Dec
02
2019
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New Amazon tool simplifies delivery of containerized machine learning models

As part of the flurry of announcements coming this week out of AWS re:Invent, Amazon announced the release of Amazon SageMaker Operators for Kubernetes, a way for data scientists and developers to simplify training, tuning and deploying containerized machine learning models.

Packaging machine learning models in containers can help put them to work inside organizations faster, but getting there often requires a lot of extra management to make it all work. Amazon SageMaker Operators for Kubernetes is supposed to make it easier to run and manage those containers, the underlying infrastructure needed to run the models and the workflows associated with all of it.

“While Kubernetes gives customers control and portability, running ML workloads on a Kubernetes cluster brings unique challenges. For example, the underlying infrastructure requires additional management such as optimizing for utilization, cost and performance; complying with appropriate security and regulatory requirements; and ensuring high availability and reliability,” AWS’ Aditya Bindal wrote in a blog post introducing the new feature.

When you combine that with the workflows associated with delivering a machine learning model inside an organization at scale, it becomes part of a much bigger delivery pipeline, one that is challenging to manage across departments and a variety of resource requirements.

This is precisely what Amazon SageMaker Operators for Kubernetes has been designed to help DevOps teams do. “Amazon SageMaker Operators for Kubernetes bridges this gap, and customers are now spared all the heavy lifting of integrating their Amazon SageMaker and Kubernetes workflows. Starting today, customers using Kubernetes can make a simple call to Amazon SageMaker, a modular and fully-managed service that makes it easier to build, train, and deploy machine learning (ML) models at scale,” Bindal wrote.

The promise of Kubernetes is that it can orchestrate the delivery of containers at the right moment, but if you haven’t automated delivery of the underlying infrastructure, you can over (or under) provision and not provide the correct amount of resources required to run the job. That’s where this new tool, combined with SageMaker, can help.

“With workflows in Amazon SageMaker, compute resources are pre-configured and optimized, only provisioned when requested, scaled as needed, and shut down automatically when jobs complete, offering near 100% utilization,” Bindal wrote.

Amazon SageMaker Operators for Kubernetes are available today in select AWS regions.

Nov
26
2019
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New Amazon capabilities put machine learning in reach of more developers

Today, Amazon announced a new approach that it says will put machine learning technology in reach of more developers and line of business users. Amazon has been making a flurry of announcements ahead of its re:Invent customer conference next week in Las Vegas.

While the company offers plenty of tools for data scientists to build machine learning models and to process, store and visualize data, it wants to put that capability directly in the hands of developers with the help of the popular database query language, SQL.

By taking advantage of tools like Amazon QuickSight, Aurora and Athena in combination with SQL queries, developers can have much more direct access to machine learning models and underlying data without any additional coding, says VP of artificial intelligence at AWS, Matt Wood.

“This announcement is all about making it easier for developers to add machine learning predictions to their products and their processes by integrating those predictions directly with their databases,” Wood told TechCrunch.

For starters, Wood says developers can take advantage of Aurora, the company’s MySQL (and Postgres)-compatible database to build a simple SQL query into an application, which will automatically pull the data into the application and run whatever machine learning model the developer associates with it.

The second piece involves Athena, the company’s serverless query service. As with Aurora, developers can write a SQL query — in this case, against any data store — and based on a machine learning model they choose, return a set of data for use in an application.

The final piece is QuickSight, which is Amazon’s data visualization tool. Using one of the other tools to return some set of data, developers can use that data to create visualizations based on it inside whatever application they are creating.

“By making sophisticated ML predictions more easily available through SQL queries and dashboards, the changes we’re announcing today help to make ML more usable and accessible to database developers and business analysts. Now anyone who can write SQL can make — and importantly use — predictions in their applications without any custom code,” Amazon’s Matt Asay wrote in a blog post announcing these new capabilities.

