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Today, manufacturers are advancing on their digital transformation journey, betting on innovative technologies like cloud and AI to strengthen competitiveness and deliver sustainable growth. Nearly two thirds of manufacturers already use cloud solutions, according to McKinsey. The actual work of scaling digital transformation projects from proof of concept to production, however, still remains a challenge for the majority of them, according to analysts.We believe the scalability challenges revolve around two factors—the lack of access to contextualized operational data and the skills gap to use complex data science and AI tools on the factory floor.To ensure manufacturers can scale their digital transformation efforts into production, Google Cloud is announcing new manufacturing solutions, specifically designed for manufacturers’ needs. The new manufacturing solutions from Google Cloud give manufacturing engineers and plant managers access to unified and contextualized data from across their disparate assets and processes.Let’s take a look at the new solutions as we follow the data journey, from the factory floor to the cloud:Manufacturing Data Engine is the foundational cloud solution to process, contextualize and store factory data. The cloud platform can acquire data from any type of machine, supporting a wide range of data, from telemetry to image data, via a private, secure, and low cost connection between edge and cloud. With built-in data normalization and context-enrichment capabilities, it provides a common data model, with a factory-optimized data lakehouse for storage. Manufacturing Connect is the factory edge platform co-developed with Litmus Automation that quickly connects with nearly any manufacturing asset via an extensive library of 250-plus machine protocols. It translates machine data into a digestible dataset and sends it to the Manufacturing Data Engine for processing, contextualization and storage. By supporting containerized workloads, it allows manufacturers to run low-latency data visualization, analytics and ML capabilities directly on the edge.Built on the Manufacturing Data Engine are a growing set of data analytics and AI use cases, enabled by Google Cloud and our partners:Manufacturing analytics & insights: An out-of-the-box integration with Looker templates that delivers a dashboarding and analytics experience. As an easy-to-use, no-code data and analytics model, it empowers manufacturing engineers and plant managers to quickly create and modify custom dashboards, adding new machines, setups, and factories automatically. The solution enables drill down into the data against KPIs, or on-demand to uncover new insights and improvement opportunities throughout the factory. Shareable insights unlock collaboration across the enterprise and with partners.Predictive maintenance: Pre-built predictive maintenance machine learning models allow manufacturers to deploy in weeks without compromising on prediction accuracy. Manufacturers can continuously improve their models and refine them in collaboration with Google Cloud engineers. Machine-level anomaly detection: A purpose-built integration that leverages Google Cloud’s Time Series Insights API on real-time machine and sensor data to identify anomalies as they occur and provide alerts. “The growing amount of sensor data generated on our assembly lines creates an opportunity for smarter analytics around product quality, production efficiency, and equipment health monitoring, but it also means new data intake and management challenges,” said Jason Ryska, director of manufacturing technology development at Ford Motor Company. “We worked with Google Cloud to implement a data platform now operating on more than 100 key machines connected across two plants, streaming and storing over 25 million records per week. We’re gaining strong insights from the data that will help us implement predictive and preventive actions and continue to become even more efficient in our manufacturing plants.”“With the tight integration of a powerful factory edge solution with Google Cloud, it is easier than ever for factories to tap into cloud capabilities,” said Masaharu Akieda, general manager for the Digital Solutions Division at KYOCERA Communication Systems Company. “Google Cloud’s solutions enable a broader group of users beyond data scientists to quickly access, analyze and use data in a variety of use cases. We are excited to partner with Google Cloud as we implement new manufacturing solutions to optimize production operations and consistently increase quality.”“As the global innovator of solid state cooling and heating technology, we’ve developed a sustainable manufacturing platform that uses less water, less electricity, and less chemical waste,” says Jason Ruppert, chief operations officer of Phononic. “This partnership with Google Cloud allows us to contextualize data across all of our manufacturing processes – ultimately providing us the analytics and insights to optimize our operations and continue to bring to the world products that cool sustainably, reducing greenhouse gas (GhG) emissions and improving the environment.”A growing number of partners are extending Google Cloud’s manufacturing solutions, from connectors, to AI-driven use cases. Take a look at what our partners are saying about the Manufacturing Data Engine and Manufacturing Connect at our upcoming Google Cloud Manufacturing Spotlight.With Google cloud’s new manufacturing solutions, three critical pieces of smart manufacturing operations are strengthened and integrated: factory-floor engineers, data, and