Microsoft Corp. today previewed a new Azure instance for training artificial intelligence models that targets the emerging class of advanced, ultra-large neural networks being pioneered by the likes of OpenAI.

The instance, called the ND A100 v4, is being touted by Microsoft as its most powerful AI-optimized virtual machine to date.

The ND A100 v4 aims to address an important new trend in AI development. Engineers usually develop a separate machine learning model for every use case they seek to automate, but recently, a shift has started toward building one big, multipurpose model and customizing it for multiple use cases. One notable example of such an AI is the OpenAI research group’s GPT-3 model, whose 175 billion learning parameters allow it to perform tasks as varied as searching the web and writing code.

Microsoft is one of OpenAI’s top corporate backers. The company has also adopted the multipurpose AI approach internally, disclosing in the instance announcement today that such large AI models are used to power features across Bing and Outlook.

The ND A100 v4 is aimed at helping other companies train their own supersized neural networks by providing eight of Nvidia Corp.’s latest A100 graphics processing units per instance. Customers can link multiple ND A100 v4 instances together to create an AI training cluster with up to “thousands” of GPUs.

Microsoft didn’t specify exactly how many GPUs are supported. But even at the low end of the possible range, assuming a cluster with a graphics card count in the low four figures, the performance is likely not far behind that of a small supercomputer. Earlier this year, Microsoft built an Azure cluster for OpenAI that qualified as one of the world’s top five supercomputers, and that cluster had 10,000 GPUs.

In the new ND A100 v4 instance, what facilitates the ability to cluster together GPUs is a dedicated 200-gigabit per second InfiniBand network link provisions to each chip. These connections allow the graphics cards to communicate with each across instances. The speed at which GPUs can share data is a big factor in how fast they can process that data, and Microsoft says its the ND A100 v4 VM offers 16 times more GPU-to-GPU bandwidth than any other major public cloud.

The InfiniBand connections are powered by networking gear supplied by Nvidia’s Mellanox unit. To support the eight onboard GPUs, the new instance also packs a central processing unit from Advanced Micro Devices Inc.’s second-generation Epyc series of server processors.

The end result is what the company describes as a big jump in AI training performance. “Most customers will see an immediate boost of 2x to 3x compute performance over the previous generation of systems based on Nvidia V100 GPUs with no engineering work,” Ian Finder, a senior program manager at Azure, wrote in a blog post. He added that some customers may see performance improve by up to 20 times in some cases.

Microsoft’s decision to use Nvidia chips and Mellanox gear to power the instance shows how chipmaker is already reaping dividends from its $6.9 billion acquisition of Mellanox, which closed this year. Microsoft’s own investments in AI and related develop have likewise helped it win customers. Today’s debut of the new AI instance was preceded by the Tuesday announcement that the U.S. Energy Department has partnered with the tech giant to develop AI disaster response tools on Azure.

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Several years ago, big data was at the height of its hype cycle and Hadoop was its poster child technology. Today, open source analytics are solidly part of the enterprise software stack, the term “big data” seems antiquated, and it has become accepted folklore that Hadoop is, well…dead. That’s just so much hyperbole, though; while Hadoop is no longer white hot, it is still a significant factor. That’s because it defines an ecosystem of affiliated projects supporting database, data lake, streaming data and data engineering functionality. And that’s why cloud services based on Hadoop, like Amazon’s EMR and Google Cloud’s Dataproc, are still critical and useful.

Microsoft’s cloud Hadoop service, Azure HDInsight, is an especially good example of the utility of Hadoop, because the service persists, even as Microsoft has launched other offerings that compete with it. For example, Microsoft partnered with Databricks, the company founded by Apache Spark’s creators, to offer and directly support a version of that company’s eponymous service. Additionally, Microsoft evolved its Azure SQL Data Warehouse service into Azure Synapse Analytics, which now offers on-demand Spark pools to support full data lake functionality, as a public preview feature. But HDInsight, the Hadoop service Microsoft first launched in 2013, is still chugging along.

