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Amazon releases Kendra to solve enterprise search with AI and machine learning

Enterprise search has always been a tough nut to crack. The Holy Grail has always been to operate like Google, but in-house. You enter a few keywords and you get back that nearly perfect response at the top of the list of the results. The irony of trying to do search locally has been a lack of content.

While Google has the universe of the World Wide Web to work with, enterprises have a much narrower set of responses. It would be easy to think that should make it easier to find the ideal response, but the fact is that it’s the opposite. The more data you have, the more likely you’ll find the correct document.

Amazon is trying to change the enterprise search game by putting it into a more modern machine learning-driven context to use today’s technology to help you find that perfect response just as you typically do on the web.

Today the company announced the general availability of Amazon Kendra, its cloud enterprise search product that the company announced last year at AWS re:Invent. It uses natural language processing to allow the user to simply ask a question, then searches across the repositories connected to the search engine to find a precise answer.

“Amazon Kendra reinvents enterprise search by allowing end-users to search across multiple silos of data using real questions (not just keywords) and leverages machine learning models under the hood to understand the content of documents and the relationships between them to deliver the precise answers they seek (instead of a random list of links),” the company described the new service in a statement.

AWS has tuned the search engine for specific industries including IT, healthcare and insurance. It promises energy, industrial, financial services, legal, media and entertainment, travel and hospitality, human resources, news, telecommunications, mining, food and beverage and automotive will be coming later this year.

This means any company in one of those industries should have a head start when it comes to searching because the system will understand the language specific to those verticals. You can drop your Kendra search box into an application or a website, and it has features like type ahead you would expect in a tool like this.

Enterprise search has been around for a long time, but perhaps by bringing AI and machine learning to bear on it, we can finally solve it once and for all.

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AWS hits $10B for the quarter putting it on a $40B run rate

AWS, the cloud arm of Amazon, would be a pretty successful business on its own. Today, the company announced it has passed $10 billion for the quarter, putting the cloud business on an impressive run rate of more than $40 billion.

It was a bright spot for the company in an earnings report that saw it report net income of $2.5 billion, down $1 billion from a year ago.

Still, most companies would take that for the entire business, but AWS, which started off as kind of a side hustle for Amazon back in 2006, has grown into a powerful business all on its own. With a growth rate of 33%, it’s still growing briskly, even if it’s slowing down a bit as the law of large numbers begins to work against it.

Even though Microsoft has grown more quickly — in yesterday’s report Microsoft reported that Azure was growing at a 59% clip — AWS had such a big head start and controls a big chunk of the market share.

To give you a sense of how quickly this business has grown, Bloomberg’s Jon Erlichman tweeted the Q1 numbers for AWS since 2014, and it’s pretty amazing growth:

Amazon’s cloud revenue in Q1:
(Amazon Web Services)

Q1 2020: $10.2 billion
Q1 2019: $7.7 billion
Q1 2018: $5.4 billion
Q1 2017: $3.7 billion
Q1 2016: $2.6 billion
Q1 2015: $1.6 billion
Q1 2014: $1.1 billion

— Jon Erlichman (@JonErlichman) April 30, 2020

In 2014, it was a $4 billion a year business. Today it is 9.1x that and still going strong. The good news for everyone involved is that this is a huge market, and while nobody could ever characterize the pandemic and it’s economic fall-out as good news for anyone, the fact is that it is forcing companies to move to the cloud faster than they might have wanted to go.

That should bode well for all the cloud infrastructures vendors, even as the economy shrinks, the kinds of services these vendors offer should be in more demand than ever, and that means these numbers could just keep growing for some time.

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AWS launches Amazon AppFlow, its new SaaS integration service

AWS today launched Amazon AppFlow, a new integration service that makes it easier for developers to transfer data between AWS and SaaS applications like Google Analytics, Marketo, Salesforce, ServiceNow, Slack, Snowflake and Zendesk. Like similar services, including Microsoft Azure’s Power Automate, for example, developers can trigger these flows based on specific events, at pre-set times or on-demand.

