Dec
15
2020
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Twitter taps AWS for its latest foray into the public cloud

Twitter has a lot going on, and it’s not always easy to manage that kind of scale on your own. Today, Amazon announced that Twitter has signed a multi-year agreement with AWS to run its real-time timelines. It’s a major win for Amazon’s cloud arm.

While the companies have worked together in some capacity for over a decade, this marks the first time that Twitter is tapping AWS to help run its core timelines.

“This expansion onto AWS marks the first time that Twitter is leveraging the public cloud to scale their real-time service. Twitter will rely on the breadth and depth of AWS, including capabilities in compute, containers, storage and security, to reliably deliver the real-time service with the lowest latency, while continuing to develop and deploy new features to improve how people use Twitter,” the company explained in the announcement.

Parag Agrawal, chief technology officer at Twitter, sees this as a way to expand and improve the company’s real-time offerings by taking advantage of AWS’s network of data centers to deliver content closer to the user. “The collaboration with AWS will improve performance for people who use Twitter by enabling us to serve Tweets from data centers closer to our customers at the same time as we leverage the Arm-based architecture of AWS Graviton2 instances. In addition to helping us scale our infrastructure, this work with AWS enables us to ship features faster as we apply AWS’s diverse and growing portfolio of services,” Agrawal said in a statement.

It’s worth noting that Twitter also has a relationship with Google Cloud. In 2018, it announced it was moving its Hadoop clusters to GCP.

This announcement could be considered a case of the rich getting richer as AWS is the leader in the cloud infrastructure market by far, with around 33% market share. Microsoft is in second with around 18% and Google is in third with 9%, according to Synergy Research. In its most recent earnings report, Amazon reported $11.6 billion in AWS revenue, putting it on a run rate of over $46 billion.

Dec
08
2020
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AWS expands on SageMaker capabilities with end-to-end features for machine learning

Nearly three years after it was first launched, Amazon Web Services’ SageMaker platform has gotten a significant upgrade in the form of new features, making it easier for developers to automate and scale each step of the process to build new automation and machine learning capabilities, the company said.

As machine learning moves into the mainstream, business units across organizations will find applications for automation, and AWS is trying to make the development of those bespoke applications easier for its customers.

“One of the best parts of having such a widely adopted service like SageMaker is that we get lots of customer suggestions which fuel our next set of deliverables,” said AWS vice president of machine learning, Swami Sivasubramanian. “Today, we are announcing a set of tools for Amazon SageMaker that makes it much easier for developers to build end-to-end machine learning pipelines to prepare, build, train, explain, inspect, monitor, debug and run custom machine learning models with greater visibility, explainability and automation at scale.”

Already companies like 3M, ADP, AstraZeneca, Avis, Bayer, Capital One, Cerner, Domino’s Pizza, Fidelity Investments, Lenovo, Lyft, T-Mobile and Thomson Reuters are using SageMaker tools in their own operations, according to AWS.

The company’s new products include Amazon SageMaker Data Wrangler, which the company said was providing a way to normalize data from disparate sources so the data is consistently easy to use. Data Wrangler can also ease the process of grouping disparate data sources into features to highlight certain types of data. The Data Wrangler tool contains more than 300 built-in data transformers that can help customers normalize, transform and combine features without having to write any code.

Amazon also unveiled the Feature Store, which allows customers to create repositories that make it easier to store, update, retrieve and share machine learning features for training and inference.

Another new tool that Amazon Web Services touted was Pipelines, its workflow management and automation toolkit. The Pipelines tech is designed to provide orchestration and automation features not dissimilar from traditional programming. Using pipelines, developers can define each step of an end-to-end machine learning workflow, the company said in a statement. Developers can use the tools to re-run an end-to-end workflow from SageMaker Studio using the same settings to get the same model every time, or they can re-run the workflow with new data to update their models.