AI. Empowering factory-floor engineers to be the hub of smart manufacturingOver the last few years, the manufacturing industry contributed more than 10% of the U.S. gross domestic product, or 24% of GDP with indirect value (i.e. purchases from other industries) included. This is also the sector that employs approximately 15 million people, representing 10% of total U.S. employment. However, more than 20% of manufacturers’ workforce in the US is older than 55 years, and an average age of 44 years old – with similar patterns seen across the world. Finding new talent to replace the retiring workforce is getting increasingly harder for manufacturers.Companies therefore need to both enable their existing workforce, while making it more attractive to new talent to join. This balance requires making critical technology such as Cloud and AI accessible, easier to use, and deeply embedded in manufacturers’ day-to-day operations.Google Cloud’s manufacturing solutions are designed with this end in mind. Combining fast implementation and ease-of-use, powerful digital tools are put directly into the hands of the manufacturers’ workforce to uncover new insights and optimize operations in entirely new ways.Key parts of the solution are low- to no-code in setup and use, and therefore are suitable for a large variety of end users. Built for scale, the solutions allow for template-based rollouts and encourage reuse through standardization. Designed with best practices in mind, manufacturers are enabled to focus precious resources on use cases, instead of the underlying infrastructure.Manufacturing engineers can visualize and drill down into data using Manufacturing Analytics & Insights, built on Looker’s business intelligence engine. Being integrated with the Manufacturing Data Engine, its automatic configuration provides an up-to-date view into any aspect of manufacturing operations. From the COO to plant managers and factory engineers, users are enabled to easily browse and explore factory data on the enterprise, factory, line machine, and sensor level.Besides designing manufacturing solutions from the ground up for ease-of-use, Google Cloud and partners are actively helping manufacturers in upskilling their workforce capabilities with a dedicated enablement service.Making every data point accessible and actionableData is the backbone of digital manufacturing transformation and manufacturers have a potential abundance of data: performance logs from a single machine can generate 5 gigabytes of data per week, and a typical smart factory can produce 5 petabytes per week.However, this wealth of data and the insights contained within it remain largely inaccessible for many manufacturers today: data is often only partially captured, and then locked away in a variety of disparate and proprietary systems.Manufacturing Connect, co-developed with Litmus Automation, provides an industry-leading breadth of 250-plus native protocol connectors to quickly connect to and acquire data from nearly any production asset and system with a few clicks. Integrated analytics features and support for containerized workloads provide manufacturers with the option for on-premise processing of data.A complementary cloud component allows manufacturers to centrally manage, configure, standardize and update edge instances across all their factories for roll-outs on a global scale. Integrated in the same UI, users can also manage downstream processing of data sent to the cloud by configuring Google Cloud’s Manufacturing Data Engine solution.The Manufacturing Data Engine provides structure to the data, and allows for semantic contextualization. Doing so, data is made universally accessible and useful across the enterprise. By abstracting away the underlying complexity of manufacturing data, manufacturers and partners are enabled to develop high value, repeatable, scalable, and quick to implement analytics and AI use cases.AI for smart manufacturing demands a broad partner ecosystemManufacturers recognize the value of AI solutions in driving cost and production optimizations. So much so that several of them have active patents on AI initiatives. In fact, according to research from Google in June, 2021, 66% of manufacturers that use AI in their day-to-day operations report their reliance on AI is increasing. Google Cloud helps manufacturers put cloud technology and artificial intelligence to work helping factories run faster and smoother. Customers using the Manufacturing Data Engine from Google Cloud can directly access Google Cloud’s industry-leading Vertex AI platform, which offers integrated AI/ML tools ranging from AutoML for manufacturing engineers, to advanced AI tools for experts to fine-tune results. With Google Cloud, AI/ML use case development has never been more accessible for manufacturers.Crossing the scalability chasm for using the power of cloud and AI in manufacturingOur mission is to accelerate your digital transformation by bridging data silos, and to help make every engineer into a data scientist with easy-to-use AI technologies and an industry data platform. Join us at the Google Cloud Manufacturer Spotlight to learn more.The new manufacturing solutions will be demonstrated in person for the first time at Hannover Messe 2022, May 30–June 2. Visit us at Stand E68, Hall 004, or schedule a meeting for an onsite demonstration with our experts.Related ArticleLeading with Google Cloud & Partners to modernize infrastructure in manufacturingLearn how Google Cloud Partner Advantage partners help customers solve real-world business challenges in manufacturing.Read Article
Quelle: Google Cloud Platform

