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Microsoft makes a push for service mesh interoperability

Published May 21, 2019

Services meshes. They are the hot new thing in the cloud native computing world. At KubeCon, the bi-annual festival of all things cloud native, Microsoft today announced that it is teaming up with a number of companies in this space to create a generic service mesh interface. This will make it easier for developers to adopt the concept without locking them into a specific technology.

In a world where the number of network endpoints continues to increase as developers launch new micro-services, containers and other systems at a rapid clip, they are making the network smarter again by handling encryption, traffic management and other functions so that the actual applications don’t have to worry about that. With a number of competing service mesh technologies, though, including the likes of Istio and Linkerd, developers currently have to choose which one of these to support.

“I’m really thrilled to see that we were able to pull together a pretty broad consortium of folks from across the industry to help us drive some interoperability in the service mesh space,” Gabe Monroy, Microsoft’s lead product manager for containers and the former CTO of Deis, told me. “This is obviously hot technology — and for good reasons. The cloud-native ecosystem is driving the need for smarter networks and smarter pipes and service mesh technology provides answers.”

The partners here include Buoyant, HashiCorp, Solo.io, Red Hat, AspenMesh, Weaveworks, Docker, Rancher, Pivotal, Kinvolk and VMware . That’s a pretty broad coalition, though it notably doesn’t include cloud heavyweights like Google, the company behind Istio, and AWS.

“In a rapidly evolving ecosystem, having a set of common standards is critical to preserving the best possible end-user experience,” said Idit Levine, founder and CEO of Solo.io. “This was the vision behind SuperGloo — to create an abstraction layer for consistency across different meshes, which led us to the release of Service Mesh Hub last week. We are excited to see service mesh adoption evolve into an industry-level initiative with the SMI specification.”

For the time being, the interoperability features focus on traffic policy, telemetry and traffic management. Monroy argues that these are the most pressing problems right now. He also stressed that this common interface still allows the different service mesh tools to innovate and that developers can always work directly with their APIs when needed. He also stressed that the Service Mesh Interface (SMI), as this new specification is called, does not provide any of its own implementations of these features. It only defines a common set of APIs.

Currently, the most well-known service mesh is probably Istio, which Google, IBM and Lyft launched about two years ago. SMI may just bring a bit more competition to this market since it will allow developers to bet on the overall idea of a service mesh instead of a specific implementation.

In addition to SMI, Microsoft also today announced a couple of other updates around its cloud-native and Kubernetes services. It announced the first alpha of the Helm 3 package manager, for example, as well as the 1.0 release of its Kubernetes extension for Visual Studio Code and the general availability of its AKS virtual nodes, using the open source Virtual Kubelet project.

Quadric.io raises $15M to build a plug-and-play supercomputer for autonomous systems

Published May 20, 2019

Quadric.io, a startup founded by some of the folks behind the once-secretive bitcoin mining operation “21E6,” has raised $15 million in a Series A round that will fund the development of a supercomputer designed for autonomous systems.

The round was led by automotive Tier 1 supplier DENSO and its semiconductor products arm NSITEXE, which will also be one of Quadric.io’s customers for future electronic systems in all levels of autonomous driving solutions. Leawood VC also participated in the Series A round.

The company says it will use the injection of capital to build out its product and hire more people, as well as business development.

Pear, Uncork Capital, SV Angel, Cota Capital and Trucks VC are seed investors in Quadric.io.

The roots of Quadric.io grew from a seemingly disconnected mission to produce an agricultural robot designed to transform the way vineyards were managed. The company launched in 2016 by CEO Veerbhan Kheterpal, CTO Nigel Drego and CPO Daniel Firu — all co-founders of 21 Inc. The bitcoin startup, once known as 21E6, would later rebrand as Earn.com before being acquired by Coinbase for $100 million.

Quadric’s original plan was stymied by some real-world fundamentals. The power-hungry ag robot was weighed down by batteries that became too unwieldy to move amongst vineyard rows and the processing time to turn loads of environmental data into actual actions based on algorithms were too slow.