To address the longstanding issues with data bias in artificial intelligence and machine learning models, Amazon launched SageMaker Clarify. First announced today, this tool allegedly provides bias detection across the machine learning workflow, so developers can build with an eye toward better transparency on how models were set up. There are open-source tools that can do these tests, Amazon acknowledged, but the tools are manual and require a lot of lifting from developers, according to the company.

Other products designed to simplify the machine learning application development process include SageMaker Debugger, which enables developers to train models faster by monitoring system resource utilization and alerting developers to potential bottlenecks; Distributed Training, which makes it possible to train large, complex, deep learning models faster than current approaches by automatically splitting data across multiple GPUs to accelerate training times; and SageMaker Edge Manager, a machine learning model management tool for edge devices, which allows developers to optimize, secure, monitor and manage models deployed on fleets of edge devices.

Last but not least, Amazon unveiled SageMaker JumpStart, which provides developers with a searchable interface to find algorithms and sample notebooks so they can get started on their machine learning journey. The company said it would give developers new to machine learning the option to select several pre-built machine learning solutions and deploy them into SageMaker environments.

Dec
03
2020
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AWS expands startup assistance program

Last year, AWS launched the APN Global Startup Program, which is sort of AWS’s answer to an incubator for mid to late-stage startups deeply involved with AWS technology. This year, the company wants to expand that offering, and today it announced some updates to the program at the Partner keynote at AWS re:Invent.

While startups technically have to pay a $2,500 fee if they are accepted to the program, AWS typically refunds that fee, says Doug Yeum, head of the Global Partner Organization at AWS — and they get a lot of benefits for being part of the program.

“While the APN has a $2,500 annual program fee, startups that are accepted into the invite-only APN Global Startup Program get that fee back, as well as free access to substantial additional resources both in terms of funding as well as exclusive program partner managers and co-sell specialists resources,” Yeum told TechCrunch.

And those benefits are pretty substantial, including access to a new “white glove program” that lets them work with a program manager with direct knowledge of AWS and who has experience working with startups. In addition, participants get access to an ISV program to work more directly with these vendors to increase sales and access to data exchange services to move third-party data into the AWS cloud.

What’s more, they can apply to the new AI/ML Acceleration program. As AWS describes it, “This includes up to $5,000 AWS credits to fund experiments on AWS services, enabling startups to explore AWS AI/ML tools that offer the best fit for them at low risk.”

Finally, they get partially free access to the AWS Marketplace, offsetting the normal marketplace listing fees for the first five offerings. Some participants will also get access to AWS sales to help use the power of the large company to drive a startup’s sales.

While you can apply to the program, the company also recruits individual startups that catch its attention. “We also proactively invite mid to late-stage startups built on AWS that, based on market signals, are showing traction and offer interesting use cases for our mutual enterprise customers,” Yeum explained.

Among the companies currently involved in the program are HashiCorp, Logz.io and Snapdocs. Interested startups can apply on the APN Global Startup website.

Dec
01
2020
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AWS updates its edge computing solutions with new hardware and Local Zones

AWS today closed out its first re:Invent keynote with a focus on edge computing. The company launched two smaller appliances for its Outpost service, which originally brought AWS as a managed service and appliance right into its customers’ existing data centers in the form of a large rack. Now, the company is launching these smaller versions so that its users can also deploy them in their stores or office locations. These appliances are fully managed by AWS and offer 64 cores of compute, 128GB of memory and 4TB of local NVMe storage.

In addition, the company expanded its set of Local Zones, which are basically small extensions of existing AWS regions that are more expensive to use but offer low-latency access in metro areas. This service launched in Los Angeles in 2019 and starting today, it’s also available in preview in Boston, Houston and Miami. Soon, it’ll expand to Atlanta, Chicago, Dallas, Denver, Kansas City, Las Vegas, Minneapolis, New York, Philadelphia, Phoenix, Portland and Seattle. Google, it’s worth noting, is doing something similar with its Mobile Edge Cloud.