6. Mai 2022

da Agency

Solving for food waste with data analytics in Google Cloud

With over ⅓ of the food in the USA ending up as waste according to the USDA, it is a compelling challenge to address this travesty. What will happen to hunger, food prices, trash reduction, water consumption, and overall sustainability when we stop squandering this abundance?Beginning with the departure from the farm to the back of the store, the freshness clock continues to run. Grocers work very hard to purchase high quality produce items for their customers and the journey to the shelf can take a toll in both quality and remaining shelf life. Suppliers focus on delivering their items through the arduous supply chain journey to the store with speed and gentle handling. The baton is then passed to the store to unload and present the items to customers with care to sell through each lot significantly before the expiration or sell by date. This is to ensure that the time spent in the customer’s home is ample to ensure a great eating experience as well. Food waste is a farm to fork problem with opportunity at every step of the chain, but today we will focus on the segment that the grocery industry oversees.With the complexities of weather, geopolitical issues, distribution, sales variability, pricing, promotions, and inventory management, it seems daunting to impact waste. Fortunately, data analytics and machine learning in the cloud is a powerful weapon in the fight against food waste. Data Scientists harness knowledge to draw meaning from data turning that data into decision driving information. One key Google has been working on to accelerate value is to break down data silos and leverage machine learning to realize better outcomes, using our Google Data Cloud platform. This enables better planning through demand forecasting, Inventory management, assortment planning, and dynamic pricing and promotions.That sounds great but how does it work? Let’s walk through a day in the life journey to see how the integrated Google Data Cloudplatform can change the game for good. Our friendly fictitious grocer FastFreshFood is committed to selling high quality perishable items to their local market. Their goal is to minimize food waste and maximize revenue by selling as much perishable fresh food as possible before the sell by date. Our fictitious grocer in partnership with Google Cloud could build a solution that will take a significant bite out of their food waste volume and better satisfy customers. Sales through the register and online are processed in real time with Datastream, Dataflow to keep an accurate perpetual inventory by minute of every single item.A Demand forecasting model using machine learning algorithms in BigQuery then identifies needs for back room replenishment, so Direct Store Delivery and daily store Distribution Centers manage ordering more efficiently to ensure just the right amount of each product each day.Realtime reporting dashboards in Looker with alerting capabilities enable the system to operate with strong associate support and understanding. The reporting suite shows inventory levels into the future, daily orders, and at risk items.The pricing algorithm could also alert store leadership concerning any items that will not sell through and suggest real time in store specials resulting in zero waste at shelf and maximized revenue.This approach is not just for perishable categories and is a pattern that works well for in-store produced items and center store items. The key point is that by bringing ML/AI to difficult business problems grocers are reinventing what is possible for both their profitability and sustainability.The technical implementation of this design pattern in Google Cloud leverages Datastream, Dataflow, BigQuery and Looker products, it is detailed in a technical tutorial accompanying this blog post.In partnership with Google Cloud, retailers can solve complex problems with innovative solutions to achieve higher quality, lower cost, and provide great customer experiences. To learn more from this and other use cases, please visit our Design Patterns website.Curious to learn more? We’re excited to share what we know about tackling food waste at Google, a topic we’ve been working on in the last decade as we’ve embarked on reducing our own food waste in our operations in over 50 countries in the world. The Google Food for Good team works exclusively on Google Cloud Platform with our partners on this topic. Two additional articles below. Silos are for food, not for data – tackling food waste with technologyThis business Cloud blog directly addresses information silos that currently exist across many nodes in the food system and how to break down cultural and organizational barriers to sharing. “Unsiloing” data to work toward solving food waste and food insecurity This follow-on technical Cloud blog articulates the path to setting up data pipelines, translating between data sets (not everyone calls a tomato a tomato!) and making sense of emergent insights.Related ArticleSilos are for food, not data—tackling food waste with technologySee how Kroger, Feeding America, St. Mary’s and other food banks joined forces to solve the problems of food waste and food insecurity us…Read Article
Quelle: Google Cloud Platform

