowed into the keynote theatre just yet so this is a good time to get acquainted with the conference space.

The weather's getting nice now! KubeCon's keynote talks are located on the third floor of the centre.

The registration area is filling up, although the Paris Expo Centre's coffee shops aren't open yet unfortunately!

It's a crisp but sunny day here in Paris as people make their way in to the Paris Expo Centre for the first day of KubeCon 2024.

Welcome to ITPro's live coverage of KubeCon 2024 at the Paris Expo conference center. We're on the ground in Paris waiting for the opening keynote to begin.

In today's session, we'll hear from the Cloud Native Computing Foundation (CNCF) on all the latest developments in the cloud native space, along with community updates and live presentations from a host of industry figures.

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Read more from the original source:
KubeCon 2024: All the latest news and announcements - ITPro

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Article reviewed by Dana Cortade, PhD from Align to Innovate.

Stay up to date on the latest science with Brush Up Summaries.

Cloud labs provide remote access to automated scientific instruments, reagents, and robots.

Remote labs are physical laboratories that enable researchers to conduct experiments without being physically near the equipment. They offer remote access to raw reagents, instruments, sample preparation, and step-by-step experimental processes. Besides improving access to complex experiments for more researchers, remote labs can also minimize human error by automating workflows.1

Cloud labs are a type of remote lab that enables researchers to conduct their experiments remotely using a computer and cloud-based infrastructure.2 The cloud is an extensive network of software servers, which can be accessed through an online interface from any geographical location. Cloud labs provide access to software that coordinates automated scientific instruments, inventories (e.g., chemical reagents), and robots to perform experiments and process data.3 By logging into the cloud, researchers can carry out scalable, standardized, and robust biological protocols in remotely operated labs.

The idea of remote work can be traced back to the 1970s when Jack Nilles, a NASA engineer, proposed the use of telecommuting to alleviate traffic congestion.4 The advent of personal computers and widespread internet access in the early 2000s has contributed to the concept of remote work being popularly accepted as a viable working arrangement across many disciplines.5

In scientific research, the outbreak of the covid pandemic highlighted researchers strong reliance on in-person work and the limitations of human-dependent experimental procedures.6 It was during this time that cloud labs gained immense popularity in the research community.7 Currently, the demand for cloud lab services is growing at a faster pace compared to pre-pandemic levels.

Progress and adoption of remote labs stem from major milestones in remote work technologies, laboratory automation, robotics, cloud infrastructure, and the pandemic response.

The Scientist

The creation of remote labs has been made possible by rapid progress in automation, facilitated by advances in robotics and artificial intelligence (AI).2,8

Automation is a multidisciplinary strategy that enables the operation of laboratory tools and instruments with minimum human interaction. Automation also improves sample throughput, which saves a considerable amount of discovery time and contributes to breakthroughs that are conventionally limited by manual methods. In order to automate their research, scientists depend on automated equipment. We hope with time and innovation that more and more labs will have access to automation, whether it's within their own labs, in core facilities that they can use, or in commercial entities like cloud labs, said Dana Cortade, a technical project manager at Align to Innovate, a scientific nonprofit organization that focuses on improving science via automation.

Robotics play a key role in automating standard laboratory operations and reducing human errors. As an example, liquid handling robots in remote labs enable reagent preparation and sample transfer between different experimental steps with high precision.9 Automation using pipetting robots is another time-saving strategy for simultaneous processing of many samples while reducing contamination risks.10

AI has significantly contributed to the development of automated and remote labs. Within the realm of AI, machine learning (ML) uses mathematical models to perform complex prediction tasks. Cloud labs commonly use ML models to predict material properties and experimental outcomes, supporting automated and semi-automated workflows. AI-based algorithms help scientists analyze big data faster by automating the process.11

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Two examples of commercial cloud labs areStrateosandEmerald Cloud Lab.12,13 Both primarily provide services for industry researchers. Strateos develops in-house cloud lab platforms for private companies working on drug discovery and characterization. Emerald Cloud Lab provides many laboratory services, such as sample preparation, bioassays, DNA and peptide synthesis, bioimaging, and biomaterial characterization. Arctoris is another commercial remote lab that is a technology-enabled multinational contract research organization (CRO). It offers a remote-operated platform that is mainly focused on automated, AI-driven drug discovery and testing.14