Quadric was looking for a chip designed for processing on the edge and that supported decision making in real time — all while crunching data faster and sipping, not slurping power. That need grew into Quadric’s core product today: a supercomputer that the company says hits that sweet spot of increased computational speed and reduced power consumption.

Kheterpal noted in a recent post on Medium that Intel’s CPUs work “very well for standard computer processing” and Nvidia’s GPUs have “ushered in astounding new graphics processing for gaming and much more.” But, he argued, Quadric needed something neither of those companies could provide: a chip designed for processing on the edge.

The company created a single unified architecture in the supercomputer that enables high-performance computing and artificial intelligence. The supercomputer, which is built around the Quadric Processor, is plug-and-play. This means people can plug in their sensor set and build their entire application to support “near-instantaneous” decision making, Quadric says. The company claims that early testing of Quadric’s system has shown up to 100 times lower latency and a 90% reduction in power consumption.

Quadric designed the instruction set, chip architecture and system architecture of the chip. System-level manufacturing is done at a contract manufacturer in Santa Clara, Calif., while chip manufacturing and assembly is done in Asia.

Quadric argues this underlying technology is a prerequisite for companies developing autonomous systems that will be used in the construction, transportation, agriculture and warehousing industries. The underlying tech that supports autonomous machines used in these industries either lacks the performance or solves only a small part of the full application, according to Quadric.

The startup contends that machines with autonomous functions require processing speed and responsiveness “on the edge” — meaning at the machine level, not in the cloud.

Other companies, most recently Tesla, have opted to build their own chips to meet this specific need. But as Kheterpal notes, not all companies have the resources to build the tech from the ground up.

“ Quadric is a plug and play option that eliminates the need for building heterogeneous systems with significant hardware and software integration costs — thereby taking years off of product development roadmaps,” Kheterpal wrote.

Unveiling its latest cohort, Alchemist announces $4 million in funding for its enterprise accelerator

Published May 16, 2019

The enterprise software and services-focused accelerator Alchemist has raised $4 million in fresh financing from investors BASF and the Qatar Development Bank, just in time for its latest demo day unveiling 20 new companies.

Qatar and BASF join previous investors, including the venture firms Mayfield, Khosla Ventures, Foundation Capital, DFJ and USVP, and corporate investors like Cisco, Siemens and Juniper Networks.

While the roster of successes from Alchemist’s fund isn’t as lengthy as Y Combinator, the accelerator program has launched the likes of the quantum computing upstart Rigetti, the soft-launch developer tool LaunchDarkly and drone startup Matternet .

Some (personal) highlights of the latest cohort include:

Bayware: Helmed by a former head of software-defined networking from Cisco, the company is pitching a tool that makes creating networks in multi-cloud environments as easy as copying and pasting.
MotorCortex.AI: Co-founded by a Stanford engineering professor and a Carnegie Mellon roboticist, the company is using computer vision, machine learning and robotics to create a fruit packer for packaging lines. Starting with avocados, the company is aiming to tackle the entire packaging side of pick and pack in logistics.
Resilio: With claims of a 96% effectiveness rate and $35,000 in annual recurring revenue with another $1 million in the pipeline, Resilio is already seeing companies embrace its mobile app that uses a phone’s camera to track stress levels and application-based prompts on how to lower it, according to Alchemist.
Operant Networks: It’s a long-held belief (of mine) that if computing networks are already irrevocably compromised, the best thing that companies and individuals can do is just encrypt the hell out of their data. Apparently Operant agrees with me. The company is claiming 50% time savings with this approach, and have booked $1.9 million in 2019 as proof, according to Alchemist.
HPC Hub: HPC Hub wants to democratize access to supercomputers by overlaying a virtualization layer and pre-installed software on underutilized super computers to give more companies and researchers easier access to machines… and they’ve booked $92,000 worth of annual recurring revenue.
DinoPlusAI: This chip developer is designing a low latency chip for artificial intelligence applications, reducing latency by 12 times over a competing Nvidia chip, according to the company. DinoPlusAI sees applications for its tech in things like real-time AI markets and autonomous driving. Its team is led by a designer from Cadence and Broadcom and the company already has $8 million in letters of intent signed, according to Alchemist.
Aero Systems West: Co-founders from the Air Force’s Research Labs and MIT are aiming to take humans out of drone operations and maintenance. The company contends that for every hour of flight time, drones require seven hours of maintenance and check ups. Aero Systems aims to reduce that by using remote analytics, self-inspection, autonomous deployment and automated maintenance to take humans out of the drone business.