Unlike some of its competitors, though, AWS is positioning this service more as a data transfer service than a way to automate workflows, and, while the data flow can be bi-directional, AWS’s announcement focuses mostly on moving data from SaaS applications to other AWS services for further analysis. For this, AppFlow also includes a number of tools for transforming the data as it moves through the service.

“Developers spend huge amounts of time writing custom integrations so they can pass data between SaaS applications and AWS services so that it can be analysed; these can be expensive and can often take months to complete,” said AWS principal advocate Martin Beeby in today’s announcement. “If data requirements change, then costly and complicated modifications have to be made to the integrations. Companies that don’t have the luxury of engineering resources might find themselves manually importing and exporting data from applications, which is time-consuming, risks data leakage, and has the potential to introduce human error.”

Every flow (which AWS defines as a call to a source application to transfer data to a destination) costs $0.001 per run, though, in typical AWS fashion, there’s also cost associated with data processing (starting at 0.02 per GB).

“Our customers tell us that they love having the ability to store, process, and analyze their data in AWS. They also use a variety of third-party SaaS applications, and they tell us that it can be difficult to manage the flow of data between AWS and these applications,” said Kurt Kufeld, vice president, AWS. “Amazon AppFlow provides an intuitive and easy way for customers to combine data from AWS and SaaS applications without moving it across the public internet. With Amazon AppFlow, our customers bring together and manage petabytes, even exabytes, of data spread across all of their applications — all without having to develop custom connectors or manage underlying API and network connectivity.”

At this point, the number of supported services remains comparatively low, with only 14 possible sources and four destinations (Amazon Redshift and S3, as well as Salesforce and Snowflake). Sometimes, depending on the source you select, the only possible destination is Amazon’s S3 storage service.

Over time, the number of integrations will surely increase, but for now, it feels like there’s still quite a bit more work to do for the AppFlow team to expand the list of supported services.

AWS has long left this market to competitors, even though it has tools like AWS Step Functions for building serverless workflows across AWS services and EventBridge for connections applications. Interestingly, EventBridge currently supports a far wider range of third-party sources, but as the name implies, its focus is more on triggering events in AWS than moving data between applications.

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Google Cloud’s fully managed Anthos is now generally available for AWS

A year ago, back in the days of in-person conferences, Google officially announced the launch of its Anthos multi-cloud application modernization platform at its Cloud Next conference. The promise of Anthos was always that it would allow enterprises to write their applications once, package them into containers and then manage their multi-cloud deployments across GCP, AWS, Azure and their on-prem data centers.

Until now, support for AWS and Azure was only available in preview, but today, the company is making support for AWS and on-premises generally available. Microsoft Azure support remains in preview, though.

“As an AWS customer now, or a GCP customer, or a multi-cloud customer, […] you can now run Anthos on those environments in a consistent way, so you don’t have to learn any proprietary APIs and be locked in,” Eyal Manor, the GM and VP of engineering in charge of Anthos, told me. “And for the first time, we enable the portability between different infrastructure environments as opposed to what has happened in the past where you were locked into a set of APIs.”

Manor stressed that Anthos was designed to be multi-cloud from day one. As for why AWS support is launching ahead of Azure, Manor said that there was simply more demand for it. “We surveyed the customers and they said, ‘hey, we want, in addition to GCP, we want AWS,’ ” he said. But support for Azure will come later this year and the company already has a number of preview customers for it. In addition, Anthos will also come to bare metal servers in the future.

Looking even further ahead, Manor also noted that better support for machine learning workloads is on the way. Many businesses, after all, want to be able to update and run their models right where their data resides, no matter what cloud that may be. There, too, the promise of Anthos is that developers can write the application once and then run it anywhere.