6. Mai 2022

da Agency

CIS hardening support in Container-Optimized OS from Google

At Google, we follow a security-first philosophy to make safeguarding our clients’ and users’ data easier and more scalable, with strong security principles built into multiple layers of Google Cloud. In line with this philosophy, we want to make sure that our Container-Optimized OS adheres to industry-standard security best practices. To this end, we released a CIS benchmark for Container-Optimized OS that codifies the recommendations for hardening and security measures we have been using. Our Container-Optimized OS 97 releases now support CIS Level 1 compliance, with an option to enable support for CIS Level 2 hardening.CIS benchmarks help define the security recommendations for various software systems, including various operating systems. In the past, Google had developed a CIS benchmark for Kubernetes as part of the continued contributions to the container orchestration space. We decided to build a CIS benchmark for Container-Optimized OS because they are well recognized across the industry, are created and reviewed in open source, and can provide a good baseline when it comes to hardening your operating systems. Our benchmarks for Container-Optimized OS are based on the CIS benchmarks defined by their security community for distribution-independent Linux OSes. In addition to applying some of the security recommendations for generic Linuxes—such as making file permissions more strict—we included measures to support hardening specific to Container-Optimized OS, such as verifying that the OS has the capabilities for checking filesystem integrity with dm-verity or that logs can be exported to Cloud Logging. We also removed some checks that don’t apply to Container-Optimized OS due to its minimal OS footprint that can reduce the attack surface. Container-Optimized OS 97 and later versions come with support for CIS Level 1 and can allow users to optionally apply support for Level 2 hardening as well.Compliance is not just about a one-time hardening effort, however. You will need to ensure that the deployed OS images stay within compliance throughout their life. At Google, we continually run scans on our Google Cloud projects to help verify that our VMs and container images are kept up-to-date with the latest CIS security guidelines. To help scan a wide range of products with a low resource usage overhead, we developed Localtoast, our own open-source configuration scanner.Localtoast is highly customizable and can be used to detect insecure OS configurations on local and remote machines, VMs, and containers. Google uses Localtoast internally to help verify CIS compliance on a wide range of Container-Optimized OS installations and other OSes. Its configuration and scan results are stored in the same Grafeas format that deploy-time security enforcement systems such as Kritis use, which can make it easier to integrate with existing supply chain security and integrity tooling. See this video for a showcase of how you can use this Localtoast scanner on COS.Included in the Localtoast repo is a set of scan configuration files that help scan Container-Optimized OS’ CIS benchmarks. For other Linux OSes, we include a fallback config which supports and is based on the distribution-independent Linux CIS benchmarks and aims to help provide relevant security findings for a wide range of Linuxes—with support for more OSes coming in the future.Apart from the configs for scanning live instances, we also released modified configs for scanning container images.Container-Optimized OS 97 and above comes with Localtoast and the Container-Optimized OS-specific scanning config that supports CIS compliance pre-installed. We welcome you to try out our user-guide, and hope that the provided tools will help you get a step further in your journey toward keeping your cloud infrastructure secure. If you have any questions, don’t hesitate to reach out to us.Related Article4 new ways Citrix & Google Cloud can simplify your Cloud MigrationCitrix and Google Cloud simplify your cloud migration. The expanding partnership between Citrix and Google Cloud means that customers con…Read Article
Quelle: Google Cloud Platform

