His most profound loves are for CRPGs, immersive sims, and any game whose ambition outstrips its budget. He thinks you're all far too mean about Deus Ex: Invisible War. Open menu Close menu PC Gamer PC Gamer THE GLOBAL AUTHORITY ON PC GAMES. opens in new tab opens in new tab opens in new tab opens in new tab opens in new tab opens in new tab. US Edition. News Reviews Hardware Best Of Magazine The Top Forum More PCGaming Show Podcasts Coupons Newsletter SignUp Community Guidelines Affiliate Links Meet the team About PC Gamer.

Popular WoW: Dragonflight Darktide Midnight Suns Holiday gifts Warzone 2. Audio player loading…. PC Gamer Newsletter Sign up to get the best content of the week, and great gaming deals, as picked by the editors. Contact me with news and offers from other Future brands Receive email from us on behalf of our trusted partners or sponsors. Do you anticipate a higher percentage of customer workloads moving back on premises than you maybe would have three years ago? Absolutely not. We're a big enough business, if you asked me have you ever seen X, I could probably find one of anything, but the absolute dominant trend is customers dramatically accelerating their move to the cloud.

Moving internal enterprise IT workloads like SAP to the cloud, that's a big trend. Creating new analytics capabilities that many times didn't even exist before and running those in the cloud.

More startups than ever are building innovative new businesses in AWS. Our public-sector business continues to grow, serving both federal as well as state and local and educational institutions around the world.

It really is still day one. The opportunity is still very much in front of us, very much in front of our customers, and they continue to see that opportunity and to move rapidly to the cloud.

In general, when we look across our worldwide customer base, we see time after time that the most innovation and the most efficient cost structure happens when customers choose one provider, when they're running predominantly on AWS. A lot of benefits of scale for our customers, including the expertise that they develop on learning one stack and really getting expert, rather than dividing up their expertise and having to go back to basics on the next parallel stack.

That being said, many customers are in a hybrid state, where they run IT in different environments. In some cases, that's by choice; in other cases, it's due to acquisitions, like buying companies and inherited technology. We understand and embrace the fact that it's a messy world in IT, and that many of our customers for years are going to have some of their resources on premises, some on AWS.

Some may have resources that run in other clouds. We want to make that entire hybrid environment as easy and as powerful for customers as possible, so we've actually invested and continue to invest very heavily in these hybrid capabilities. A lot of customers are using containerized workloads now, and one of the big container technologies is Kubernetes.

We have a managed Kubernetes service, Elastic Kubernetes Service, and we have a … distribution of Kubernetes Amazon EKS Distro that customers can take and run on their own premises and even use to boot up resources in another public cloud and have all that be done in a consistent fashion and be able to observe and manage across all those environments. So we're very committed to providing hybrid capabilities, including running on premises, including running in other clouds, and making the world as easy and as cost-efficient as possible for customers.

Can you talk about why you brought Dilip Kumar, who was Amazon's vice president of physical retail and tech, into AWS as vice president applications and how that will play out? He's a longtime, tenured Amazonian with many, many different roles — important roles — in the company over a many-year period. Dilip has come over to AWS to report directly to me, running an applications group. We do have more and more customers who want to interact with the cloud at a higher level — higher up the stack or more on the application layer.

We talked about Connect, our contact center solution, and we've also built services specifically for the healthcare industry like a data lake for healthcare records called Amazon HealthLake. We've built a lot of industrial services like IoT services for industrial settings, for example, to monitor industrial equipment to understand when it needs preventive maintenance. We have a lot of capabilities we're building that are either for … horizontal use cases like Amazon Connect or industry verticals like automotive, healthcare, financial services.

We see more and more demand for those, and Dilip has come in to really coalesce a lot of teams' capabilities, who will be focusing on those areas. You can expect to see us invest significantly in those areas and to come out with some really exciting innovations. Would that include going into CRM or ERP or other higher-level, run-your-business applications? I don't think we have immediate plans in those particular areas, but as we've always said, we're going to be completely guided by our customers, and we'll go where our customers tell us it's most important to go next.

It's always been our north star. Correction: This story was updated Nov. Bennett Richardson bennettrich is the president of Protocol. Prior to joining Protocol in , Bennett was executive director of global strategic partnerships at POLITICO, where he led strategic growth efforts including POLITICO's European expansion in Brussels and POLITICO's creative agency POLITICO Focus during his six years with the company. Prior to POLITICO, Bennett was co-founder and CMO of Hinge, the mobile dating company recently acquired by Match Group.

Bennett began his career in digital and social brand marketing working with major brands across tech, energy, and health care at leading marketing and communications agencies including Edelman and GMMB.