Asay added that this approach is far easier than what developers had to do in the past to achieve this. “There is often a large amount of fiddly, manual work required to take these predictions and make them part of a broader application, process or analytics dashboard,” he wrote.

As an example, Wood offers a lead-scoring model you might use to pick the most likely sales targets to convert. “Today, in order to do lead scoring you have to go off and wire up all these pieces together in order to be able to get the predictions into the application,” he said. With this new capability, you can get there much faster.

“Now, as a developer I can just say that I have this lead scoring model which is deployed in SageMaker, and all I have to do is write literally one SQL statement that I do all day long into Aurora, and I can start getting back that lead scoring information. And then I just display it in my application and away I go,” Wood explained.

As for the machine learning models, these can come pre-built from Amazon, be developed by an in-house data science team or purchased in a machine learning model marketplace on Amazon, says Wood.

Today’s announcements from Amazon are designed to simplify machine learning and data access, and reduce the amount of coding to get from query to answer faster.

Nov
20
2019
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Google Cloud launches Bare Metal Solution

Google Cloud today announced the launch of a new bare metal service, dubbed the Bare Metal Solution. We aren’t talking about bare metal servers offered directly by Google Cloud here, though. Instead, we’re talking about a solution that enterprises can use to run their specialized workloads on certified hardware that’s co-located in the Google Cloud data centers and directly connect them to Google Cloud’s suite of other services. The main workload that makes sense for this kind of setup is databases, Google notes, and specifically Oracle Database.

Bare Metal Solution is, as the name implies, a fully integrated and fully managed solution for setting up this kind of infrastructure. It involves a completely managed hardware infrastructure that includes servers and the rest of the data center facilities like power and cooling, support contracts with Google Cloud and billing are handled through Google’s systems, as well as an SLA. The software that’s deployed on those machines is managed by the customer — not Google.

The overall idea, though, is clearly to make it easier for enterprises with specialized workloads that can’t easily be migrated to the cloud to still benefit from the cloud-based services that need access to the data from these systems. Machine learning is an obvious example, but Google also notes that this provides these companies with a bridge to slowly modernize their tech infrastructure in general (where ‘modernize’ tends to mean ‘move to the cloud’).

“These specialized workloads often require certified hardware and complicated licensing and support agreements,” Google writes. “This solution provides a path to modernize your application infrastructure landscape, while maintaining your existing investments and architecture. With Bare Metal Solution, you can bring your specialized workloads to Google Cloud, allowing you access and integration with GCP services with minimal latency.”

Since this service is co-located with Google Cloud, there are no separate ingress and egress charges for data that moves between Bare Metal Solution and Google Cloud in the same region.

The servers for this solution, which are certified to run a wide range of applications (including Oracle Database) range from dual-socket 16-core systems with 384 GB of RAM to quad-socket servers with 112 cores and 3072 GB of RAM. Pricing is on a monthly basis, with a preferred term length of 36 months.

Obviously, this isn’t the kind of solution that you self-provision, so the only way to get started — and get pricing information — is to talk to Google’s sales team. But this is clearly the kind of service that we should expect from Google Cloud, which is heavily focused on providing as many enterprise-ready services as possible.

Nov
14
2019
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Eigen nabs $37M to help banks and others parse huge documents using natural language and ‘small data’

One of the bigger trends in enterprise software has been the emergence of startups building tools to make the benefits of artificial intelligence technology more accessible to non-tech companies. Today, one that has built a platform to apply the power of machine learning and natural language processing to massive documents of unstructured data has closed a round of funding as it finds strong demand for its approach.

Eigen Technologies, a London-based startup whose machine learning engine helps banks and other businesses that need to extract information and insights from large and complex documents like contracts, is today announcing that it has raised $37 million in funding, a Series B that values the company at around $150 million – $180 million.

The round was led by Lakestar and Dawn Capital, with Temasek and Goldman Sachs Growth Equity (which co-led its Series A) also participating. Eigen has now raised $55 million in total.