“I think a lot of the initial response and excitement was from the developer audiences,” Jennifer Lin, Google Cloud’s VP of product management, told me. “Eric Brewer had led a white paper that we did to say that a lot of the Anthos architecture sort of decouples the developer and the operator stakeholder concerns. There hadn’t been a multi-cloud shared software architecture where we could do that and still drive emerging and existing applications with a common shared software stack.”

She also noted that a lot of Google Cloud’s ecosystem partners endorsed the overall Anthos architecture early on because they, too, wanted to be able to write once and run anywhere — and so do their customers.

Plaid is one of the launch partners for these new capabilities. “Our customers rely on us to be always available and as a result we have very high reliability requirements,” said Naohiko Takemura, Plaid’s head of engineering. “We pursued a multi-cloud strategy to ensure redundancy for our critical KARTE service. Google Cloud’s Anthos works seamlessly across GCP and our other cloud providers preventing any business disruption. Thanks to Anthos, we prevent vendor lock-in, avoid managing cloud-specific infrastructure, and our developers are not constrained by cloud providers.”

With this release, Google Cloud is also bringing deeper support for virtual machines to Anthos, as well as improved policy and configuration management.

Over the next few months, the Anthos Service Mesh will also add support for applications that run in traditional virtual machines. As Lin told me, “a lot of this is is about driving better agility and taking the complexity out of it so that we have abstractions that work across any environment, whether it’s legacy or new or on-prem or AWS or GCP.”

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AWS and Facebook launch an open-source model server for PyTorch

AWS and Facebook today announced two new open-source projects around PyTorch, the popular open-source machine learning framework. The first of these is TorchServe, a model-serving framework for PyTorch that will make it easier for developers to put their models into production. The other is TorchElastic, a library that makes it easier for developers to build fault-tolerant training jobs on Kubernetes clusters, including AWS’s EC2 spot instances and Elastic Kubernetes Service.

In many ways, the two companies are taking what they have learned from running their own machine learning systems at scale and are putting this into the project. For AWS, that’s mostly SageMaker, the company’s machine learning platform, but as Bratin Saha, AWS VP and GM for Machine Learning Services, told me, the work on PyTorch was mostly motivated by requests from the community. And while there are obviously other model servers like TensorFlow Serving and the Multi Model Server available today, Saha argues that it would be hard to optimize those for PyTorch.

“If we tried to take some other model server, we would not be able to quote optimize it as much, as well as create it within the nuances of how PyTorch developers like to see this,” he said. AWS has lots of experience in running its own model servers for SageMaker that can handle multiple frameworks, but the community was asking for a model server that was tailored toward how they work. That also meant adapting the server’s API to what PyTorch developers expect from their framework of choice, for example.

As Saha told me, the server that AWS and Facebook are now launching as open source is similar to what AWS is using internally. “It’s quite close,” he said. “We actually started with what we had internally for one of our model servers and then put it out to the community, worked closely with Facebook, to iterate and get feedback — and then modified it so it’s quite close.”

Bill Jia, Facebook’s VP of AI Infrastructure, also told me, he’s very happy about how his team and the community has pushed PyTorch forward in recent years. “If you look at the entire industry community — a large number of researchers and enterprise users are using AWS,” he said. “And then we figured out if we can collaborate with AWS and push PyTorch together, then Facebook and AWS can get a lot of benefits, but more so, all the users can get a lot of benefits from PyTorch. That’s our reason for why we wanted to collaborate with AWS.”

As for TorchElastic, the focus here is on allowing developers to create training systems that can work on large distributed Kubernetes clusters where you might want to use cheaper spot instances. Those are preemptible, though, so your system has to be able to handle that, while traditionally, machine learning training frameworks often expect a system where the number of instances stays the same throughout the process. That, too, is something AWS originally built for SageMaker. There, it’s fully managed by AWS, though, so developers never have to think about it. For developers who want more control over their dynamic training systems or to stay very close to the metal, TorchElastic now allows them to recreate this experience on their own Kubernetes clusters.