Bennett is originally from Portland, Maine, and received his bachelor's degree from Colgate University. Prior to joining Protocol in , he worked on the business desk at The New York Times, where he edited the DealBook newsletter and wrote Bits, the weekly tech newsletter. He has previously worked at MIT Technology Review, Gizmodo, and New Scientist, and has held lectureships at the University of Oxford and Imperial College London.

He also holds a doctorate in engineering from the University of Oxford. We launched Protocol in February to cover the evolving power center of tech. It is with deep sadness that just under three years later, we are winding down the publication. As of today, we will not publish any more stories.

All of our newsletters, apart from our flagship, Source Code, will no longer be sent. Source Code will be published and sent for the next few weeks, but it will also close down in December. Building this publication has not been easy; as with any small startup organization, it has often been chaotic.

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As companies expand their use of AI beyond running just a few machine learning models, and as larger enterprises go from deploying hundreds of models to thousands and even millions of models, ML practitioners say that they have yet to find what they need from prepackaged MLops systems. As companies expand their use of AI beyond running just a few machine learning models, ML practitioners say that they have yet to find what they need from prepackaged MLops systems.

Kate Kaye is an award-winning multimedia reporter digging deep and telling print, digital and audio stories. She covers AI and data for Protocol. Her reporting on AI and tech ethics issues has been published in OneZero, Fast Company, MIT Technology Review, CityLab, Ad Age and Digiday and heard on NPR.

Kate is the creator of RedTailMedia. org and is the author of "Campaign ' A Turning Point for Digital Media," a book about how the presidential campaigns used digital media and data.

On any given day, Lily AI runs hundreds of machine learning models using computer vision and natural language processing that are customized for its retail and ecommerce clients to make website product recommendations, forecast demand, and plan merchandising. And he said that while some MLops systems can manage a larger number of models, they might not have desired features such as robust data visualization capabilities or the ability to work on premises rather than in cloud environments.

As companies expand their use of AI beyond running just a few ML models, and as larger enterprises go from deploying hundreds of models to thousands and even millions of models, many machine learning practitioners Protocol interviewed for this story say that they have yet to find what they need from prepackaged MLops systems.

Companies hawking MLops platforms for building and managing machine learning models include tech giants like Amazon, Google, Microsoft, and IBM and lesser-known vendors such as Comet, Cloudera, DataRobot, and Domino Data Lab. It's actually a complex problem. Intuit also has constructed its own systems for building and monitoring the immense number of ML models it has in production, including models that are customized for each of its QuickBooks software customers.

The model must recognize those distinctions. For instance, Hollman said the company built an ML feature management platform from the ground up. For companies that have been forced to go DIY, building these platforms themselves does not always require forging parts from raw materials. DBS has incorporated open-source tools for coding and application security purposes such as Nexus, Jenkins, Bitbucket, and Confluence to ensure the smooth integration and delivery of ML models, Gupta said.

Intuit has also used open-source tools or components sold by vendors to improve existing in-house systems or solve a particular problem, Hollman said. However, he emphasized the need to be selective about which route to take. I think that the best AI will be a build plus buy. However, creating consistency through the ML lifecycle from model training to deployment to monitoring becomes increasingly difficult as companies cobble together open-source or vendor-built machine learning components, said John Thomas, vice president and distinguished engineer at IBM.

The reality is most people are not there, so you have a whole bunch of different tools. Companies struggling to find suitable off-the-shelf MLops platforms are up against another major challenge, too: finding engineering talent. Many companies do not have software engineers on staff with the level of expertise necessary to architect systems that can handle large numbers of models or accommodate millions of split-second decision requests, said Abhishek Gupta, founder and principal researcher at Montreal AI Ethics Institute and senior responsible AI leader and expert at Boston Consulting Group.

For one thing, smaller companies are competing for talent against big tech firms that offer higher salaries and better resources.

For companies with less-advanced AI operations, shopping at the existing MLops platform marketplace may be good enough, Hollman said. To give you the best possible experience, this site uses cookies. If you continue browsing. you accept our use of cookies. You can review our privacy policy to find out more about the cookies we use. Workplace Enterprise Fintech China Policy Newsletters Braintrust Podcast Events Careers About Us. Source Code. Cloud Computing. CX in the Enterprise. Enterprise Power Index.

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Best Futures Trading Strategies. Setelah itu Anda akan mendapat sekian persen hak kepemilikan sebuah perusahaan. Donna Goodison dgoodison is Protocol's senior reporter focusing on enterprise infrastructure technology, from the 'Big 3' cloud computing providers to data centers. Hair Growth. We're a big enough business, if you asked me have you ever seen X, I could probably find one of anything, but the absolute dominant trend is customers dramatically accelerating their move to the cloud. Best Student Loans.

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