In the world of manufacturing, the Industrial Internet of Things (IIoT) has come, and that means data. A lot of data. Smart machines, equipped with sensors, add to the large quantity of data already generated from quality systems, MES, ERP and other production systems. All this data is being gathered in different formats and at different cadences making it nearly impossible to use—or to deliver business insights. Azure has mastered ingesting and storing manufacturing data with services such as Azure IoT Hub and Azure Data Lake, and now our partner Sight Machine has solved for the other huge challenge: data variety. Sight Machine on Azure is a leading AI-enabled analytics platform that enables manufacturers to normalize and contextualize plant floor data in real-time. The creation of these digital twins allows them to find new insights, transform operations, and unlock new value.
Data in the pre-digital world
Manufacturers are aware of the untapped potential of production data. Global manufacturers have begun investing in on-premises solutions for capturing and storing factory floor data. But these pre-digital world methods have many disadvantages. They result in siloed data, uncontextualized data (raw machine data with no connection to actual production processes), and limited accessibility (engineers
In December 2018, Microsoft launched the Azure IoT certification service, a web-based test automation workflow to streamline the certification process through self-serve tools. Azure IoT certification service (AICS) was designed to reduce the operational processes and engineering costs for hardware manufacturers to get their devices certified for Azure Certified for IoT program and be showcased on the Azure IoT device catalog.
The initial version of AICS focused on IoT device certification. Today, we are taking steps to expand the service to now also support Azure IoT Edge Device certification. Azure IoT Edge device is a device which comprised of three key components: IoT Edge modules, IoT Edge runtime and a cloud-based interface. Learn more about these three components in this blog explaining IoT Edge.
What it means to certify as Azure IoT Edge device is that the certification program validates the functionality of three key components described above. The certification program also ensures that the identity of a device is protected through validation of security components. You can review specific technical requirements for Azure IoT Edge device certification.
This expansion of AICS capabilities builds on the related expansion of the Azure Certified for IoT program to support Azure IoT Edge
With the expansion of IoT across all industries data is becoming the currency of innovation. Organizations have both an opportunity and a business imperative to adopt technologies quickly, build digital competencies, and offer new value-added services that will serve their broader ecosystem.
Manufacturing is an industry where IoT is having a transformational impact, yet which also requires many companies to come together for IoT to be effective. We see several challenges that slow down innovation in manufacturing, such as proprietary data structures from legacy industrial assets and closed industrial solutions. These closed structures foster data silos and limit productivity, hindering production and profitability. It takes more than new software to drive transformation—it takes a new approach to open standards, an ecosystem mindset, the ability to break out of the “walled garden” for data as well as new technology.
This is why Microsoft has invested heavily in making Azure work seamlessly with OPC UA. In fact, we are the leading contributor of open source software to the OPC Foundation. To further this open platform approach, we have collaborated with world-leading manufacturers to accelerate innovation in industrial IoT to shorten time to value. But we feel we need to do more, not
Precision medicine tailors a patient’s medical treatment by factoring in their genetic makeup and clinical data. The key to applying this methodology is integrating clinical data with an individual’s genomic data for the most complete longitudinal healthcare record to power the most precise and effective treatment.
Problem: data in silos, detached from the point of care
Currently, clinical information resides in silos (elecftronic healthcare records, radiological information systems, laboratory information systems, and picture archiving and communication systems), with little to no integration or interoperability between them. Furthermore, there is not just one genome for a patient, but multiple “omes” including the genome, proteome, transcriptome, epigenome, and microbiome and beyond. The lack of availability of a complete, integrated longitudinal patient record incorporating multiomics to power precision medicine has several detrimental effects. First and foremost, it results in less effective medicine, and suboptimal patient outcomes. It can also delay diagnoses where data required to support a clinical decision is not readily available. Working with an incomplete medical record can increase the risk of errors. Last but not least, this can exacerbate the lack of coordination across multidisciplinary care teams, resulting in suboptimal patient care and increased healthcare costs. For precision medicine, this presents
Microsoft creates deep, technical content to help developers enhance their proficiency when building solutions using the Azure AI Platform. Our preferred training partners redeliver our LearnAI Bootcamps for customers around the globe on topics including Azure Databricks, Azure Machine Learning service, Azure Search, and Cognitive Services. Umanis, a systems integrator and preferred AI training partner based in France, has been innovating in Big Data and Analytics in numerous verticals for more than 25 years and has developed an effective methodology for guiding customers into the Intelligent Cloud. Here, Philippe Harel, the AI Practice Director at Umanis, describes this methodology and shares lessons learned to empower customers to do more with data and AI.
2019 is the year when artificial intelligence (AI) and machine learning (ML) are shifting from being mere buzzwords to real-world adoption and rollouts across the enterprise. This year reminds us of the cloud adoption curve a few years ago, when it was no longer an option to stay on-premises alone, but a question of how to make the shift. As you draw up plans on how to best use AI, here are some learnings and methodologies that Umanis is following.