6. Mai 2022

da Agency

Optimize and scale your startup on Google Cloud: Introducing the Build Series

We understand that at each stage of the startup journey, you need different levels of support and resources to start, build and grow. To help with your journey, we created the Google Cloud Technical Guide for Startups to help your organization across these different milestones.Technical Guides for Startups to support your startup journeyThe Google Cloud Technical Guides for Startups series includes a video series and handbooks, consisting of three parts optimized for different stages of a startup’s journey.The Start Series: Begin by building, deploying and managing new applications on Google Cloud from start to finish.The Build Series: Optimize and scale existing deployments to reach your target audiences.The Grow Series: Grow and attain scale with deployments on Google Cloud.The Start Series – is fully available on this playlist. In this series, we introduced topics to get you started on Google Cloud. This included setting up your project, choosing the right compute option, configuring databases, networking, as well as understanding support and billing.Now that you have applications running on Google Cloud, it is time to take the next step to optimize and scale these deployments.Kicking off the Build SeriesWith our Start Series complete, we are happy to announce the second program in the series – the Build Series! The Build Series focuses on optimizing deployments and scaling your business, enabling you to build a foundation to accelerate your startups’ growth in the future. We will dive into many exciting topics, ranging from startup programs to Google Cloud’s data analytics and pipelines solutions, machine learning, API management and more. You will learn to gain insights from your data and to better manage and secure your applications, which will accelerate scale and understanding of your end user. Our first episode shares an overview of these topics, and features our new website which has many useful startup resources and technical handbooks. Watch our kick off video to find out more.Embark on the journey togetherWe hope that you will join us on this journey, as we Start, Build, and Grow together. Get started by checking out our website and our full playlist on the Google Cloud Tech channel. Don’t forget to subscribe to stay up to date. See you in the cloud!Related ArticleBootstrap your startup with the Google Cloud Technical Guides for Startups : A Look into the Start SeriesAnnouncing the summary of the first phase of the Google Cloud Technical Guides for Startups, a video series for technical enablement aime…Read Article
Quelle: Google Cloud Platform

6. Mai 2022

da Agency

Advancing systems research with open-source Google workload traces

With rapid expansion of internet and cloud computing, warehouse-scale computing (WSC) workloads (search, email, video sharing, online maps, online shopping, etc.) have reached planetary scale and are driving the lion’s share of growth in computing demand. WSC workloads also differ from others in their requirements for on-demand scalability, elasticity and availability. Many studies (e.g., Profiling a warehouse-scale computer) and books (e.g., The Datacenter as a Computer: Designing Warehouse-Scale Machines) have pointed out that WSC workloads have fundamentally different characteristics than traditional benchmarks and require changes to modern computer architecture to achieve optimal efficiency. Google workloads have data and instruction footprints that go beyond the capacity of modern CPU caches, such that the CPU spends a significant portion of its time waiting for code and data. Simply increasing memory bandwidth would not solve the problem, as many accesses are in the critical path for application request processing; it is just as important to reduce memory access latency as it is to increase memory bandwidth.Over the years, the computer architecture community has expressed the need for WSC workload traces to perform architecture research. Today, we are pleased to announce that we’ve published select Google workload traces. These traces will help systems designers better understand how WSC workloads perform as they interact with underlying components, and develop new solutions for front-end and data-access bottlenecks.We captured these workload traces using DynamoRIO on computer servers running Google workloads — you can find more details at https://dynamorio.org/google_workload_traces.html. To protect user privacy, these traces only contain instruction and memory addresses. We have found these traces useful for understanding WSC workloads and seeding internal research on processor front-ends, on-die interconnects, caches and memory subsystems, etc. — all areas that greatly impact WSC workloads. For example, we used these traces to develop AsmDB. Likewise, we hope these traces will enable the computer architecture community to develop new ideas that improve performance and efficiency of other WSC workloads.
Quelle: Google Cloud Platform

6. Mai 2022

da Agency

Azure Health Data Services: Engineering product for partners

The healthcare industry has come a long way from putting pen to paper on a pharmacy script or clinical SOAP note to now, being able to deliver primary care in the emerging hospital at home. My career in the healthcare and life sciences (HLS) industry has spanned different roles including: a military clinician, life science entrepreneur, clinical research application scientist, and business leader. Currently, I head the Partner Alliances team for Microsoft’s global health and Life sciences Cloud and Data engineering and product group. Today, I consider myself an HLS generalist bridging the gap between engineering and the application of it in the wild. I look forward to continuing to listen to the needs, implement solutions, and partner with others to bring forward meaningful change in healthcare.

Last month, we launched Azure Health Data Services, a platform as a service (PaaS) offering designed exclusively to support Protected Health Information (PHI) in the cloud, built on the global open standards Fast Healthcare Interoperability Resources (FHIR)® and Digital Imaging Communications in Medicine (DICOM). Watching the team work to develop this product, I feel compelled to share how intentional our product team is at building healthcare technologies for an industry that is currently experiencing historically unprecedented transformation. We are deploying technology that can ingest, transform, and persist data, allowing our customers to use their data to span workflows from discovery research to clinical end points.1 The underlying technology enables our customers to engage in activities ranging from novel biomarker identification to virtual clinical decision support. For example, today our customers can combine cellular assay data, pathology data, molecular imaging, genomics, handwritten, voice, and text derived notes. With so much data, the goal is to enable our customers to derive insights from a single system of record, so that they can optimize the user experience for patient, research and clinical workflows so that adherence to treatment increases, scientists gain faster contextual evidence to support their early discoveries and clinicians can spend more time focused on delivering healthcare without experiencing burnout and information overload. The bottom line is, when you can bring these data sets together in a meaningful way, you inherently increase your signal to noise ratio since you are no longer looking for a needle in a haystack; you are looking for a book in a library.