Biofoundries are another type of remote lab; they offer infrastructure for testing genetically engineered organisms for different biotechnological applications, such as the production of antibiotics, bioplastics, and biofuels.15 Although many biofoundries have been established over the last five years, they are not widely used in the biotechnology or biological research communities, largely due to lack of awareness. Recent initiatives such as The Global Biofoundry Alliance aim to foster coordination among biofoundries and expand their global reach.15

The recent growth of automated experimentation techniques and data science has led to the development of self-driving labs.16 These labs enhance molecule and material synthesis and characterization by performing ML-selected experiments using automated tools and techniques. Scientists use these facilities for the production of complex organic compounds, thin-film materials, and carbon nanotubes.17-19

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The main advantages of using cloud labs are scalability, safety, 24/7 availability, and automation of a wide range of tasks.20 Because cloud labs often include many types of remotely operated equipment, scientists can perform a broader range of experiments without needing technical expertise to manually operate each instrument. Newly generated experimental data is stored securely for future analysis, and successful experimental protocols can be saved, transferred to other work platforms, and reused in the future.

There are also challenges to using remote labs. For instance, many cloud lab limitations revolve around data integrity, data protection, and data security issues, stemming from service disruption, data loss, and cyber-attacks.21

Another limitation centers around funding challenges in academia, which prevents the widespread adoption of cloud technology. I think that a lot of the really cool discoveries and breakthroughs that we've seen have come from either really big labs or industry groups, because theyve had the funding for a while now to be able to have high throughput and automation in their labs, said Cortade. Additionally, commercial cloud lab services are often expensive and come with lengthy contract terms, primarily suitable for industry customers and not academic institutions. Align to Innovate is one recent initiative that aims to address this challenge.

References

More:
How Cloud Labs and Remote Research Shape Science - The Scientist

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The Cloud Native Computing Foundation (CNCF) will add 45 new members to its growing community. The announcement was made during KubeCon + CloudNativeCon Europe in Paris. Among the new members are Aviatrix, ITQ and grocery delivery service Picnic.

According to CNCFs own statement, these new additions to the CNCF fold symbolize a collaborative effort to address the multifaceted challenges confronting modern enterprises. Chris Aniszczyk, CNCFs CTO, lauded the influx of diverse entities, remarking, Every quarter, it is inspiring to witness the burgeoning involvement of a myriad of organizations in contributing to and leveraging cloud native technologies. The investment and engagement from these forward-thinking companies are pivotal in propelling the evolution of our ecosystem.

Most new members have joined the Silver Membership tier. Additionally, several companies and organisations have joined as either End User Members or End User Supporters. Find the complete list here.

Since its inception in 2015, CNCF helps spread open-source projects intended for cloud-native applications. It gives developers a chance to mature their projects step by step, giving these applications time to become reliable and commercially deployable.

To be admitted under the wings of the CNCF, developers must meet a number of requirements. These include accepting a Code of Conduct and clearly communicating version updates. The collective effort of its members has greatly shaped the trajectory of cloud-native computing. The CNCF oversees the development of influentialopen-source projectssuch as Kubernetes, Envoy and Prometheus.

Tip: These are the 10 most dangerous Kubernetes vulnerabilities

The CNCF claims to be committed to making cloud-native technology accessible to all, be it in public, private or hybrid domains. Cloud-native technology includes solutions for developing and running containers, microservices and service meshes.

The Cloud Native Computing Foundation (CNCF) is part of the Linux Foundation. Organizations that use CNCF projects in their offerings can join the CNCF as members, some of these are AWS, Google Cloud, IBM, and Microsoft.

Also read: Cloud Native Computing Foundation grows to 800 members

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Cloud Native Computing Foundation (CNCF) welcomes 45 new members - Techzine Europe

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At the forefront of technological advancements, Microsoft has recently emphasized its significant investment in artificial intelligence (AI) and cloud solutions within its partner ecosystem. The company announced an array of updates, aimed at empowering partners to innovate and grow. These initiatives promise to reshape how Microsoft collaborates with its global network of solutions providers and expand the companys influence in small to midsize businesses (SMBs).

Microsofts key focus on AI has resulted in Solution Partners utilizing Azures AI capabilities across more than 53,000 customer implementations. The enthusiasm for AI has translated into a substantial increase in partner involvement, downloads of Microsofts AI resources, and personalized AI-driven security solutions. The newly announced Copilot for Security, for instance, which will be available in all commerce channels, stands as testament to the rapid integration of AI into security measures.

The Solutions Partner program has seen enhancements through certified software designations, allowing partners to specialize and differentiate themselves effectively. These designations span a variety of sectors, including healthcare, retail, financial services, manufacturing, and sustainability, showcasing the extensive reach of Microsofts AI and cloud services.