Watch a live stream of Alchemist’s demo day pitches, starting at 3PM, here.

Reality Check: The marvel of computer vision technology in today’s camera-based AR systems

Published May 15, 2019

Alex Chuang
Contributor

Alex Chuang is the Managing Partner of Shape Immersive, a boutique studio that helps enterprise and brands transform their businesses by incorporating VR/AR solutions into their strategies.

Part 1: How do computers know where it is in the world? (Localization)

Mars Rover Curiosity taking a selfie on Mars. Source: https://www.nasa.gov/jpl/msl/pia19808/looking-up-at-mars-rover-curiosity-in-buckskin-selfie/

When NASA scientists put the rover onto Mars, they needed a way for the robot to navigate itself on a different planet without the use of a global positioning system (GPS). They came up with a technique called Visual Inertial Odometry (VIO) to track the rover’s movement over time without GPS. This is the same technique that our smartphones use to track their spatial position and orientation.

A VIO system is made out of two parts.

Google makes travel planning easier

Published May 14, 2019

Google today announced a major revamp of its travel planning tools on the web. After launching a similar set of tools on mobile last year, the company today announced that google.com/travel on the web will now let you see information about all of your previously reserved trips and easily switch between flight, hotel and package searches.

In many ways, this finally brings all of Google’s travel services under one hood — a process that has taken far longer than I would’ve anticipated after Google bought ITA nine years ago.

Google Trips is essentially the landing page for the new site and brings together your existing bookings and information about your destination. The service will then feed your travel information back into Google Search and Maps. To do this, Google.com/travel (which I think we can safely call Google Travel, even if Google itself doesn’t do so), will use the confirmation emails and receipts from your Gmail inbox to build the timeline of your trip.

Because both the web and mobile versions are now on feature parity, this also makes it easier to pick up your trip planning on any device. Like always, though, you won’t be able to make any reservations through Google’s systems. Instead, Google will send you to an airline’s or hotel’s reservation system to complete a booking.

The actual flight and hotel search engines are still the same, though if Google previously offered the ability to buy flight and hotel packages, it did a good job of hiding that. Now, this option gets first billing, together with the hotel and flight searches.

“Our goal is to simplify trip planning by helping you quickly find the most useful information and pick up where you left off on any device. We’ll continue to make planning and taking trips easier with Google Maps, Google Search and google.com/travel—so you can get out and enjoy the world.”

Sadly, Google hasn’t ported Inbox’s useful Trip Bundles over to Gmail yet, though, despite promises to do so before shutting down Inbox. For the time being, the new Google Travel site is a pretty good alternative.

Microsoft wants you to work less

Published May 6, 2019

Microsoft today announced updates to its MyAnalytics platform and a new Outlook feature that are meant to help you work less, find more time to focus on the work that actually matters and, by extension, get more downtime.

Until now, for example, MyAnalytics, Microsoft’s tool for helping employees track their productivity, would provide you with a measure of how much time you spent working after hours. That’s not necessarily a healthy number to track. Going forward, MyAnalytics will track the number of days you managed to unplug after work and didn’t check your email or work on a document at 8pm (something Microsoft’s own PR department could learn from given that it has a tendency to provide essential press materials for next-day embargoes at 6:30pm). The idea here, obviously, is to get employees to focus on this number instead of how much they work when they are off the clock.

“Our customers often tell us they spend all day in meetings with little time to focus on pressing tasks and projects,” Microsoft communications chief Frank X. Shaw also noted in a press briefing ahead of today’s announcement.