DANCE WITH THE ELEPHANT THAT BRUNG YA?

HDInsight was co-developed with Hortonworks, a company that subsequently merged with Cloudera. After that merger, the new Cloudera rationalized and refactored the two companies’ Hadoop distributions – Hortonworks Data Platform (HDP) and Cloudera Distribution including Apache Hadoop (CDH) into the runtime for the new Cloudera Data Platform (CDP). And while that’s all well and good for Cloudera, the retirement of HDP created a challenge for Microsoft, as HDInsight has been based on HDP since its inception.

Many were wondering whether Microsoft would somehow adopt the CDP runtime, or if it would perhaps go its own way, and build its own Hadoop distro, as AWS and Google did from the get-go. The latter has now come to pass, as Microsoft has created its own Hadoop distribution, based on Apache open source components. The general availability of the new distribution was announced on July 21st during Inspire, Microsoft’s virtual event for its partner ecosystem. 

As it turns out, in order to maintain compatibility and minimize disruption, Microsoft took HDP 3.1.6, on which HDInsight 4.0 was based, and cloned it. Today, if you provision an HDInsight 4.0 cluster in various Azure regions (I’ve confirmed it for Canada East, US Central and US East), your cluster will use this new distro. You probably won’t notice though. The HDI version number hasn’t changed and neither have the underlying open source project versions. The only way you’d really know the change took place is the replacement of “HDP-3.1.6.2” with “HDInsight-4.1.0.26” in the “Versions” tab of Ambari’s Admin screen, as shown in the figure at the top of this post. In all other respects, the experience will be identical.

VALUE PROP

Why did Microsoft go to the trouble, you might ask? Keep in mind, even if there’s overlap, that HDInsight can do things other Azure data services cannot. For example, Azure Databricks clusters do not mix and match components – they feature a combination of a proprietary version of Spark, Delta Lake, Delta Engine and MLflow. Synapse Spark pools are also Spark-exclusive, though they’re based on the open source Spark bits. They’re also serverless, with billing based on actual queries executed. For some customers that model is better; for others, not so much.

So if you want a discrete big data cluster on Azure, billed by the hour, with open source Spark, as well as Hive, HBase, Pig and Hadoop (and, optionally, Kafka, Storm, Microsoft’s Machine Learning Server and open source Spark as well), you’re going to want to use HDInsight. And now, with Microsoft having made the investment to build, test and deploy its own Hadoop distribution, the service has a future that’s much more solid and it can continue to service the workloads it’s uniquely suited to.

GO BIG OR GO CLONE

Now that Microsoft controls HDInsight’s underlying distribution, will it innovate and evolve it beyond the HDP clone that it is now? There’s precedent for that, given Microsoft had already innovated on top of the HDP-based distro. Working with Hortonworks, Microsoft had built unique integrations with Azure Blob Storage and Azure Data Lake Storage, as well as Azure SQL Database. More recently, the addition of Machine Learning Server and .NET for Apache Spark introduced additional value-adds.

Perhaps Microsoft could next integrate Spark 3.0, Presto and AirFlow into HDInsight’s mix of open source components. On the Microsoft technology side, support for one or two of the company’s own notebook experiences, and perhaps tighter integration with Cosmos DB and Azure Machine Learning could come as well. These integrations would add value to HDInsight and the Azure services with which it became more tightly integrated.

Moves such as these would make Microsoft’s commitment to HDInsight clear. Ultimately, that commitment has to be full-throated; for too long, it’s been halfhearted. But for now, uncertainty has been eliminated, compatibility has been maintained and HDInsight is no longer dependent on a third party organization. That’s a win for Microsoft’s customers; it just might embolden them to want more.

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Microsoft announced July 8 a new artificial intelligence tool for its cloud platform Azure that allows developers to analyze unstructured medical data, including clinical notes, clinical trial protocols and medical publications. 