In supporting the SMB market, Microsoft aims to roll out tailored partner pathways for Azure and security solutions later this year. This strategic initiative is expected to furnish SMB-focused solution providers with critical resources and benefits.

Additionally, new partner designations for training and support services are set to be launched, offering exclusive resources and customer-facing recognition. These designations highlight an ongoing commitment to fostering a skilled partner base, ready to tackle emerging tech challenges.

In an ever-evolving digital landscape, Microsofts partner program updates signal a transformative phase where AI is turning from a concept into large-scale implementation, enabling partners to deliver cutting-edge solutions and foster growth in their specific domains.

Microsofts Commitment to AI and Cloud Innovation in the Partner Ecosystem

Microsofts latest endeavors underscore a deep commitment to harnessing the potential of artificial intelligence (AI) and cloud technology, particularly within its partner ecosystem. The technology giant is set on empowering its partners to pioneer innovations and expand their reach, especially within the small to midsize business market.

AI and Cloud Solutions Driving Industry Transformation

The push into AI by Microsoft has been especially notable, with Solution Partners harnessing Azures AI capabilities in over 53,000 customer implementations. This enthusiasm is not unfounded; artificial intelligence is radically transforming industries by enabling new capabilities and workflows. These advances have not only led to an uptick in partner engagement and utilization of Microsofts AI tools but also in the development of more refined AI-driven security solutionshighlighted by the introduction of Copilot for Security, which reflects the deep integration of AI into modern cybersecurity measures.

Program Enhancements and New Specializations

Enhancements to the Solutions Partner program, including new certified software designations, are facilitating greater specialization, allowing partners to differentiate themselves in key sectors such as healthcare, retail, finance, manufacturing, and sustainability. These sectors are increasingly dependent on cloud and AI technologies to drive efficiency, customer engagement, and innovation. By focusing on these areas, Microsoft is not only expanding its reach but also empowering its partners to gain a competitive edge.

Market Forecasts and Industry Adoption

The market forecasts for AI and cloud computing are bullish, projecting significant growth driven by the twin engines of technological advancement and industry adoption. Organizations are leveraging cloud infrastructure and AI to improve decision-making, automate processes, and deliver personalized services. Consequently, this generates additional opportunities for partners who can provide tailored solutions.

Issues in AI and Cloud Industries

While the future looks bright, the integration of AI and cloud solutions is not without its challenges. Issues such as data privacy, security, ethical use of AI, and the demand for skilled professionals remain at the forefront of industry discussions. Organizations and their partners must navigate these complex issues while ensuring compliance with regulatory standards and fostering public trust in AI applications.

Supporting the SMB Market and Partner Growth

Microsofts commitment to the SMB sector is evident through its strategy to unveil bespoke pathways for Azure and security solutions. By providing SMB-focused solution providers with essential resources, Microsoft is tackling the unique challenges faced by smaller enterprises, often in need of scalable and cost-effective solutions that do not compromise on functionality.

Furthermore, the tech giant is on track to introduce new partner designations aimed at enhancing training and support services. This move is poised to provide partners with exclusive resources and recognition, helping them to stand out in a crowded marketplace.

Conclusion

In an ever-changing digital space, Microsofts program updates are leading a transformative shiftwhere once AI was a burgeoning concept; it now takes center stage in large-scale implementation efforts. The partners of Microsoft are equipped to supply high-tech solutions and drive growth across various domains, an indication that the future of cloud and AI within the partner ecosystem is not just promisingits already unfolding.

For additional insights and information, feel free to visit Microsofts official website at Microsoft for a more comprehensive understanding of their investments and advancements in AI and cloud technologies.

Iwona Majkowska is a prominent figure in the tech industry, renowned for her expertise in new technologies, artificial intelligence, and solid-state batteries. Her work, often at the forefront of innovation, provides critical insights into the development and application of cutting-edge AI solutions and the evolution of energy storage technologies. Majkowskas contributions are pivotal in shaping the future of sustainable energy and intelligent systems, making her a respected voice in both academic and industrial circles. Her articles and research papers are a valuable resource for professionals and enthusiasts alike, seeking to understand the impact and potential of these transformative technologies.

