Personal blog of Yzmir Ramirez

Like virtually every big enterprise company, a few years ago, the German auto giant Daimler decided to invest in its own on-premises data centers. And while those aren’t going away anytime soon, the company today announced that it has successfully moved its on-premises big data platform to Microsoft’s Azure cloud. This new platform, which the company calls eXtollo, is Daimler’s first major service to run outside of its own data centers, though it’ll probably not be the last.

As Daimler’s head of its corporate center of excellence for advanced analytics and big data Guido Vetter told me, the company started getting interested in big data about five years ago. “We invested in technology — the classical way, on-premise — and got a couple of people on it. And we were investigating what we could do with data because data is transforming our whole business as well,” he said.

By 2016, the size of the organization had grown to the point where a more formal structure was needed to enable the company to handle its data at a global scale. At the time, the buzz phrase was “data lakes” and the company started building its own in order to build out its analytics capacities.

“Sooner or later, we hit the limits as it’s not our core business to run these big environments,” Vetter said. “Flexibility and scalability are what you need for AI and advanced analytics and our whole operations are not set up for that. Our backend operations are set up for keeping a plant running and keeping everything safe and secure.” But in this new world of enterprise IT, companies need to be able to be flexible and experiment — and, if necessary, throw out failed experiments quickly.

So about a year and a half ago, Vetter’s team started the eXtollo project to bring all the company’s activities around advanced analytics, big data and artificial intelligence into the Azure Cloud, and just over two weeks ago, the team shut down its last on-premises servers after slowly turning on its solutions in Microsoft’s data centers in Europe, the U.S. and Asia. All in all, the actual transition between the on-premises data centers and the Azure cloud took about nine months. That may not seem fast, but for an enterprise project like this, that’s about as fast as it gets (and for a while, it fed all new data into both its on-premises data lake and Azure).

If you work for a startup, then all of this probably doesn’t seem like a big deal, but for a more traditional enterprise like Daimler, even just giving up control over the physical hardware where your data resides was a major culture change and something that took quite a bit of convincing. In the end, the solution came down to encryption.

“We needed the means to secure the data in the Microsoft data center with our own means that ensure that only we have access to the raw data and work with the data,” explained Vetter. In the end, the company decided to use the Azure Key Vault to manage and rotate its encryption keys. Indeed, Vetter noted that knowing that the company had full control over its own data was what allowed this project to move forward.

Vetter tells me the company obviously looked at Microsoft’s competitors as well, but he noted that his team didn’t find a compelling offer from other vendors in terms of functionality and the security features that it needed.

Today, Daimler’s big data unit uses tools like HD Insights and Azure Databricks, which covers more than 90 percents of the company’s current use cases. In the future, Vetter also wants to make it easier for less experienced users to use self-service tools to launch AI and analytics services.

While cost is often a factor that counts against the cloud, because renting server capacity isn’t cheap, Vetter argues that this move will actually save the company money and that storage costs, especially, are going to be cheaper in the cloud than in its on-premises data center (and chances are that Daimler, given its size and prestige as a customer, isn’t exactly paying the same rack rate that others are paying for the Azure services).

As with so many big data AI projects, predictions are the focus of much of what Daimler is doing. That may mean looking at a car’s data and error code and helping the technician diagnose an issue or doing predictive maintenance on a commercial vehicle. Interestingly, the company isn’t currently bringing to the cloud any of its own IoT data from its plants. That’s all managed in the company’s on-premises data centers because it wants to avoid the risk of having to shut down a plant because its tools lost the connection to a data center, for example.

Enterprises now amass huge amounts of data, both from their own tools and applications, as well as from the SaaS applications they use. For a long time, that data was basically exhaust. Maybe it was stored for a while to fulfill some legal requirements, but then it was discarded. Now, data is what drives machine learning models, and the more data you have, the better. It’s maybe no surprise, then, that the big cloud vendors started investing in data warehouses and lakes early on. But that’s just a first step. After that, you also need the analytics tools to make all of this data useful.

Today, it’s Microsoft turn to shine the spotlight on its data analytics services. The actual news here is pretty straightforward. Two of these are services that are moving into general availability: the second generation of Azure Data Lake Storage for big data analytics workloads and Azure Data Explorer, a managed service that makes easier ad-hoc analysis of massive data volumes. Microsoft is also previewing a new feature in Azure Data Factory, its graphical no-code service for building data transformation. Data Factory now features the ability to map data flows.

Those individual news pieces are interesting if you are a user or are considering Azure for your big data workloads, but what’s maybe more important here is that Microsoft is trying to offer a comprehensive set of tools for managing and storing this data — and then using it for building analytics and AI services.