To combat this, the company today launched a few new features that will let you set up regular “focus time.” The first of this is a tool that lets you set up focus time each week, as well as a feature in Microsoft teams that will alert your fellow employees when you are trying to get things done.

Because your colleagues often don’t care about your flow, though, and are prone to scheduling yet another unnecessary meeting during those times, Microsoft is also launching a new AI-powered Outlook plugin that will help you rebook your focus time and find times for focusing on specific to-do items.

In the future, the company also plans to introduce well-being, networking and collaboration plans.

Focus plans will become available in preview in the next few months for Microsoft 365 and Office 365 users, with E5 customers getting them first.

Microsoft launches a drag-and-drop machine learning tool

Published May 2, 2019

Microsoft today announced three new services that all aim to simplify the process of machine learning. These range from a new interface for a tool that completely automates the process of creating models, to a new no-code visual interface for building, training and deploying models, all the way to hosted Jupyter-style notebooks for advanced users.

Getting started with machine learning is hard. Even to run the most basic of experiments takes a good amount of expertise. All of these new tools greatly simplify this process by hiding away the code or giving those who want to write their own code a pre-configured platform for doing so.

The new interface for Azure’s automated machine learning tool makes creating a model as easy as importing a data set and then telling the service which value to predict. Users don’t need to write a single line of code, while in the backend, this updated version now supports a number of new algorithms and optimizations that should result in more accurate models. While most of this is automated, Microsoft stresses that the service provides “complete transparency into algorithms, so developers and data scientists can manually override and control the process.”

For those who want a bit more control from the get-go, Microsoft also today launched into preview a visual interface for its Azure Machine Learning service that will allow developers to build, train and deploy machine learning models without having to touch any code.

This tool, the Azure Machine Learning visual interface, looks suspiciously like the existing Azure ML Studio, Microsoft’s first stab at building a visual machine learning tool. Indeed, the two services look identical. The company never really pushed this service, though, and almost seemed to have forgotten about it despite the fact that it always seemed like a really useful tool for getting started with machine learning.

Microsoft says this new version combines the best of Azure ML Studio with the Azure Machine Learning service. In practice, this means that while the interface is almost identical, the Azure Machine Learning visual interface extends what was possible with ML Studio by running on top of the Azure Machine Learning service and adding that services’ security, deployment and life cycle management capabilities.

The service provides an easy interface for cleaning up your data, training models with the help of different algorithms, evaluating them and, finally, putting them into production.

While these first two services clearly target novices, the new hosted notebooks in Azure Machine Learning are clearly geared toward the more experienced machine learning practitioner. The notebooks come pre-packaged with support for the Azure Machine Learning Python SDK and run in what the company describes as a “secure, enterprise-ready environment.” While using these notebooks isn’t trivial either, this new feature allows developers to quickly get started without the hassle of setting up a new development environment with all the necessary cloud resources.

Microsoft announces the $3,500 HoloLens 2 Development Edition

Published May 2, 2019

As part of its rather odd Thursday afternoon pre-Build news dump, Microsoft today announced the HoloLens 2 Development Edition. The company announced the much-improved HoloLens 2 at MWC Barcelona earlier this year, but it’s not shipping to developers yet. Currently, the best release date we have is “later this year.” The Development Edition will launch alongside the regular HoloLens 2.

The Development Edition, which will retail for $3,500 to own outright or on a $99 per month installment plan, doesn’t feature any special hardware. Instead, it comes with $500 in Azure credits and three-month trials of Unity Pro and the Unity PiXYZ plugin for bringing engineering renderings into Unity.

To get the Development Edition, potential buyers have to join the Microsoft Mixed Reality Developer Program and those who already pre-ordered the standard edition will be able to change their order later this year.

As far as HoloLens news goes, that’s all a bit underwhelming. Anybody can get free Azure credits, after all (though usually only $200) and free trials of Unity Pro are also readily available (though typically limited to 30 days).

Oddly, the regular HoloLens 2 was also supposed to cost $3,500. It’s unclear if the regular edition will now be somewhat cheaper, cost the same but come without the credits or really why Microsoft is doing this at all. Turning this into a special “Development Edition” feels more like a marketing gimmick than anything else, as well as an attempt to bring some of the futuristic glamour of the HoloLens visor to today’s announcements.