Continued here:
Microsoft Expands Its Partner Ecosystem with AI and Cloud Advancements - yTech

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Ansys, Cadence, Hexagon, Microsoft, Rockwell Automation, Siemens, Trimble Adopt Omniverse Technologies to Help Customers Design, Simulate, Build and Operate Physically Based Digital Twins

GTCNVIDIA today announced thatNVIDIA Omniverse Cloudwill be available as APIs, extending the reach of the worlds leading platform for creating industrial digital twin applications and workflows across the entire ecosystem of software makers.

The five new Omniverse Cloud application programming interfaces enable developers to easily integrate core Omniverse technologies directly into existing design and automation software applications for digital twins, or their simulation workflows for testing and validating autonomous machines like robots or self-driving vehicles.

Some of the worlds largest industrial software makers that are embracing Omniverse Cloud APIs into their software portfolios include: Ansys, Cadence, Dassault Systmes for its 3DEXCITE brand, Hexagon, Microsoft, Rockwell Automation, Siemens and Trimble.

Everything manufactured will have digital twins, said Jensen Huang, founder and CEO of NVIDIA. Omniverse is the operating system for building and operating physically realistic digital twins. Omniverse and generative AI are the foundational technologies to digitalize the $50 trillion heavy industries market.

New Omniverse Cloud APIs The five new Omniverse Cloud APIs, which can be used individually or collectively, include:

Bringing Interactive Visualization and Collaboration to Industrial Applications Siemens, a leading technology company for automation, digitalization and sustainability, is adopting Omniverse Cloud APIs within its Siemens Xcelerator Platform, starting with Teamcenter X, the industry-leading cloud-based product lifecycle management (PLM) software.

In his GTC keynote, HuangshowedTeamcenter X connected to Omniverse APIs, giving the software the ability to connect design data to NVIDIA generative AI APIs, and use Omniverse RTX rendering directly inside the app.

Through the NVIDIA Omniverse API, Siemens empowers customers with generative AI to make their physics-based digital twins even more immersive, said Roland Busch, president and CEO of Siemens AG. This will help everybody to design, build and test next-generation products, manufacturing processes and factories virtually before they are built in the physical world. By combining the real and the digital worlds, Siemens digital twin technology is enabling companies around the world to become more competitive, resilient and sustainable.

Ansys, a leader in engineering-simulation software, is adopting Omniverse Cloud APIs to enable data interoperability and RTX visualization in solutions such as Ansys AVxcelerate for autonomous vehicles, Ansys Perceive EM for 6G simulation, and NVIDIA accelerated solvers such as Ansys Fluent.

Cadence, a leading computational software provider, is adopting Omniverse Cloud APIs into its Cadence Reality Digital Twin Platform so enterprises can design, simulate and optimize data centers in a digital twin prior to physical build-out.

Dassault Systmes, a leader in virtual universes for sustainable innovation, is adopting Omniverse Cloud APIs and Shutterstock 3D AI Services to power generative storytelling in its 3DEXCITE applications for content creation.

Other examples include:

In ademo released at GTC, Microsoft and NVIDIA reveal early work with Hexagon and Rockwell Automation that showcases these advancements.

Accelerating Autonomous Machine Development As demand increases for robots, autonomous vehicles (AVs) and AI-based monitoring systems, developers are seeking to accelerate their end-to-end workflows.

Sensor data is critical for training, testing and validating full-stack autonomy, from perception to planning and control.

Omniverse Cloud APIs connect a rich developer ecosystem of simulation tools and applications such as Foretellixs Foretify Platform, CARLA, MathWorks and industry-leading sensor solution providers like FORVIA HELLA, Luminar, SICK AG and Sony Semiconductor Solutions to enable full-stack training and testing with high-fidelity, physically based sensor simulation.

Available first on Microsoft Azure, Omniverse Cloud APIs will be offered later this year to developers for use on self-hosted and managed NVIDIA accelerated systems.

The next era of industrial digitalization has arrived, said Andy Pratt, corporate vice president, Microsoft Emerging Technologies. With NVIDIA Omniverse APIs on Microsoft Azure, organizations across industries and around the world can connect, collaborate and enhance their existing tools to create the next wave of AI-enabled digital twins.

Transforming Industries With Omniverse Digital Twins The new cloud APIs complement the broad adoption of Omniverse by a range of global leaders across industries, including:

To learn more aboutNVIDIA Omniverse, watch theGTC keynote. Register free to attendOmniverse sessionsfrom NVIDIA and industry leaders at GTC, which runs through March 21.

Read more from the original source:
NVIDIA Announces Omniverse Cloud APIs to Power Wave of Industrial Digital Twin Software Tools - NVIDIA Blog

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