“AI is a top priority for every company around the globe,” Julia White, Microsoft’s corporate VP for Azure, told me. “And as we are working with our customers on AI, it becomes clear that their analytics often aren’t good enough for building an AI platform.” These companies are generating plenty of data, which then has to be pulled into analytics systems. She stressed that she couldn’t remember a customer conversation in recent months that didn’t focus on AI. “There is urgency to get to the AI dream,” White said, but the growth and variety of data presents a major challenge for many enterprises. “They thought this was a technology that was separate from their core systems. Now it’s expected for both customer-facing and line-of-business applications.”

Data Lake Storage helps with managing this variety of data since it can handle both structured and unstructured data (and is optimized for the Spark and Hadoop analytics engines). The service can ingest any kind of data — yet Microsoft still promises that it will be very fast. “The world of analytics tended to be defined by having to decide upfront and then building rigid structures around it to get the performance you wanted,” explained White. Data Lake Storage, on the other hand, wants to offer the best of both worlds.

Likewise, White argued that while many enterprises used to keep these services on their on-premises servers, many of them are still appliance-based. But she believes the cloud has now reached the point where the price/performance calculations are in its favor. It took a while to get to this point, though, and to convince enterprises. White noted that for the longest time, enterprises that looked at their analytics projects thought $300 million projects took forever, tied up lots of people and were frankly a bit scary. “But also, what we had to offer in the cloud hasn’t been amazing until some of the recent work,” she said. “We’ve been on a journey — as well as the other cloud vendors — and the price performance is now compelling.” And it sure helps that if enterprises want to meet their AI goals, they’ll now have to tackle these workloads, too.

In 2016, Serkan Piantino packed up his desk at Facebook with hopes to move on to something new. The former director of Engineering for Facebook AI Research had every intention to keep working on AI, but quickly realized a huge issue.

Unless you’re under the umbrella of one of these big tech companies like Facebook, it can be very difficult and incredibly expensive to get your hands on the hardware necessary to run machine learning experiments.

So he built Spell, which today received $15 million in Series A funding led by Eclipse Ventures and Two Sigma Ventures.

Spell is a collaborative platform that lets anyone run machine learning experiments. The company connects clients with the best, newest hardware hosted by Google, AWS and Microsoft Azure and gives them the software interface they need to run, collaborate and build with AI.

“We spent decades getting to a laptop powerful enough to develop a mobile app or a website, but we’re struggling with things we develop in AI that we haven’t struggled with since the 70s,” said Piantino. “Before PCs existed, the computers filled the whole room at a university or NASA and people used terminals to log into a single main frame. It’s why Unix was invented, and that’s kind of what AI needs right now.”

In a meeting with Piantino this week, TechCrunch got a peek at the product. First, Piantino pulled out his MacBook and opened up Terminal. He began to run his own code against MNIST, which is a database of handwritten digits commonly used to train image detection algorithms.

He started the program and then moved over to the Spell platform. While the original program was just getting started, Spell’s cloud computing platform had completed the test in less than a minute.

The advantage here is obvious. Engineers who want to work on AI, either on their own or for a company, have a huge task in front of them. They essentially have to build their own computer, complete with the high-powered GPUs necessary to run their tests.

With Spell, the newest GPUs from Nvidia and Google are virtually available for anyone to run their tests.

Individual users can get on for free, specify the type of GPU they need to compute their experiment and simply let it run. Corporate users, on the other hand, are able to view the runs taking place on Spell and compare experiments, allowing users to collaborate on their projects from within the platform.

Enterprise clients can set up their own cluster, and keep all of their programs private on the Spell platform, rather than running tests on the public cluster.

Spell also offers enterprise customers a “spell hyper” command that offers built-in support for hyperparameter optimization. Folks can track their models and results and deploy them to Kubernetes/Kubeflow in a single click.

But perhaps most importantly, Spell allows an organization to instantly transform their model into an API that can be used more broadly throughout the organization, or used directly within an app or website.

The implications here are huge. Small companies and startups looking to get into AI now have a much lower barrier to entry, whereas large traditional companies can build out their own proprietary machine learning algorithms for use within the organization without an outrageous upfront investment.

Individual users can get on the platform for free, whereas enterprise clients can get started for $99/month per host you use over the course of a month. Piantino explains that Spell charges based on concurrent usage, so if the customer has 10 concurrent things running, the company considers that the “size” of the Spell cluster and charges based on that.

Piantino sees Spell’s model as the key to defensibility. Whereas many cloud platforms try to lock customers in to their entire suite of products, Spell works with any language framework and lets users plug and play on the platforms of their choice by simply commodifying the hardware. In fact, Spell doesn’t even share with clients which cloud cluster (Microsoft Azure, Google or AWS) they’re on.