Eigen today is working primarily in the financial sector — its offices are smack in the middle of The City, London’s financial center — but the plan is to use the funding to continue expanding the scope of the platform to cover other verticals such as insurance and healthcare, two other big areas that deal in large, wordy documentation that is often inconsistent in how its presented, full of essential fine print, and typically a strain on an organisation’s resources to be handled correctly — and is often a disaster if it is not.

The focus up to now on banks and other financial businesses has had a lot of traction. It says its customer base now includes 25% of the world’s G-SIB institutions (that is, the world’s biggest banks), along with others that work closely with them, like Allen & Overy and Deloitte. Since June 2018 (when it closed its Series A round), Eigen has seen recurring revenues grow sixfold with headcount — mostly data scientists and engineers — double. While Eigen doesn’t disclose specific financials, you can see the growth direction that contributed to the company’s valuation.

The basic idea behind Eigen is that it focuses what co-founder and CEO Lewis Liu describes as “small data.” The company has devised a way to “teach” an AI to read a specific kind of document — say, a loan contract — by looking at a couple of examples and training on these. The whole process is relatively easy to do for a non-technical person: you figure out what you want to look for and analyse, find the examples using basic search in two or three documents and create the template, which can then be used across hundreds or thousands of the same kind of documents (in this case, a loan contract).

Eigen’s work is notable for two reasons. First, typically machine learning and training and AI requires hundreds, thousands, tens of thousands of examples to “teach” a system before it can make decisions that you hope will mimic those of a human. Eigen requires a couple of examples (hence the “small data” approach).

Second, an industry like finance has many pieces of sensitive data (either because it’s personal data, or because it’s proprietary to a company and its business), and so there is an ongoing issue of working with AI companies that want to “anonymise” and ingest that data. Companies simply don’t want to do that. Eigen’s system essentially only works on what a company provides, and that stays with the company.

Eigen was founded in 2014 by Dr. Lewis Z. Liu (CEO) and Jonathan Feuer (a managing partner at CVC Capital Partners, who is the company’s chairman), but its earliest origins go back 15 years earlier, when Liu — a first-generation immigrant who grew up in the U.S. — was working as a “data-entry monkey” (his words) at a tire manufacturing plant in New Jersey, where he lived, ahead of starting university at Harvard.

A natural computing whiz who found himself building his own games when his parents refused to buy him a games console, he figured out that the many pages of printouts he was reading and re-entering into a different computing system could be sped up with a computer program linking up the two. “I put myself out of a job,” he joked.

His educational life epitomises the kind of lateral thinking that often produces the most interesting ideas. Liu went on to Harvard to study not computer science, but physics and art. Doing a double major required working on a thesis that merged the two disciplines together, and Liu built “electrodynamic equations that composed graphical structures on the fly” — basically generating art using algorithms — which he then turned into a “Turing test” to see if people could detect pixelated actual work with that of his program. Distill this, and Liu was still thinking about patterns in analog material that could be re-created using math.

Then came years at McKinsey in London (how he arrived on these shores) during the financial crisis where the results of people either intentionally or mistakenly overlooking crucial text-based data produced stark and catastrophic results. “I would say the problem that we eventually started to solve for at Eigen became tangible,” Liu said.

Then came a physics PhD at Oxford where Liu worked on X-ray lasers that could be used to decrease the complexity and cost of making microchips, cancer treatments and other applications.

While Eigen doesn’t actually use lasers, some of the mathematical equations that Liu came up with for these have also become a part of Eigen’s approach.

“The whole idea [for my PhD] was, ‘how do we make this cheaper and more scalable?,’ ” he said. “We built a new class of X-ray laser apparatus, and we realised the same equations could be used in pattern matching algorithms, specifically around sequential patterns. And out of that, and my existing corporate relationships, that’s how Eigen started.”

Five years on, Eigen has added a lot more into the platform beyond what came from Liu’s original ideas. There are more data scientists and engineers building the engine around the basic idea, and customising it to work with more sectors beyond finance. 