Microsoft’s Text Analytics for health allows researchers, data analysts and medical professionals to detect words and phrases from unstructured text and connect them to relevant healthcare and biomedical concepts, such as diagnoses, medication names and treatments. Users can extract more than 100 types of personally identifiable information, including protected health information, in unstructured text. 

The AI tool also links entities to medical ontologies and coding systems, such as the Unified Medical Language System, to detect connections between medical concepts mentioned in text. For example, the tool can be used to find the relationship between a medication name and the dosage associated with it.  

Medical researchers and data analysts can use the tool to create analytics on historical medical data, develop prediction models and match patients to clinical trials. Seattle-based Allen Institute for AI used it to develop a COVID-19 search engine allowing researchers to more quickly analyze coronavirus information. The institute, founded by Microsoft co-founder Paul Allen in 2014, partnered with the University College London to review medical research reports. 

“We have been partnering with engineers at Microsoft and data scientists to build a ‘living’ reviews system – that automatically identifies relevant research for reviews as they are published,” James Thomas, a professor at University College London, stated in a news release. “Text Analytics for health provides a powerful tool for extracting insights from clinical literature, with rich support for a wide range of healthcare terminology so that we can more quickly and accurately identify relevant information.” 

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Cyient today announced it has joined Microsoft Azure Certified for Internet of Things (IoT), ensuring customers get IoT solutions up and running quickly with hardware and software that has been pre-tested and verified to work with Microsoft Azure IoT services. Microsoft Azure Certified for IoT allows businesses to reach customers where they are, working with an ecosystem of devices and platforms, allowing for faster time to production.

Cyient combines 28 years of operations technology experience and industry domain knowledge with its digital solutions and services expertise to help businesses simplify complex IoT implementations and manage the convergence of OT/IT for improved outcomes. From solving a single, high-impact customer challenge to mapping out a strategic roadmap for superior operations, Cyient helps customers Connect, Integrate, Analyze, and Act on data to drive actions.

Cyient IoT Edge Gateway 5400, the flagship product in the company’s family of IoT gateways, is Microsoft Azure Certified for IoT. It provides customers with a modular and scalable edge gateway for seamless connectivity of legacy machines and next-gen intelligent devices to the Industrial IoT network and the cloud. Cyient’s IoT Edge Gateway 5400 provides diverse connectivity and communication protocol options, advanced data processing, and edge analytics capabilities for remote asset monitoring and predictive maintenance solutions. Cyient’s modular platform approach to hardware and software, with plug-and-play modules and over-the-air firmware updates, ensure your long-life assets evolve as your business and technology evolve.

Speaking on the collaboration, Suman Narayan, Senior Vice President, Semiconductors and Medical Technologies and Healthcare at Cyient, said, “Industries today are increasing focus on digital technology and building intelligence into equipment for remote monitoring and to extend asset life, improve customer experience, and ensure regulatory compliance. The Microsoft Azure IoT Certification validates Cyient’s ability to accelerate IoT deployments for customers and ensure seamless integration from the edge to the cloud.”

“Microsoft Azure Certified for IoT extends our promise to bring IoT to business scale, starting with interoperable solutions from leading technology companies around the world,” said Jerry Lee, Director of Marketing for Azure Internet of Things, Microsoft Corp. “With trusted offerings and verified partners, Microsoft Azure Certified for IoT accelerates the deployment of IoT even further.”

IoT projects are complex and take a long time to implement. Customers find that choosing and connecting the right set of devices, assets or sensors to the cloud can be time-consuming. To jumpstart their IoT projects with confidence, customers are looking for certified devices and platforms that are tested for readiness, compatibility and usability with the Microsoft Azure IoT Suite. By choosing a partner from the Microsoft Azure Certified for IoT program, customers can save time and effort on project specs and RFP processes by knowing in advance what devices and offerings will work with the Azure IoT Suite.

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The UK government’s Crown Commercial Service (CCS) has secured an agreement with Microsoft to offer public sector organizations discounted rates for Azure.

Billed as the Azure Pricing Arrangement (APA), the agreement builds on the Digital Transformation Arrangement (DTA) than has existed between Microsoft and the CCS since 2018 and runs until April 2021.