AWS has a bit of a reputation when it comes to open source and its engagement with the open-source community. In this case, though, it’s nice to see AWS lead the way to bring some of its own work on building model servers, for example, to the PyTorch community. In the machine learning ecosystem, that’s very much expected, and Saha stressed that AWS has long engaged with the community as one of the main contributors to MXNet and through its contributions to projects like Jupyter, TensorFlow and libraries like NumPy.

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Pileus helps businesses cut their cloud spend

Israel-based Pileus, which is officially launching today, aims to help businesses keep their cloud spend under control. The company also today announced that it has raised a $1 million seed round from a private angel investor.

Using machine learning, the company’s platform continuously learns about how a user typically uses a given cloud and then provides forecasts and daily personalized recommendations to help them stay within a budget.

Pileus currently supports AWS, with support for Google Cloud and Microsoft Azure coming soon.

With all of the information it gathers about your cloud usage, the service can also monitor usage for any anomalies. Because, at its core, Pileus keeps a detailed log of all your cloud spend, it also can provide detailed reports and dashboards of what a user is spending on each project and resource.

If you’ve ever worked on a project like this, you know that these reports are only as good as the tags you use to identify each project and resource, so Pileus makes that a priority on its platform, with a tagging tool that helps enforce tagging policies.

“My team and I spent many sleepless nights working on this solution,” says Pileus CEO Roni Karp. “We’re thrilled to finally be able to unleash Pileus to the masses and help everyone gain more efficiency of their cloud experience while helping them understand their usage and costs better than ever before.”

Pileus currently offers a free 30-day trial. After that, the service shows you a $180/month or $800 per year price, but once you connect your accounts, it’ll charge 1% of your savings, not the default pricing you’ll see at first.

The company isn’t just focused on individual businesses, though. It’s also targeting managed service providers that can use the platform to create reports and manage their own customer billing. Karp believes this will become a significant source of revenue for Pileus because “there are not many good tools in the field today, especially for Azure.”

It’s no secret that Pileus is launching into a crowded market, where well-known incumbents like Cloudability already share mindshare with a growing number of startups. Karp, however, believes that Pileus can stand out, largely because of its machine learning platform and its ability to provide users with immediate value, whereas, he argues, it often takes several weeks for other platforms to deliver results.

 

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Tech giants should let startups defer cloud payments

Google, Amazon and Microsoft are the landlords. Amidst the coronavirus economic crisis, startups need a break from paying rent. They’re in a cash crunch. Revenue has stopped flowing in, capital markets like venture debt are hesitant and startups and small-to-medium sized businesses are at risk of either having to lay off huge numbers of employees and/or shut down.

Meanwhile, the tech giants are cash rich. Their success this decade means they’re able to weather the storm for a few months. Their customers cannot.

Cloud infrastructure costs area amongst many startups’ top expense besides payroll. The option to pay these cloud bills later could save some from going out of business or axing huge parts of their staff. Both would hurt the tech industry, the economy and the individuals laid off. But most worryingly for the giants, it could destroy their customer base.

The mass layoffs have already begun. Soon we’re sure to start hearing about sizable companies shutting down, upended by COVID-19. But there’s still an opportunity to stop a larger bloodbath from ensuing.

That’s why I have a proposal: cloud relief.

The platform giants should let startups and small businesses defer their cloud infrastructure payments for three to six months until they can pay them back in installments. Amazon AWS, Google Cloud, Microsoft Azure, these companies’ additional infrastructure products, and other platform providers should let customers pause payment until the worst of the first wave of the COVID-19 economic disruption passes. Profitable SaaS providers like Salesforce could give customers an extension too.

There are plenty of altruistic reasons to do this. They have the resources to help businesses in need. We all need to support each other in these tough times. This could protect tons of families. Some of these startups are providing important services to the public and even discounting them, thereby ramping up their bills while decreasing revenue.