So, on the one hand the speed of the tests themselves goes up based on access to new hardware, but, because Spell is an agnostic platform, there is also a huge advantage in how quickly one can get set up and start working.

The company plans to use the funding to further grow the team and the product, and Piantino says he has his eye out for top-tier engineering talent, as well as a designer.

The Pentagon is going to make one cloud vendor exceedingly happy when it chooses the winner of the $10 billion, ten-year enterprise cloud project dubbed the Joint Enterprise Defense Infrastructure (or JEDI for short). The contract is designed to establish the cloud technology strategy for the military over the next 10 years as it begins to take advantage of current trends like Internet of Things, artificial intelligence and big data.

Ten billion dollars spread out over ten years may not entirely alter a market that’s expected to reach $100 billion a year very soon, but it is substantial enough give a lesser vendor much greater visibility, and possibly deeper entree into other government and private sector business. The cloud companies certainly recognize that.

That could explain why they are tripping over themselves to change the contract dynamics, insisting, maybe rightly, that a multi-vendor approach would make more sense.

One look at the Request for Proposal (RFP) itself, which has dozens of documents outlining various criteria from security to training to the specification of the single award itself, shows the sheer complexity of this proposal. At the heart of it is a package of classified and unclassified infrastructure, platform and support services with other components around portability. Each of the main cloud vendors we’ll explore here offers these services. They are not unusual in themselves, but they do each bring a different set of skills and experiences to bear on a project like this.

It’s worth noting that it’s not just interested in technical chops, the DOD is also looking closely at pricing and has explicitly asked for specific discounts that would be applied to each component. The RFP process closes on October 12th and the winner is expected to be chosen next April.

Amazon

What can you say about Amazon? They are by far the dominant cloud infrastructure vendor. They have the advantage of having scored a large government contract in the past when they built the CIA’s private cloud in 2013, earning $600 million for their troubles. It offers GovCloud, which is the product that came out of this project designed to host sensitive data.

Many of the other vendors worry that gives them a leg up on this deal. While five years is a long time, especially in technology terms, if anything, Amazon has tightened control of the market. Heck, most of the other players were just beginning to establish their cloud business in 2013. Amazon, which launched in 2006, has maturity the others lack and they are still innovating, introducing dozens of new features every year. That makes them difficult to compete with, but even the biggest player can be taken down with the right game plan.

Microsoft

If anyone can take Amazon on, it’s Microsoft. While they were somewhat late the cloud they have more than made up for it over the last several years. They are growing fast, yet are still far behind Amazon in terms of pure market share. Still, they have a lot to offer the Pentagon including a combination of Azure, their cloud platform and Office 365, the popular business suite that includes Word, PowerPoint, Excel and Outlook email. What’s more they have a fat contract with the DOD for $900 million, signed in 2016 for Windows and related hardware.

Azure Stack is particularly well suited to a military scenario. It’s a private cloud you can stand up and have a mini private version of the Azure public cloud. It’s fully compatible with Azure’s public cloud in terms of APIs and tools. The company also has Azure Government Cloud, which is certified for use by many of the U.S. government’s branches, including DOD Level 5. Microsoft brings a lot of experience working inside large enterprises and government clients over the years, meaning it knows how to manage a large contract like this.

Google

When we talk about the cloud, we tend to think of the Big Three. The third member of that group is Google. They have been working hard to establish their enterprise cloud business since 2015 when they brought in Diane Greene to reorganize the cloud unit and give them some enterprise cred. They still have a relatively small share of the market, but they are taking the long view, knowing that there is plenty of market left to conquer.

They have taken an approach of open sourcing a lot of the tools they used in-house, then offering cloud versions of those same services, arguing that who knows better how to manage large-scale operations than they do. They have a point, and that could play well in a bid for this contract, but they also stepped away from an artificial intelligence contract with DOD called Project Maven when a group of their employees objected. It’s not clear if that would be held against them or not in the bidding process here.

IBM

IBM has been using its checkbook to build a broad platform of cloud services since 2013 when it bought Softlayer to give it infrastructure services, while adding software and development tools over the years, and emphasizing AI, big data, security, blockchain and other services. All the while, it has been trying to take full advantage of their artificial intelligence engine, Watson.

As one of the primary technology brands of the 20th century, the company has vast experience working with contracts of this scope and with large enterprise clients and governments. It’s not clear if this translates to its more recently developed cloud services, or if it has the cloud maturity of the others, especially Microsoft and Amazon. In that light, it would have its work cut out for it to win a contract like this.