There are a number of AI companies building tools for non-technical business end-users, and one of the areas that comes close to what Eigen is doing is robotic process automation, or RPA. Liu notes that while this is an important area, it’s more about reading forms more readily and providing insights to those. The focus of Eigen is more on unstructured data, and the ability to parse it quickly and securely using just a few samples.

Liu points to companies like IBM (with Watson) as general competitors, while startups like Luminance is another taking a similar approach to Eigen by addressing the issue of parsing unstructured data in a specific sector (in its case, currently, the legal profession).

Stephen Nundy, a partner and the CTO of Lakestar, said that he first came into contact with Eigen when he was at Goldman Sachs, where he was a managing director overseeing technology, and the bank engaged it for work.

“To see what these guys can deliver, it’s to be applauded,” he said. “They’re not just picking out names and addresses. We’re talking deep, semantic understanding. Other vendors are trying to be everything to everybody, but Eigen has found market fit in financial services use cases, and it stands up against the competition. You can see when a winner is breaking away from the pack and it’s a great signal for the future.”

Nov
07
2019
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How Microsoft is trying to become more innovative

Microsoft Research is a globally distributed playground for people interested in solving fundamental science problems.

These projects often focus on machine learning and artificial intelligence, and since Microsoft is on a mission to infuse all of its products with more AI smarts, it’s no surprise that it’s also seeking ways to integrate Microsoft Research’s innovations into the rest of the company.

Across the board, the company is trying to find ways to become more innovative, especially around its work in AI, and it’s putting processes in place to do so. Microsoft is unusually open about this process, too, and actually made it somewhat of a focus this week at Ignite, a yearly conference that typically focuses more on technical IT management topics.

At Ignite, Microsoft will for the first time present these projects externally at a dedicated keynote. That feels similar to what Google used to do with its ATAP group at its I/O events and is obviously meant to showcase the cutting-edge innovation that happens inside of Microsoft (outside of making Excel smarter).

To manage its AI innovation efforts, Microsoft created the Microsoft AI group led by VP Mitra Azizirad, who’s tasked with establishing thought leadership in this space internally and externally, and helping the company itself innovate faster (Microsoft’s AI for Good projects also fall under this group’s purview). I sat down with Azizirad to get a better idea of what her team is doing and how she approaches getting companies to innovate around AI and bring research projects out of the lab.

“We began to put together a narrative for the company of what it really means to be in an AI-driven world and what we look at from a differentiated perspective,” Azizirad said. “What we’ve done in this area is something that has resonated and landed well. And now we’re including AI, but we’re expanding beyond it to other paradigm shifts like human-machine interaction, future of computing and digital responsibility, as more than just a set of principles and practices but an area of innovation in and of itself.”

Currently, Microsoft is doing a very good job at talking and thinking about horizon one opportunities, as well as horizon three projects that are still years out, she said. “Horizon two, we need to get better at, and that’s what we’re doing.”

It’s worth stressing that Microsoft AI, which launched about two years ago, marks the first time there’s a business, marketing and product management team associated with Microsoft Research, so the team does get a lot of insights into upcoming technologies. Just in the last couple of years, Microsoft has published more than 6,000 research papers on AI, some of which clearly have a future in the company’s products.

Nov
04
2019
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The 7 most important announcements from Microsoft Ignite

It’s Microsoft Ignite this week, the company’s premier event for IT professionals and decision-makers. But it’s not just about new tools for role-based access. Ignite is also very much a forward-looking conference that keeps the changing role of IT in mind. And while there isn’t a lot of consumer news at the event, the company does tend to make a few announcements for developers, as well.

This year’s Ignite was especially news-heavy. Ahead of the event, the company provided journalists and analysts with an 87-page document that lists all of the news items. If I counted correctly, there were about 175 separate announcements. Here are the top seven you really need to know about.