Given the ever-increasing speed of
We continue to expand the Azure Marketplace ecosystem. From February 16 to February 28, 2019, 50 new offers successfully met the onboarding criteria and went live. See details of the new offers below:
Analytics Zoo: A unified Analytics + AI platform: Analytics Zoo provides a unified analytics and AI platform that unites Spark, TensorFlow, Keras, and BigDL programs into an integrated pipeline. The pipeline can then transparently scale out to a large Hadoop/Spark cluster.
Blender 3D On Windows Server 2016: Studios around the world use Blender as their go-to 3-D software for remodeling, rendering, animation, video editing, compositing, texturing, and more. Apps4Rent helps you deploy Blender on Microsoft Azure.
CIS CentOS 7.5 Benchmark L1: This image of CentOS 7.5 is preconfigured by CIS to the recommendations in the associated CIS Benchmark. CIS Benchmarks are vendor-agnostic, consensus-based security configuration guides.
IBM DB2 Advanced Enterprise Server Edition 11.1: Install IBM DB2 Advanced Enterprise Server Edition in just a few minutes. IBM DB2 is ideal for development, test, and production infrastructure, and MidVision’s RapidDeploy is shipped for streamlined administration.
Quality assurance matters to manufacturers. The reputation and bottom line of a company can be adversely affected if defective products are released. If a defect is not detected, and the flawed product is not removed early in the production process, the damage can run in the hundreds of dollars per unit. To mitigate this, many manufacturers install cameras to monitor their products as they move along the production line. But the data may not always be useful. For example, cameras alone often struggle with identifying defects at high volume of images moving at high speed. Now, a solution provider has developed a way to integrate such existing systems into quality assurance management. Mariner, with its Spyglass solution, uses AI from Azure to achieve visibility over the entire line, and to prevent product defects before they become a problem.
Quality assurance expenses
Quality assurance (QA) management in manufacturing is time-consuming and expensive, but critical. The effects of poor quality are substantial, as they result in:
Re-work costs Production inefficiencies Wasted materials Expensive and embarrassing recalls
And worst of all, dissatisfied customers that demand returns.
Multiple variables across multiple facilities
Too many variables make product defect analysis and prediction difficult. Manufacturers need
The Internet of Things (IoT) is an ongoing journey. Three years ago, when I entered this business, the world of IoT was in its infancy. Traditional industry technology adopters understood the importance of innovation and implemented isolated solutions to address discrete business issues such as inventory management, loss prevention, logistics management, and other such processes that could be automated.
Digital transformation requires that these solutions be connected so that the data can be collected and analyzed more effectively across systems to drive exponential improvements in operations, profitability, and customer and employee loyalty. The advent of sensors and analytics at the edge plus advancements in cloud platforms and data analytics is enabling this. Systems and services are now connected to provide more holistic solutions that deliver value through operational or profitability improvements and in many cases, through new revenue streams.
The creation of these solutions typically requires an ecosystem of partners. This is where Microsoft provides a distinct advantage, through our partner-plus-platform-approach, that is driving change in IoT technology adoption. Microsoft has committed $5 billion in IoT-focused investments to grow and support our partner ecosystem–specifically through unrelenting R&D innovation in critical areas, like security, new development tools and intelligent services, artificial
The Open Compute Project (OCP) Global Summit 2019 kicks off today in San Jose where a vibrant and growing community is sharing the latest in innovation to make hardware more efficient, flexible, and scalable.
For Microsoft, our journey with OCP began in 2014 when we joined the foundation and contributed the very same server and datacenter designs that power our global Azure cloud, but it didn’t stop there. Each year at the OCP summit, we contribute innovation that addresses the most pressing challenges for our industry, including a modular and globally compatible server design and universal motherboard with Project Olympus to enabling hardware security with Project Cerberus to a next generation specification for SSD storage with Project Denali.
This year we’re turning our attention to the exploding volume of data being created daily. Data is at the heart of digital transformation and companies are leveraging data to improve customer experiences, open new markets, make employees and processes more productive, and create new sources of competitive advantage as they work toward the future of tomorrow.
Data – the engine of Digital Transformation
The Global Datasphere* which quantifies and analyzes the amount of data created, captured, and replicated in any given year
Special thanks to Lee Schlesinger and the Talend team for their contribution to this blog post.
Following the significant announcement around the continued price-performance leadership of Azure Data Warehouse in February 2019, Talend announced support of Stitch Data Loader for Azure SQL Data Warehouse. Stich Data Loader is Talend’s recent addition to its offering portfolio small and mid-market customers. With Stitch Data Loader, customers can load 5 million rows/month into Azure SQL Data Warehouse for free or scale up to an unlimited number of rows with a subscription.
All across the industry, there is a rapid shift to the cloud. Utilizing fast, flexible, and secure cloud data warehouse is an important first step in that journey. With Microsoft Azure SQL Data Warehouse and Stitch Data Loader companies can get started faster than ever. The fact that ADW can be up to 14x faster, and 94 percent less expensive than similar options in the marketplace, should only help further accelerate adoption of cloud scale analytics by customers of all sizes.
Building pipelines to the cloud with Stitch Data Loader
The Stitch team built the Azure SQL Data Warehouse integration with the help of Microsoft engineers. The solution leverages Azure Blob Storage