This has covered the provision of Microsoft software products including Windows 10, Office 365 and Microsoft Teams to public sector organisations, as well as cloud and security solutions.

The amended agreement means that all eligible public sector organizations under the DTA agreement will have discounted access to Microsoft Azure business tools , such as AI and analytics, blockchain and big data application.

Microsoft said the agreement would support organizations that used multiple cloud providers or hybrid solutions that stored some data on their own servers and some in the public cloud.

According to recent data from IT analytics firm Flexera, multi-cloud use is now mainstream, with more than half of organizations (53%) now using multiple public and private cloud systems.

Chris Perkins, general manager of public sector sales at Microsoft UK, said Azure offered “the security, scalability and resilience” that the public sector required to provide services to citizens “in a cost-effective way.”

Perkins added: “Today’s agreement caters for public sector organisations, regardless of where they are on their cloud journey, and Microsoft is looking forward to working with our customers to accelerate and unlock the benefits of moving to Azure.”

The UK government and Microsoft have been working together on cloud services since the launch of the UK’s Cloud First policy in 2013, which encouraged public sector organizations to ensure that they sought both quality and value for money when considering new cloud services.

Simon Tse, chief executive of CCS, said: “CCS provides commercial agreements which help organisations across the entire public sector save time and money on buying everyday goods and services.

“This agreement with Microsoft, is yet another example of our commitment to achieving outstanding commercial value for our customers, as they work to deliver essential services for UK citizens.”

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About six months after its announcement at the Ignite conference last fall, Microsoft leveraged a group of MVP analysts and professionals to delve deeper into the Synapse Analytics Analytics service. As noted in Andrew’s coverage last fall, Azure Synapse Analytics is a rebrand and evolution of Azure SQL Data Warehouse, expanding its footprint to span data warehouse, data lakes, and data integration within. from a single cloud service.

The notion of guide is as close as possible to a single source of truth, which in this case involves converging data warehouse, data lake, and data integration. This is a much tougher challenge than it seems, not only to collect highly coveted relational data with a wide range of variable and semistructured data, but also to bring together different groups of professionals with competencies, methods and demands. which are often diametrically opposite.

At one end you have database developers who specialize in working with SQL, while at the other end of the spectrum, data scientists and developers who work on the data lake often work with programmatic analytics in languages ​​like Python. Data warehouses, like any relational system, have typically been used for production and operating scenarios that require reliable performance and often the ability to serve a large population of users, while data lakes are ‘are more closely associated with experimentation with highly diverse datasets and less predictable workloads. which serves a good number of end users.

The result is that you have different workload features, different types of data, and different access patterns. This is the same reason data warehouses spawned years ago, as querying and information workloads interfered with operating systems. But with Azure Synapse, Microsoft is looking at the analytics side to unite the sticks. Although Azure Synapse is a service generally available today, the expanded platform is only six months out of the gate. So while Azure Synapse has the capabilities to support business analysts and data scientists, there are still more pieces that are needed.

Let’s start by keeping the lights on. Workload management has been a well-known topic for data storage for years: Ad hoc research demand patterns, period end reports and complex analytics are known, and since Years ago, turnkey data warehouse providers such as Teradata offered a family of optimized models, respectively, for “data intensive, high calculation, high or low concurrency” and “balanced” workloads to maximize the output of calculation resources.

When Hadoop arrived, it was assumed that the workload would be data intensive and thus the calculation was shifted to the data. Enter the native version of the cloud, and the pendulum again separated the calculation from the data for economic reasons (analytical workloads are infrequent, so why pay the calculation you don’t always use) with the width of band of modern data-driven modern cloud plans. movement problem. Then came the AI, which, depending on machine learning or deep learning, requires divergent resources.