Azure Arc: you can now use Azure to manage resources anywhere, including on AWS and Google Cloud

What was announced: Microsoft was among the first of the big cloud vendors to bet big on hybrid deployments. With Arc, the company is taking this a step further. It will let enterprises use Azure to manage their resources across clouds — including those of competitors like AWS and Google Cloud. It’ll work for Windows and Linux Servers, as well as Kubernetes clusters, and also allows users to take some limited Azure data services with them to these platforms.

Why it matters: With Azure Stack, Microsoft already allowed businesses to bring many of Azure’s capabilities into their own data centers. But because it’s basically a local version of Azure, it only worked on a limited set of hardware. Arc doesn’t bring all of the Azure Services, but it gives enterprises a single platform to manage all of their resources across the large clouds and their own data centers. Virtually every major enterprise uses multiple clouds. Managing those environments is hard. So if that’s the case, Microsoft is essentially saying, let’s give them a tool to do so — and keep them in the Azure ecosystem. In many ways, that’s similar to Google’s Anthos, yet with an obvious Microsoft flavor, less reliance on Kubernetes and without the managed services piece.

Microsoft launches Project Cortex, a knowledge network for your company

What was announced: Project Cortex creates a knowledge network for your company. It uses machine learning to analyze all of the documents and contracts in your various repositories — including those of third-party partners — and then surfaces them in Microsoft apps like Outlook, Teams and its Office apps when appropriate. It’s the company’s first new commercial service since the launch of Teams.

Why it matters: Enterprises these days generate tons of documents and data, but it’s often spread across numerous repositories and is hard to find. With this new knowledge network, the company aims to surface this information proactively, but it also looks at who the people are who work on them and tries to help you find the subject matter experts when you’re working on a document about a given subject, for example.

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Microsoft launched Endpoint Manager to modernize device management

What was announced: Microsoft is combining its ConfigMgr and Intune services that allow enterprises to manage the PCs, laptops, phones and tablets they issue to their employees under the Endpoint Manager brand. With that, it’s also launching a number of tools and recommendations to help companies modernize their deployment strategies. ConfigMgr users will now also get a license to Intune to allow them to move to cloud-based management.

Why it matters: In this world of BYOD, where every employee uses multiple devices, as well as constant attacks against employee machines, effectively managing these devices has become challenging for most IT departments. They often use a mix of different tools (ConfigMgr for PCs, for example, and Intune for cloud-based management of phones). Now, they can get a single view of their deployments with the Endpoint Manager, which Microsoft CEO Satya Nadella described as one of the most important announcements of the event, and ConfigMgr users will get an easy path to move to cloud-based device management thanks to the Intune license they now have access to.

Microsoft’s Chromium-based Edge browser gets new privacy features, will be generally available January 15

What was announced: Microsoft’s Chromium-based version of Edge will be generally available on January 15. The release candidate is available now. That’s the culmination of a lot of work from the Edge team, and, with today’s release, the company is also adding a number of new privacy features to Edge that, in combination with Bing, offers some capabilities that some of Microsoft’s rivals can’t yet match, thanks to its newly enhanced InPrivate browsing mode.

Why it matters: Browsers are interesting again. After years of focusing on speed, the new focus is now privacy, and that’s giving Microsoft a chance to gain users back from Chrome (though maybe not Firefox). At Ignite, Microsoft also stressed that Edge’s business users will get to benefit from a deep integration with its updated Bing engine, which can now surface business documents, too.

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You can now try Microsoft’s web-based version of Visual Studio

What was announced: At Build earlier this year, Microsoft announced that it would soon launch a web-based version of its Visual Studio development environment, based on the work it did on the free Visual Studio Code editor. This experience, with deep integrations into the Microsoft-owned GitHub, is now live in a preview.

Why it matters: Microsoft has long said that it wants to meet developers where they are. While Visual Studio Online isn’t likely to replace the desktop-based IDE for most developers, it’s an easy way for them to make quick changes to code that lives in GitHub, for example, without having to set up their IDE locally. As long as they have a browser, developers will be able to get their work done..