Five years ago, under the leadership of Peter Lee, Microsoft made a purposeful decision that enabled us to lead the way in cloud, data, AI, and innovation. In 2020, Microsoft won the Frost and Sullivan Best Practice Award for our commitment to global AI for healthcare IT growth, and our innovation and leadership in the industry. The Microsoft executive health leadership team realized that we needed a common standards-based platform for healthcare and life sciences data and a secure compliant environment for the industry to build on. To accomplish this, we would need to contribute to the interoperability momentum for FHIR® standard. We also knew we had to lead with partners that know the space better than we do. We are now focused on building the most trusted, health data platform designed with security and compliance in mind, that is ready to ingest a variety of data types and standards, workflow accelerators, and scenario-specific features. Our hope is that this will enable our ecosystem of partners to push the last mile of innovation for our shared customers in provider, pharmaceutical, payor, and life sciences.
With our partners as the foundation of our business, we will maintain competitive velocity in such transformational times.

Our approach to building Azure Health Data Services has been to support our partners by building and managing the underlying cloud technology so they can remain focused on the front-line industry scenarios. We appreciate the intimate business propriety required to remain innovative and competitive. For this model to work, we must begin and end with the question “are we going to build, buy, or partner for this given product, feature, or capability?” These decisions are rigorous and informed by key industry opinion leaders, the partner ecosystem, and our leadership teams.

Taking inspiration from industry leaders

To support this thesis, we built Health Data Services Partner Alliances team. Our charter is to listen to industry leaders like Tom Arneman at EPAM, BJ Moore at Providence, and the broader trusted advisors across the Microsoft health and life sciences partnership ecosystem. This industry driven feedback challenged us to deliver interoperable, FHIR enabled services and partner led solutions. Partners like Redox, Onyx, 3Cloud, EPAM, SAS, Efferent, Teladoc and ZS Services have been instrumental in providing direct user feedback.

These solutions are coming to life with our mutual customers across the provider, payer and pharma industries. Together we are delivering diversified solutions across the HLS continuum that includes users like translational oncology clinical trial coordinators to care providers remotely accessing their patients. We have worked closely to evolve features with early movers that have deep expertise in multi-modal interoperability deployments, FHIR resource creation, MedTech eventing features for remote patient monitoring, and DICOM for imaging. Now we are scaling these managed services with global partners, their large enterprise HLS practices and industry leading ISV solutions. We are deploying a breadth motion and application toolset that will make it simpler for our partners to build new transactional and analytic SMART on FHIR and other applications on top of Azure Health Data Services.

These partners are the cornerstone of building solutions for the greatest challenges we see today and foresee in years to come. At Microsoft we focus on aligning with them on a defined customer and business opportunity, we then commit resources and appropriate enablement to deliver timely and measurable business value. When we execute in this way, our likelihood of optimized collaboration, product; market fit, market adoption, and long-term partnership is much greater.

Azure Health Data Services is built with the goal of enabling our customers to be able to do more with their health data. We want our partners to be able to provide them solutions to do so—solutions optimized for Azure, Microsoft Cloud for Healthcare and Azure Health Data Services which can help them transform patient experience, discover new insights, and accelerate innovation.

Learn more

Learn more about Azure Health Data Services.
Read our recent blog, “Microsoft launches Azure Health Data Services to unify health data and power AI in the cloud.”
Learn more about Microsoft Cloud for Healthcare.
Learn more about how health companies are using Azure to drive better health outcomes.

1EPAM Debuts New Cloud-Powered Digital Clinical Trials Platform.

®FHIR is a registered trademark of Health Level Seven International, registered in the U.S. Trademark Office and are used with their permission.
Quelle: Azure