Therefore, joining the data warehouse with the data lake is not a feat. Azure Synapse has addressed the workload issue with a native cloud architecture that relies on the separation of computation from storage in SQL Data Warehouse Gen 2 and extends this concept to heterogeneous SQL and calculates Spark within one. service. At the moment, they are using Azure Data Lake Storage (ADLS) Gen 2, which is designed to provide cloud object storage economies with the performance benefits of exposing data through a system API. files that POSIX complies with. Azure Synapse Analytics also offers a multi-level hierarchical cache inside the SQL engine that automatically moves data between performance levels (including disk storage and the NVD SSD cache) based on the workload of the Spark analytics run on high memory (8 GByte / node) instances.

Functionally, Azure Synapse Analytics starts by combining Azure Data Factory with Azure SQL Data Warehouse: the former remains available as a standalone service, while Azure Synapse replaces the second. And, although you don’t bundle Power BI or Azure Machine Learning directly into the same service, integrations are incorporated at the metadata and user interface levels, so the flow is natural.

Azure Synapse uses the concept of workspace to organize query data and code or artifacts. And the workspace can come out as a low-code / no-code tool for business analysts, or a notebook like Jupyter for data engineers and data scientists working at Spark, or applying machine learning models. In the demos, Microsoft showed how the same data transformation task could be performed in both ways. There will be some differences in experience, for example, while Synapse inherits the ability of Azure SQL Data Warehouse to support high concurrency, Spark environments often involve lone wolf data scientists or data engineers. There are also differences in data security levels: the practice is much more mature when it comes to relational database with table, column and native row level security, but not so mature by the lake of data. This is an area where Cloudera is different from SDX, which is available as part of its platform offering.

Due to the early phase of the Spark feature implementation, Python is currently supported, but R is not yet available. Given the momentum of Python, it’s probably not necessarily a stopper for most data scientists.

As a highly optimized platform, it is not surprising that Microsoft has added some customizations to its notebook implementations such as Spark and Jupyter Interact, and that not all Spark libraries are currently supported. Without delving into the weeds, Microsoft is looking for a more complete implementation of Spark in Azure Synapse after Spark 3.0 is released. However, for data scientists and engineers who want the pure Spark experience, Azure Databricks will still be the best choice.

What is our wish list?

At the moment, Azure Synapse Analytics operates on the idea of ​​a single data lake composed of relational tables, folders and files of different formats. In the future, we would like to see more data platforms reach Azure’s portfolio as we consider the data lake to be the data collection where it is located in the company. To that end, for Spark practitioners, we’d like to see first-party integration with Azure databases. There is room to extend supported computing instances, especially for AI workloads that require GPUs or ASICs. We would also like to see a hybrid strategy where Microsoft already has a foot in the door with Azure Stack and Azure Arc. And we would also like to see an Azure Synapse Partner Program that provides close integration and support for third-party tools that can connect to workspaces.

Oh, and one more thing. Today, Power BI and Azure Machine Learning are treated as ancillary services, as mentioned before, integrated into Synapse, but not included in the service. In the long term, we believe that both services need to be packaged as integral parts of Azure Synapse. Today we believe that virtually every client using Synapse will also use self-service visualization, whether it be with Power BI or third-party tools like Tableau. On the other hand, machine learning is not quite the case today, but we expect this to change quickly enough over the next two years or less with third-party models developed or built internally that will become ubiquitous. This is the writing on the wall.

This is not the first strength of Microsoft in combining the data warehouse and the data lake. For the locals, there was SQL Server 2019 Big Data Clusters, which placed a SQL Server engine on each node in a Hadoop cluster that allowed data lake (as defined initially by clusters with data stored in HDFS) accessible to the SQL query. But Azure Synapse is a complete rethink. Rather than making big data available to SQL and Python developers, it also changes the development environment by creating “workspaces”. It addresses a broader part of the analytics lifecycle – from data ingestion, transformation and integration, to self-service visualization and to collaboration – embedding Power BI reports on teams. from Microsoft.