Microsoft launches Power Virtual Agents, its no-code bot builder

What was announced: Power Virtual Agents is Microsoft’s new no-code/low-code tool for building chatbots. It leverages a lot of Azure’s machine learning smarts to let you create a chatbot with the help of a visual interface. In case you outgrow that and want to get to the actual code, you can always do so, too.

Why it matters: Chatbots aren’t exactly at the top of the hype cycle, but they do have lots of legitimate uses. Microsoft argues that a lot of early efforts were hampered by the fact that the developers were far removed from the user. With a visual too, though, anybody can come in and build a chatbot — and a lot of those builders will have a far better understanding of what their users are looking for than a developer who is far removed from that business group.

Cortana wants to be your personal executive assistant and read your emails to you, too

What was announced: Cortana lives — and it now also has a male voice. But more importantly, Microsoft launched a few new focused Cortana-based experiences that show how the company is focusing on its voice assistant as a tool for productivity. In Outlook on iOS (with Android coming later), Cortana can now read you a summary of what’s in your inbox — and you can have a chat with it to flag emails, delete them or dictate answers. Cortana can now also send you a daily summary of your calendar appointments, important emails that need answers and suggest focus time for you to get actual work done that’s not email.

Why it matters: In this world of competing assistants, Microsoft is very much betting on productivity. Cortana didn’t work out as a consumer product, but the company believes there is a large (and lucrative) niche for an assistant that helps you get work done. Because Microsoft doesn’t have a lot of consumer data, but does have lots of data about your work, that’s probably a smart move.

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SAN FRANCISCO, CA – APRIL 02: Microsoft CEO Satya Nadella walks in front of the new Cortana logo as he delivers a keynote address during the 2014 Microsoft Build developer conference on April 2, 2014 in San Francisco, California (Photo by Justin Sullivan/Getty Images)

Bonus: Microsoft agrees with you and thinks meetings are broken — and often it’s the broken meeting room that makes meetings even harder. To battle this, the company today launched Managed Meeting Rooms, which for $50 per room/month lets you delegate to Microsoft the monitoring and management of the technical infrastructure of your meeting rooms.

Nov
04
2019
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You can now ask Excel questions about your data

Microsoft today announced an update to Excel that brings natural language queries to the venerable spreadsheet tool. Available now to Office Insiders, this new feature allows you to talk to Excel like you’re talking to a person, and get quick answers to your queries without having to write a query.

“Natural language query is another step toward making data insights and visualization more approachable and accessible to users with various levels of Excel experience,” Microsoft explains. “Novice users will not need to know how to write a formula to gain useful insights from their data, while power users will be able to save time by automating the data discovery process by simply asking the right questions and quickly adding charts and tables they need for better and faster decisions.”

It’s worth noting that Google already offers similar features in Google Sheets. In my experience, Google sometimes does a pretty good job at finding data but also regularly fails to find even a single relevant data point, so it remains to be seen how good Excel is compared to that.

Today’s announcement is one in a series of recent launches for Excel that brought a number of new machine learning smarts to the spreadsheet. Among those is Excel’s ability to better understand your entries and provide you with additional information about stocks, geographical data and more.

Nov
04
2019
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Microsoft’s Azure Synapse Analytics bridges the gap between data lakes and warehouses

At its annual Ignite conference in Orlando, Fla., Microsoft today announced a major new Azure service for enterprises: Azure Synapse Analytics, which Microsoft describes as “the next evolution of Azure SQL Data Warehouse.” Like SQL Data Warehouse, it aims to bridge the gap between data warehouses and data lakes, which are often completely separate. Synapse also taps into a wide variety of other Microsoft services, including Power BI and Azure Machine Learning, as well as a partner ecosystem that includes Databricks, Informatica, Accenture, Talend, Attunity, Pragmatic Works and Adatis. It’s also integrated with Apache Spark.