Then there are the PR reasons. After years of techlash and anti-trust scrutiny, here’s the chance for the giants to prove their size can be beneficial to the world. Recruiters could use it as a talking point. “We’re the company that helped save Silicon Valley.” There’s an explanation for them squirreling away so much cash: the rainy day has finally arrived.

But the capitalistic truth and the story they could sell to Wall Street is that it’s not good for our business if our customers go out of business. Look at what happened to infrastructure providers in the dot-com crash. When tons of startups vaporized, so did the profits for those selling them hosting and tools. Any government stimulus for businesses would be better spent by them paying employees than paying the cloud companies that aren’t in danger. Saving one future Netflix from shutting down could cover any short-term loss from helping 100 other businesses.

This isn’t a handout. These startups will still owe the money. They’d just be able to pay it a little later, spread out over their monthly bills for a year or so. Once mass shelter-in-place orders subside, businesses can operate at least a little closer to normal, investors can get less cautious and customers will have the cash they need to pay their dues. Plus interest, if necessary.

Meanwhile, they’ll be locked in and loyal customers for the foreseeable future. Cloud vendors could gate the deferment to only customers that have been with them for X amount of months or that have already spent Y amount on the platform. The vendors also could offer the deferment on the condition that customers add a year or more to their existing contracts. Founders will remember who gave them the benefit of the doubt.

cloud ice cream cone imagine

Consider it a marketing expense. Platforms often offer discounts or free trials to new customers. Now it’s existing customers that need a reprieve. Instead of airport ads, the giants could spend the money ensuring they’ll still have plenty of developers building atop them by the end of 2020.

Beyond deferred payment, platforms could just push the due date on all outstanding bills to three or six months from now. Alternatively, they could offer a deep discount such as 50% off for three months if they didn’t want to deal with accruing debt and then servicing it. Customers with multi-year contracts could offered the opportunity to downgrade or renegotiate their contracts without penalties. Any of these might require giving sales quota forgiveness to their account executives.

It would likely be far too complicated and risky to accept equity in lieu of cash, a cut of revenue going forward or to provide loans or credit lines to customers. The clearest and simplest solution is to let startups skip a few payments, then pay more every month later until they clear their debt. When asked for comment or about whether they’re considering payment deferment options, Microsoft declined, and Amazon and Google did not respond.

To be clear, administering payment deferment won’t be simple or free. There are sure to be holes that cloud economists can poke in this proposal, but my goal is to get the conversation started. It could require the giants to change their earnings guidance. Rewriting deals with significantly sized customers will take work on both ends, and there’s a chance of breach of contract disputes. Giants would face the threat of customers recklessly using cloud resources before shutting down or skipping town.

Most taxing would be determining and enforcing the criteria of who’s eligible. The vendors would need to lay out which customers are too big so they don’t accidentally give a cloud-intensive but healthy media company a deferment they don’t need. Businesses that get questionably excluded could make a stink in public. Executing on the plan will require staff when giants are stretched thin trying to handle logistics disruptions, misinformation and accelerating work-from-home usage.

Still, this is the moment when the fortunate need to lend a hand to the vulnerable. Not a hand out, but a hand up. Companies with billions in cash in their coffers could save those struggling to pay salaries. All the fundraisers and info centers and hackathons are great, but this is how the tech giants can live up to their lofty mission statements.

We all live in the cloud now. Don’t evict us. #CloudRelief

Thanks to Falon Fatemi, Corey Quinn, Ilya Fushman, Jason Kim, Ilya Sukhar and Michael Campbell for their ideas and feedback on this proposal.

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Big opening for startups that help move entrenched on-prem workloads to the cloud

AWS CEO Andy Jassy showed signs of frustration at his AWS re:Invent keynote address in December.