But closer, Azure Synapse reflects the fact that in the cloud, vendors can break down tool chain silos into more unified bids that cover more than the lifecycle. Microsoft is almost the only vendor that leads this way. SAP Data Warehouse Cloud is taking a similar approach by integrating SAP Analytics Cloud to provide last-mile self-service viewing, while Oracle has begun publicly speaking about extending the Data Data Warehouse to a wider platform, like Azure Synapse, it would include more in the life cycle (hopefully Oracle Analytics Cloud integration will become a major component). So now we expect the next shoes to come down from AWS and Google.

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Cnvrg.io, a data science startup headquartered in Jerusalem and New York, today released a community version of its machine learning automation platform designed to help enterprises manage and scale AI. CEO Yochay Ettun says the release was motivated in part by the influx of social distancing and remote work stemming from the COVID-19 pandemic.

“The release of cnvrg.io CORE is our contribution to the strong data science community responsible for advancing AI innovation,” said Ettun. “CORE’s release marks a new vision for the data science field. As data scientists, we built CORE to fill the need that so many data scientists require, to focus less on infrastructure and more on what they do best — algorithms.”

CORE facilitates machine learning workflow management with end-to-end AI model tracking and monitoring. Its built-in cluster orchestration supports hybrid cloud and multi-cloud configurations, and its compute querying and autoscaling — which can be fine-tuned from a dashboard — ensure that every available resource is fully utilized.

CORE can be installed on-premises or in a cloud environment directly from Cnvrg.io’s website. Developers can connect data sources to it to build and automatically retrain machine learning models; run machine learning experiments at scale to ensure reproducibility; and deploy to production with any framework or programming language.

There’s no shortage of orchestration platforms in the over $1.5 billion global machine learning market. Amazon recently rolled out SageMaker Studio, an extension of its SageMaker platform that automatically collects all code and project folders for machine learning in one place. Google offers its own solution in Cloud AutoML, which supports tasks like classification, sentiment analysis, and entity extraction, as well as a range of file formats, including native and scanned PDFs. Not to be outdone, Microsoft recently introduced enhancements to Azure Machine Learning, its service that enables users to architect predictive models, classifiers, and recommender systems for cloud-hosted and on-premises apps, and IBM has a comparable product in Watson Studio AutoAI.

But two-year-old Cnvrg.io, which is backed by Jerusalem Venture Partners and private investors Kevin Bermeister and Prashant Malik, has managed to raise $8 million in venture capital to date and attract customers that include Nvidia, Sisense, NetApp, Lightricks, and Wargaming.net.

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Microsoft’s writing assistant for Microsoft 365 users is getting new AI-powered features like Rewrite Suggestions, which lets you highlight a sentence and then right-click for multiple AI-generated revision ideas, and is coming to Chrome and Edge browser extensions and Outlook.com. AI Editor can also now review text for more gender-related inclusivity and issues that can arise like brevity, formality, or lack of clarity, and review edit recommendations in a dashboard. Similarity Checker is also being introduced today with a plagiarism checker and the ability to check whether a paper includes necessary citations.

Susan Hendrich, Microsoft’s group program manager of AI and NLP for Office, told VentureBeat that Rewrite Suggestions uses a Transformer-based model, and that all new features were developed in conjunction with Microsoft Research. Experts who spoke with VentureBeat around the beginning of the year said advances in text generation and other Transformer-based AI models are among the greatest advances in the field of the artificial intelligence.

“We’ve definitely used machine learning models in our writing intelligence before, and this is one of the first times we’ve actually used deep neural networks to provide writing suggestions or intelligence.” Hendrich said. “It’s a pretty big deal for us.”

Creating a large blocklist was also part of building Rewrite Suggestions, she said, in order to ensure convolutional neural networks or modeling techniques do not make inappropriate recommendations. Due to an exploited vulnerability, Microsoft’s Tay chatbot became famous in 2016 for a laundry list of inappropriate recommendations like “smoking kush” in front of the police.

The plagiarism predictor launching today is designed to help appropriately cite sources. It builds on Editor’s previous ability to quickly incorporate research from the web into Word documents.