The folks at Unity are clearly excited, though. “Pairing HoloLens 2 with Unity’s real-time 3D development platform enables businesses to accelerate innovation, create immersive experiences, and engage with industrial customers in more interactive ways,” says Tim McDonough, GM of Industrial at Unity, in today’s announcement. “The addition of Unity Pro and PiXYZ Plugin to HoloLens 2 Development Edition gives businesses the immediate ability to create real-time 2D, 3D, VR, and AR interactive experiences while allowing for the importing and preparation of design data to create real-time experiences.”

Microsoft also today noted that Unreal Engine 4 support for HoloLens 2 will become available by the end of May.

Microsoft brings Azure SQL Database to the edge (and Arm)

Published May 2, 2019

Microsoft today announced an interesting update to its database lineup with the preview of Azure SQL Database Edge, a new tool that brings the same database engine that powers Azure SQL Database in the cloud to edge computing devices, including, for the first time, Arm-based machines.

Azure SQL Edge, Azure corporate vice president Julia White writes in today’s announcement, “brings to the edge the same performant, secure and easy to manage SQL engine that our customers love in Azure SQL Database and SQL Server.”

The new service, which will also run on x64-based devices and edge gateways, promises to bring low-latency analytics to edge devices as it allows users to work with streaming data and time-series data, combined with the built-in machine learning capabilities of Azure SQL Database. Like its larger brethren, Azure SQL Database Edge will also support graph data and comes with the same security and encryption features that can, for example, protect the data at rest and in motion, something that’s especially important for an edge device.

As White rightly notes, this also ensures that developers only have to write an application once and then deploy it to platforms that feature Azure SQL Database, good old SQL Server on premises and this new edge version.

SQL Database Edge can run in both connected and fully disconnected fashion, something that’s also important for many use cases where connectivity isn’t always a given, yet where users need the kind of data analytics capabilities to keep their businesses (or drilling platforms, or cruise ships) running.

Couchbase’s mobile database gets built-in ML and enhanced synchronization features

Published May 2, 2019

Couchbase, the company behind the eponymous NoSQL database, announced a major update to its mobile database today that brings some machine learning smarts, as well as improved synchronization features and enhanced stats and logging support, to the software.

“We’ve led the innovation and data management at the edge since the release of our mobile database five years ago,” Couchbase’s VP of Engineering Wayne Carter told me. “And we’re excited that others are doing that now. We feel that it’s very, very important for businesses to be able to utilize these emerging technologies that do sit on the edge to drive their businesses forward, and both making their employees more effective and their customer experience better.”

The latter part is what drove a lot of today’s updates, Carter noted. He also believes that the database is the right place to do some machine learning. So with this release, the company is adding predictive queries to its mobile database. This new API allows mobile apps to take pre-trained machine learning models and run predictive queries against the data that is stored locally. This would allow a retailer to create a tool that can use a phone’s camera to figure out what part a customer is looking for.

To support these predictive queries, Couchbase mobile is also getting support for predictive indexes. “Predictive indexes allow you to create an index on prediction, enabling correlation of real-time predictions with application data in milliseconds,” Carter said. In many ways, that’s also the unique value proposition for bringing machine learning into the database. “What you really need to do is you need to utilize the unique values of a database to be able to deliver the answer to those real-time questions within milliseconds,” explained Carter.

The other major new feature in this release is delta synchronization, which allows businesses to push far smaller updates to the databases on their employees’ mobile devices. That’s because they only have to receive the information that changed instead of a full updated database. Carter says this was a highly requested feature, but until now, the company always had to prioritize work on other components of Couchbase.

This is an especially useful feature for the company’s retail customers, a vertical where it has been quite successful. These users need to keep their catalogs up to data and quite a few of them supply their employees with mobile devices to help shoppers. Rumor has it that Apple, too, is a Couchbase user.

The update also includes a few new features that will be more of interest to operators, including advanced stats reporting and enhanced logging support.