Customers weren’t moving to the cloud nearly fast enough for his taste, and he prodded them to move along. Some of their hesitation, as Jassy pointed out, was due to institutional inertia, but some of it also was due to a technology problem related to getting entrenched, on-prem workloads to the cloud.

When a challenge of this magnitude presents itself and you have the head of the world’s largest cloud infrastructure vendor imploring customers to move faster, you can be sure any number of players will start paying attention.

Sure enough, cloud infrastructure vendors (ISVs) have developed new migration solutions to help break that big data logjam. Large ISVs like Accenture and Deloitte are also happy to help your company deal with migration issues, but this opportunity also offers a big opening for startups aiming to solve the hard problems associated with moving certain workloads to the cloud.

Think about problems like getting data off of a mainframe and into the cloud or moving an on-prem data warehouse. We spoke to a number of experts to figure out where this migration market is going and if the future looks bright for cloud-migration startups.

Cloud-migration blues

It’s hard to nail down exactly the percentage of workloads that have been moved to the cloud at this point, but most experts agree there’s still a great deal of growth ahead. Some of the more optimistic projections have pegged it at around 20%, with the U.S. far ahead of the rest of the world.

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AWS launches Bottlerocket, a Linux-based OS for container hosting

AWS has launched its own open-source operating system for running containers on both virtual machines and bare metal hosts. Bottlerocket, as the new OS is called, is basically a stripped-down Linux distribution that’s akin to projects like CoreOS’s now-defunct Container Linux and Google’s container-optimized OS. The OS is currently in its developer preview phase, but you can test it as an Amazon Machine Image for EC2 (and by extension, under Amazon EKS, too).

As AWS chief evangelist Jeff Barr notes in his announcement, Bottlerocket supports Docker images and images that conform to the Open Container Initiative image format, which means it’ll basically run all Linux-based containers you can throw at it.

One feature that makes Bottlerocket stand out is that it does away with a package-based update system. Instead, it uses an image-based model that, as Barr notes, “allows for a rapid & complete rollback if necessary.” The idea here is that this makes updates easier. At the core of this update process is “The Update Framework,” an open-source project hosted by the Cloud Native Computing Foundation.

AWS says it will provide three years of support (after General Availability) for its own builds of Bottlerocket. As of now, the project is very much focused on AWS, of course, but the code is available on GitHub and chances are we will see others expand on AWS’ work.

The company is launching the project in cooperation with a number of partners, including Alcide, Armory, CrowdStrike, Datadog, New Relic, Sysdig, Tigera, Trend Micro and Waveworks.

“Container-optimized operating systems will give dev teams the additional speed and efficiency to run higher throughput workloads with better security and uptime,” said Michael Gerstenhaber, director of Product Management at Datadog.” We are excited to work with AWS on Bottlerocket, so that as customers take advantage of the increased scale they can continue to monitor these ephemeral environments with confidence.”

 

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How much should a startup spend on security?

One of the questions I frequently ask startup founders is how much they’re spending on security. Unsurprisingly, everyone has a different answer.

Startups and small companies are invariably faced with the prospect that they’re either not spending enough or are spending too much on something that’s hard to quantify in terms of value. It’s a tough sell to sink money into an effort to stop something that might one day happen, particularly for bootstrapped startups that must make every cent count — yet we’re told security is a crucial investment for a company’s future.

Sorry to break it to you, but there is no easy answer.

The reality is that each company is different and there is no single recommended dollar amount to spend. But it’s absolutely certain that some investment is required. We know because we see a lot of security incidents here at TechCrunch — hacks, breaches and especially data exposures, often a result of human error.

We spoke to three security experts — a head of security, a security entrepreneur and a cybersecurity fellow — to understand the questions facing startups.

Know and understand your threat model

Every company has a different threat model — by that, we mean identifying risks and possible ways of attack before they happen. Companies that store tons of user data may be a greater target than companies that don’t. Each firm needs to evaluate which kind of risks they face and identify weaknesses.

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