“What this does, which we didn’t do previously, is it actually scans your entire document, compares it to Bing’s corpora of data that’s available via the web, and then recommends how you actually cite it and citation source,” she said.

In addition to recommending the removal of gender-specific language, the Editor’s inclusivity recommendations also attempt to remove racial or ethic ethnic slurs, as well as derogatory terms for people who are physically disabled or people with cognitive disabilities.

Microsoft’s Editor was first introduced in 2016 for Word. It has always been able to do basic spelling and grammar corrections, as well as recognize things like when you use certain words or expressions too often or take actions like removing adverbs. The Microsoft Editor is currently available in more than 20 languages, and its AI models were built using Azure Machine Learning.

Upgrades to 365’s AI text editor were released today alongside a number of changes, such as merging Office 365 with Microsoft 365. Both offer productivity apps like Word, Excel, and PowerPoint, but Office 365 currently has 38 million paid subscribers and 200 million monthly active business users.

New Microsoft 365 features that will begin rolling out today and continue over the coming weeks include new Edge web browser features like alerts when a password is compromised and the launch of the Microsoft Family Safety app for location tracking and screen time management across Android, iOS, and Xbox operating systems. Also new today: Money in Excel, which lets you add accounts from over 10,000 financial institutions to power personal finance management in spreadsheets.

Presenter Coach, which uses AI to teach people how to make better PowerPoint presentations, also got an update today that critiques monotone voice expression and offers suggestions about how to phrase your work. New Presenter Coach features will be available in preview for Microsoft 365 subscribers.

In other app growth numbers shared today, Skype is seeing 40 million users a day, with usage up 70% month over month. Earlier this month, Microsoft Teams reported it has 44 million daily active users.

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2nd Watch today launched a managed DevOps service based on the continuous integration/continuous deployment (CI/CD) platform from GitLab that it will operate on behalf of customers in addition to coaching them on how to adopt best DevOps practices.

Stefana Muller, a senior product manager, said the 2nd Watch Managed DevOps service combines the managed and professional services capabilities of 2nd Watch into a single offering. That approach is required because when it comes to DevOps most organizations need as much help transitioning their culture as they will operating their CI/CD environment, she said.

One of the major challenges organizations face when trying to embrace DevOps is they typically spend a lot of money upfront only to see those efforts flounder when changing the internal culture of the organization becomes too difficult, Muller said. As part of an effort to smooth that transition, 2nd Watch DevOps experts will assess an organization’s readiness to adopt DevOps tools and processes and then train internal teams on DevOps fundamentals such as managing infrastructure as code and Git principles. The assessment is based on a Culture, Automation, Lean Practices, Measurement and Sharing (CALMS) methodology.

The 2nd Watch service is built around an on-premises edition of the GitLab CI/CD platform that it will host and upgrade on behalf of a customer on either an Amazon Web Services (AWS) or Microsoft public cloud platform. The DevOps service will focus on enabling organizations to build both monolithic and microservices-based applications faster because no organization today has fully transitioned to microservices. The company already provides a managed Kubernetes service for building and deploying applications based on a microservices architecture.

2nd Watch chose to partner with GitLab because of the completeness of that CI/CD platform, which among other things includes integrated security modules that can be employed to advance DevSecOps practices as well, Muller said, noting that longer-term, 2nd Watch is planning to layer a more robust set of managed DevSecOps services on top of the GitLab platform.

Managed DevOps offerings are starting to proliferate as the number of services providers that have attained certifications from multiple IT vendors has increased. It’s not clear to what degree organizations will prefer to rely on these services just yet. However, it’s is apparent many organizations are trying to shift as much of their limited resources to building application code versus managing IT infrastructure. It’s also possible that as the COVID-19 coronavirus pandemic becomes more widespread many organizations will need to rely on external service providers to manage their DevOps environments.

Regardless of the motivation, organizations that are at the beginning of their DevOps journey clearly will need more help than those that are further along the DevOps maturity curve. The decision most organizations will need to make is to what degree will they rely on a managed DevOps service to accelerate their transition versus opting to rely permanently on an external IT service provider to manage one of their most critical IT environments.