The general idea here — and that’s not dissimilar from what Google, Microsoft and others are now doing — is to bring AWS to the edge and to do so in a variety of form factors.

As AWS CEO Andy Jassy rightly noted, AWS always believed that the vast majority of companies, “in the fullness of time” (Jassy’s favorite phrase from this keynote), would move to the cloud. Because of this, AWS focused on cloud services over hybrid capabilities early on. He argues that AWS watched others try and fail in building their hybrid offerings, in large parts because what customers really wanted was to use the same control plane on all edge nodes and in the cloud. None of the existing solutions from other vendors, Jassy argues, got any traction (though AWSs competitors would surely deny this) because of this.

The first result of that was VMware Cloud on AWS, which allowed customers to use the same VMware software and tools on AWS they were already familiar with. But at the end of the day, that was really about moving on-premises services to the cloud.

With Outpost, AWS launched a fully managed edge solution that can run AWS infrastructure in its customers’ data centers. It’s been an interesting journey for AWS, but the fact that the company closed out its keynote with this focus on hybrid — no matter how it wants to define it — shows that it now understands that there is clearly a need for this kind of service. The AWS way is to extend AWS into the edge — and I think most of its competitors will agree with that. Microsoft tried this early on with Azure Stack and really didn’t get a lot of traction, as far as I’m aware, but it has since retooled its efforts around Azure Arc. Google, meanwhile, is betting big on Anthos.

Dec
01
2020
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AWS adds natural language search service for business intelligence from its data sets

When Amazon Web Services launched QuickSight, its business intelligence service, back in 2016 the company wanted to provide product information and customer information for business users — not just developers.

At the time, the natural language processing technologies available weren’t robust enough to give customers the tools to search databases effectively using queries in plain speech.

Now, as those technologies have matured, Amazon is coming back with a significant upgrade called QuickSight Q, which allows users to just ask a simple question and get the answers they need, according to Andy Jassy’s keynote at AWS re:Invent.

“We will provide natural language to provide what we think the key learning is,” said Jassy. “I don’t like that our users have to know which databases to access or where data is stored. I want them to be able to type into a search bar and get the answer to a natural language question.

That’s what QuickSight Q aims to do. It’s a direct challenge to a number of business intelligence startups and another instance of the way machine learning and natural language processing are changing business processes across multiple industries.

“The way Q works. Type in a question in natural language [like]… ‘Give me the trailing twelve month sales of product X?’… You get an answer in seconds. You don’t have to know tables or have to know data stores.”

It’s a compelling use case and gets at the way AWS is integrating machine learning to provide more no-code services to customers. “Customers didn’t hire us to do machine learning,” Jassy said. “They hired us to answer the questions.”

Nov
02
2020
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AWS launches its next-gen GPU instances

AWS today announced the launch of its newest GPU-equipped instances. Dubbed P4d, these new instances are launching a decade after AWS launched its first set of Cluster GPU instances. This new generation is powered by Intel Cascade Lake processors and eight of Nvidia’s A100 Tensor Core GPUs. These instances, AWS promises, offer up to 2.5x the deep learning performance of the previous generation — and training a comparable model should be about 60% cheaper with these new instances.

Image Credits: AWS

For now, there is only one size available, the p4d.24xlarge instance, in AWS slang, and the eight A100 GPUs are connected over Nvidia’s NVLink communication interface and offer support for the company’s GPUDirect interface as well.

With 320 GB of high-bandwidth GPU memory and 400 Gbps networking, this is obviously a very powerful machine. Add to that the 96 CPU cores, 1.1 TB of system memory and 8 TB of SSD storage and it’s maybe no surprise that the on-demand price is $32.77 per hour (though that price goes down to less than $20/hour for one-year reserved instances and $11.57 for three-year reserved instances.