Oracle

Oracle has been complaining since last spring to anyone who will listen, including reportedly the president, that the JEDI RFP is unfairly written to favor Amazon, a charge that DOD firmly denies. They have even filed a formal protest against the process itself.

That could be a smoke screen because the company was late to the cloud, took years to take it seriously as a concept, and barely registers today in terms of market share. What it does bring to the table is broad enterprise experience over decades and one of the most popular enterprise databases in the last 40 years.

It recently began offering a self-repairing database in the cloud that could prove attractive to DOD, but whether its other offerings are enough to help it win this contract remains to be to be seen.

AWS has its Snowball (and Snowmobile truck), Google Cloud has its data transfer appliance and Microsoft has its Azure Data Box. All of these are physical appliances that allow enterprises to ship lots of data to the cloud by uploading it into these machines and then shipping them to the cloud. Microsoft’s Azure Data Box launched into preview about a year ago and today, the company is announcing a number of updates and adding a few new boxes, too.

First of all, the standard 50-pound, 100-terabyte Data Box is now generally available. If you’ve got a lot of data to transfer to the cloud — or maybe collect a lot of offline data — then FedEx will happily pick this one up and Microsoft will upload the data to Azure and charge you for your storage allotment.

If you’ve got a lot more data, though, then Microsoft now also offers the Azure Data Box Heavy. This new box, which is now in preview, can hold up to one petabyte of data. Microsoft did not say how heavy the Data Box Heavy is, though.

Also new is the Azure Data Box Edge, which is now also in preview. In many ways, this is the most interesting of the additions since it goes well beyond transporting data. As the name implies, Data Box Edge is meant for edge deployments where a company collects data. What makes this version stand out is that it’s basically a small data center rack that lets you process data as it comes in. It even includes an FPGA to run AI algorithms at the edge.

Using this box, enterprises can collect the data, transform and analyze it on the box, and then send it to Azure over the network (and not in a truck). Using this, users can cut back on bandwidth cost and don’t have to send all of their data to the cloud for processing.

Also part of the same Data Box family is the Data Box Gateway. This is a virtual appliance, however, that runs on Hyper-V and VMWare and lets users create a data transfer gateway for importing data in Azure. That’s not quite as interesting as a hardware appliance but useful nonetheless.

Microsoft today launched two new Azure regions in China. These new regions, China North 2 in Beijing and China East 2 in Shanghai, are now generally available and will complement the existing two regions Microsoft operates in the country (with the help of its local partner, 21Vianet).

As the first international cloud provider in China when it launched its first region there in 2014, Microsoft has seen rapid growth in the region and there is clearly demand for its services there. Unsurprisingly, many of Microsoft’s customers in China are other multinationals that are already betting on Azure for their cloud strategy. These include the likes of Adobe, Coke, Costco, Daimler, Ford, Nuance, P&G, Toyota and BMW.

In addition to the new China regions, Microsoft also today launched a new availability zone for its region in the Netherlands. While availability zones have long been standard among the big cloud providers, Azure only launched this feature — which divides a region into multiple independent zones — into general availability earlier this year. The regions in the Netherlands, Paris and Iowa now offer this additional safeguard against downtime, with others to follow soon.

In other Azure news, Microsoft also today announced that Azure IoT Edge is now generally available. In addition, Microsoft announced the second generation of its Azure Data Lake Storage service, which is now in preview, and some updates to the Azure Data Factory, which now includes a web-based user interface for building and managing data pipelines.

No matter what cloud you build on, if you want to build something that’s highly available, you’re always going to opt to put your applications and data in at least two physically separated regions. Otherwise, if a region goes down, your app goes down, too. All of the big clouds also offer a concept called ‘availability zones’ in their regions to offer developers the option to host their applications in two separate data centers in the same zone for a bit of extra resilience. All big clouds, that is, except for Azure, which is only launching its availability zones feature into general availability today after first announcing a beta last September.

Ahead of today’s launch, Julia White, Microsoft’s corporate VP for Azure, told me that the company’s design philosophy behind its data center network was always about servicing commercial customers with the widest possible range of regions to allow them to be close to their customers and to comply with local data sovereignty and privacy laws. That’s one of the reasons why Azure today offers more regions than any of its competitors, with 38 generally available regions and 12 announced ones.

“Microsoft started its infrastructure approach focused on enterprise organizations and built lots of regions because of that,” White said. “We didn’t pick this regional approach because it’s easy or because it’s simple, but because we believe this is what our customers really want.”

Every availability zone has its own network connection and power backup, so if one zone in a region goes down, the others should remain unaffected. A regional disaster could shut down all of the zones in a single region, though, so most business will surely want to keep their data in at least one additional region.

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