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Microsoft and Cisco have announced that they are integrating their cloud and IoT services in a bid to ease the management of IoT devices. This mean tying together the recently released Cisco Edge Intelligence with Microsoft’s Azure IoT Hub to make transferring data from edge devices to applications in the Azure cloud simpler.

The announcement was made by two companies in a respective blogpost published last Wednesday.

“By enabling Azure IoT with Cisco IoT network devices infrastructure, IT, and operations teams can quickly take advantage of a wide variety of hardware and easily scalable telemetry collection from connected assets, to kickstart their Azure IoT application development,” wrote Tony Shakib, IoT business acceleration leader at Microsoft Azure.

He added: “Our customers can now augment their existing Cisco networks with Azure IoT ready gateways across multiple industries and use cases, without compromising the ability to implement data control and security that both Microsoft and Cisco are known for.”

Citing that Gartner’s prediction that 75% of enterprise data will be generated at the edge by 2025, Shakib pointed out that the ability to manage vast amounts of data near the edge will mean infrastructure and operations teams are required to manage more advanced data workloads, while keeping pace with business needs.

Marriage made in cloud heaven

According to Shakib, using software-based intelligence pre-loaded onto Cisco IoT network devices, telemetry data pipelines from industry-standard protocols like OPC-Unified Architecture (OPC-UA) and Modbus can be easily established using a friendly UI directly into Azure IoT Hub.

Services like Microsoft Azure Stream Analytics, Microsoft Azure Machine Learning, and Microsoft Azure Notification Hub services can be used to quickly build IoT applications for the enterprise. “Additional telemetry processing is also supported by Cisco through local scripts developed in Microsoft Visual Studio, where filtered data can also be uploaded directly into Azure IoT Hub. This collaboration provides customers with a fully integrated solution that will give access to powerful design tools, global connectivity, advance analytics, and cognitive services for analysing IoT data,” said Shakib.

Securing transmission of IoT edge data

Last January, Cisco released its Cisco Edge Intelligence software to simplify the extraction of IoT data at the network edge. The software runs on Cisco’s IoT packages and gathers data from connected devices to create logical flows from the edge into private, public or third-party clouds – in this case Microsoft Azure IoT Hub which will direct communication between IoT devices and applications.

“One of the outcomes of this partnership will be to offer customers a pre-integrated IoT edge-to-cloud application solution. Customers will be able to integrate their IoT devices through Cisco Edge Intelligence to Azure IoT Hub with a click of a button,” said Vikas Butaney, vice president of product development, IoT Business Group at Cisco in his own blogpost.

“Both Cisco and Microsoft have made it simpler, faster and more secure to send intelligent IoT edge data to enterprise-class applications. Customers will be able to drive their artificial intelligence, analytics, machine learning, and business intelligence applications to support a better digital transformation,” he added.

Already, customers in a variety of industry verticals are taking advantage of the Microsoft and Cisco partnership as they deploy more IoT solutions at the edge of the network.

For one, voestalpine, a manufacturer of high-quality steel, is benefiting from this integration in several ways. Cisco Edge Intelligence is being used to extract and transform typical factory floor data into valuable plant management information with seamless integration with Azure IoT service. To improve operations, voestalpine is using Cisco Edge Intelligence to ensure that IoT data is segmented and accessed by the right plant vendors while at the same time feeding its corporate systems on the centralised Azure cloud.

“At voestalpine, we are going through a digital journey to rethink and innovate manufacturing processes to bring increased operational efficiency. We face challenges to consistently and securely extract data from these machines and deliver the right data to our analytics applications,” said Stefan Pöchtrager, enterprise architect, voestalpine AG.

He added: “We are validating Cisco’s next-generation edge data software, Cisco Edge Intelligence, along with Azure IoT services for our cloud software development. Cisco’s out-of-the box edge solution with Azure IoT services helps us accelerate our digital journey.”

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