Image Credits: AWS

On the extreme end, you can combine 4,000 or more GPUs into an EC2 UltraCluster, as AWS calls these machines, for high-performance computing workloads at what is essentially a supercomputer-scale machine. Given the price, you’re not likely to spin up one of these clusters to train your model for your toy app anytime soon, but AWS has already been working with a number of enterprise customers to test these instances and clusters, including Toyota Research Institute, GE Healthcare and Aon.

“At [Toyota Research Institute], we’re working to build a future where everyone has the freedom to move,” said Mike Garrison, Technical Lead, Infrastructure Engineering at TRI. “The previous generation P3 instances helped us reduce our time to train machine learning models from days to hours and we are looking forward to utilizing P4d instances, as the additional GPU memory and more efficient float formats will allow our machine learning team to train with more complex models at an even faster speed.”

Oct
09
2020
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How Roblox completely transformed its tech stack

Picture yourself in the role of CIO at Roblox in 2017.

At that point, the gaming platform and publishing system that launched in 2005 was growing fast, but its underlying technology was aging, consisting of a single data center in Chicago and a bunch of third-party partners, including AWS, all running bare metal (nonvirtualized) servers. At a time when users have precious little patience for outages, your uptime was just two nines, or less than 99% (five nines is considered optimal).

Unbelievably, Roblox was popular in spite of this, but the company’s leadership knew it couldn’t continue with performance like that, especially as it was rapidly gaining in popularity. The company needed to call in the technology cavalry, which is essentially what it did when it hired Dan Williams in 2017.

Williams has a history of solving these kinds of intractable infrastructure issues, with a background that includes a gig at Facebook between 2007 and 2011, where he worked on the technology to help the young social network scale to millions of users. Later, he worked at Dropbox, where he helped build a new internal network, leading the company’s move away from AWS, a major undertaking involving moving more than 500 petabytes of data.

When Roblox approached him in mid-2017, he jumped at the chance to take on another major infrastructure challenge. While they are still in the midst of the transition to a new modern tech stack today, we sat down with Williams to learn how he put the company on the road to a cloud-native, microservices-focused system with its own network of worldwide edge data centers.

Scoping the problem

Sep
22
2020
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Microsoft challenges Twilio with the launch of Azure Communication Services

Microsoft today announced the launch of Azure Communication Services, a new set of features in its cloud that enable developers to add voice and video calling, chat and text messages to their apps, as well as old-school telephony.

The company describes the new set of services as the “first fully managed communication platform offering from a major cloud provider,” and that seems right, given that Google and AWS offer some of these features, including the AWS notification service, for example, but not as part of a cohesive communication service. Indeed, it seems Azure Communication Service is more of a competitor to the core features of Twilio or up-and-coming MessageBird.

Over the course of the last few years, Microsoft has built up a lot of experience in this area, in large parts thanks to the success of its Teams service. Unsurprisingly, that’s something Microsoft is also playing up in its announcement.

“Azure Communication Services is built natively on top a global, reliable cloud — Azure. Businesses can confidently build and deploy on the same low latency global communication network used by Microsoft Teams to support over 5 billion meeting minutes in a single day,” writes Scott Van Vliet, corporate vice president for Intelligent Communication at the company.

Microsoft also stresses that it offers a set of additional smart services that developers can tap into to build out their communication services, including its translation tools, for example. The company also notes that its services are encrypted to meet HIPPA and GDPR standards.

Like similar services, developers access the various capabilities through a set of new APIs and SDKs.

As for the core services, the capabilities here are pretty much what you’d expect. There’s voice and video calling (and the ability to shift between them). There’s support for chat and, starting in October, users will also be able to send text messages. Microsoft says developers will be able to send these to users anywhere, with Microsoft positioning it as a global service.

Provisioning phone numbers, too, is part of the services and developers will be able to provision those for in-bound and out-bound calls, port existing numbers, request new ones and — most importantly for contact-center users — integrate them with existing on-premises equipment and carrier networks.

“Our goal is to meet businesses where they are and provide solutions to help them be resilient and move their business forward in today’s market,” writes Van Vliet. “We see rich communication experiences – enabled by voice, video, chat, and SMS – continuing to be an integral part in how businesses connect with their customers across devices and platforms.”