The idea here is that Synapse allows anybody working with data in those disparate places to manage and analyze it from within a single service. It can be used to analyze relational and unstructured data, using standard SQL.

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Microsoft also highlights Synapse’s integration with Power BI, its easy to use business intelligence and reporting tool, as well as Azure Machine Learning for building models.

With the Azure Synapse studio, the service provides data professionals with a single workspace for prepping and managing their data, as well as for their big data and AI tasks. There’s also a code-free environment for managing data pipelines.

As Microsoft stresses, businesses that want to adopt Synapse can continue to use their existing workloads in production with Synapse and automatically get all of the benefits of the service. “Businesses can put their data to work much more quickly, productively, and securely, pulling together insights from all data sources, data warehouses, and big data analytics systems,” writes Microsoft CVP of Azure Data, Rohan Kumar.

In a demo at Ignite, Kumar also benchmarked Synapse against Google’s BigQuery. Synapse ran the same query over a petabyte of data in 75% less time. He also noted that Synapse can handle thousands of concurrent users — unlike some of Microsoft’s competitors.

Nov
04
2019
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Microsoft Azure gets into ag tech with the preview of FarmBeats

At its annual Ignite event in Orlando, Fla., Microsoft today announced that  Azure FarmBeats, a project that until now was mostly a research effort, will be available as a public preview and in the Azure Marketplace, starting today. FarmBeats is Microsoft’s project that combines IoT sensors, data analysis and machine learning.

The goal of FarmBeats is to augment farmers’ knowledge and intuition about their own farm with data and data-driven insights,” Microsoft explained in today’s announcement. The idea behind FarmBeats is to take in data from a wide variety of sources, including sensors, satellites, drones and weather stations, and then turn that into actionable intelligence for farmers, using AI and machine learning. 

In addition, FarmBeats also wants to be somewhat of a platform for developers who can then build their own applications on top of this data that the platform aggregates and evaluates.

As Microsoft noted during the development process, having satellite imagery is one thing, but that can’t capture all of the data on a farm. For that, you need in-field sensors and other data — yet all of this heterogeneous data then has to be merged and analyzed somehow. Farms also often don’t have great internet connectivity. Because of this, the FarmBeats team was among the first to leverage Microsoft’s efforts in using TV white space for connectivity and, of course, Azure IoT Edge for collecting all of the data.

Oct
30
2019
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Google launches TensorFlow Enterprise with long-term support and managed services

Google open-sourced its TensorFlow machine learning framework back in 2015 and it quickly became one of the most popular platforms of its kind. Enterprises that wanted to use it, however, had to either work with third parties or do it themselves. To help these companies — and capture some of this lucrative market itself — Google is launching TensorFlow Enterprise, which includes hands-on, enterprise-grade support and optimized managed services on Google Cloud.

One of the most important features of TensorFlow Enterprise is that it will offer long-term support. For some versions of the framework, Google will offer patches for up to three years. For what looks to be an additional fee, Google will also offer to companies that are building AI models engineering assistance from its Google Cloud and TensorFlow teams.

All of this, of course, is deeply integrated with Google’s own cloud services. “Because Google created and open-sourced TensorFlow, Google Cloud is uniquely positioned to offer support and insights directly from the TensorFlow team itself,” the company writes in today’s announcement. “Combined with our deep expertise in AI and machine learning, this makes TensorFlow Enterprise the best way to run TensorFlow.”

Google also includes Deep Learning VMs and Deep Learning Containers to make getting started with TensorFlow easier, and the company has optimized the enterprise version for Nvidia GPUs and Google’s own Cloud TPUs.

Today’s launch is yet another example of Google Cloud’s focus on enterprises, a move the company accelerated when it hired Thomas Kurian to run the Cloud businesses. After years of mostly ignoring the enterprise, the company is now clearly looking at what enterprises are struggling with and how it can adapt its products for them.

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