Jul
14
2020
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Google Cloud’s new BigQuery Omni will let developers query data in GCP, AWS and Azure

At its virtual Cloud Next ’20 event, Google today announced a number of updates to its cloud portfolio, but the private alpha launch of BigQuery Omni is probably the highlight of this year’s event. Powered by Google Cloud’s Anthos hybrid-cloud platform, BigQuery Omni allows developers to use the BigQuery engine to analyze data that sits in multiple clouds, including those of Google Cloud competitors like AWS and Microsoft Azure — though for now, the service only supports AWS, with Azure support coming later.

Using a unified interface, developers can analyze this data locally without having to move data sets between platforms.

“Our customers store petabytes of information in BigQuery, with the knowledge that it is safe and that it’s protected,” said Debanjan Saha, the GM and VP of Engineering for Data Analytics at Google Cloud, in a press conference ahead of today’s announcement. “A lot of our customers do many different types of analytics in BigQuery. For example, they use the built-in machine learning capabilities to run real-time analytics and predictive analytics. […] A lot of our customers who are very excited about using BigQuery in GCP are also asking, ‘how can they extend the use of BigQuery to other clouds?’ ”

Image Credits: Google

Google has long said that it believes that multi-cloud is the future — something that most of its competitors would probably agree with, though they all would obviously like you to use their tools, even if the data sits in other clouds or is generated off-platform. It’s the tools and services that help businesses to make use of all of this data, after all, where the different vendors can differentiate themselves from each other. Maybe it’s no surprise then, given Google Cloud’s expertise in data analytics, that BigQuery is now joining the multi-cloud fray.

“With BigQuery Omni customers get what they wanted,” Saha said. “They wanted to analyze their data no matter where the data sits and they get it today with BigQuery Omni.”

Image Credits: Google

He noted that Google Cloud believes that this will help enterprises break down their data silos and gain new insights into their data, all while allowing developers and analysts to use a standard SQL interface.

Today’s announcement is also a good example of how Google’s bet on Anthos is paying off by making it easier for the company to not just allow its customers to manage their multi-cloud deployments but also to extend the reach of its own products across clouds. This also explains why BigQuery Omni isn’t available for Azure yet, given that Anthos for Azure is still in preview, while AWS support became generally available in April.

Jul
09
2020
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Docker partners with AWS to improve container workflows

Docker and AWS today announced a new collaboration that introduces a deep integration between Docker’s Compose and Desktop developer tools and AWS’s Elastic Container Service (ECS) and ECS on AWS Fargate. Previously, the two companies note, the workflow to take Compose files and run them on ECS was often challenging for developers. Now, the two companies simplified this process to make switching between running containers locally and on ECS far easier.

docker/AWS architecture overview“With a large number of containers being built using Docker, we’re very excited to work with Docker to simplify the developer’s experience of building and deploying containerized applications to AWS,” said Deepak Singh, the VP for compute services at AWS. “Now customers can easily deploy their containerized applications from their local Docker environment straight to Amazon ECS. This accelerated path to modern application development and deployment allows customers to focus more effort on the unique value of their applications, and less time on figuring out how to deploy to the cloud.”

In a bit of a surprise move, Docker last year sold off its enterprise business to Mirantis to solely focus on cloud-native developer experiences.

“In November, we separated the enterprise business, which was very much focused on operations, CXOs and a direct sales model, and we sold that business to Mirantis,” Docker CEO Scott Johnston told TechCrunch’s Ron Miller earlier this year. “At that point, we decided to focus the remaining business back on developers, which was really Docker’s purpose back in 2013 and 2014.”

Today’s move is an example of this new focus, given that the workflow issues this partnership addresses had been around for quite a while already.

It’s worth noting that Docker also recently engaged in a strategic partnership with Microsoft to integrate the Docker developer experience with Azure’s Container Instances.

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