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MIT to launch new Office of Research Computing and Data

As the computing and data needs of MIT’s research community continue to grow — both in their quantity and complexity — the Institute is launching a new effort to ensure that researchers have access to the advanced computing resources and data management services they need to do their best work. 

At the core of this effort is the creation of the new Office of Research Computing and Data (ORCD), to be led by Professor Peter Fisher, who will step down as head of the Department of Physics to serve as the office’s inaugural director. The office, which formally opens in September, will build on and replace the MIT Research Computing Project, an initiative supported by the Office of the Vice President for Research, which contributed in recent years to improving the computing resources available to MIT researchers.

“Almost every scientific field makes use of research computing to carry out our mission at MIT — and computing needs vary between different research groups. In my world, high-energy physics experiments need large amounts of storage and many identical general-purpose CPUs, while astrophysical theorists simulating the formation of galaxy clusters need relatively little storage, but many CPUs with high-speed connections between them,” says Fisher, the Thomas A. Frank (1977) Professor of Physics, who will take up the mantle of ORCD director on Sept. 1.

“I envision ORCD to be, at a minimum, a centralized system with a spectrum of different capabilities to allow our MIT researchers to start their projects and understand the computational resources needed to execute them,” Fisher adds.

The Office of Research Computing and Data will provide services spanning hardware, software, and cloud solutions, including data storage and retrieval, and offer advice, training, documentation, and data curation for MIT’s research community. It will also work to develop innovative solutions that address emerging or highly specialized needs, and it will advance strategic collaborations with industry.

The exceptional performance of MIT’s endowment last year has provided a unique opportunity for MIT to distribute endowment funds to accelerate progress on an array of Institute priorities in fiscal year 2023, beginning July 1, 2022. On the basis of community input and visiting committee feedback, MIT’s leadership identified research computing as one such priority, enabling the expanded effort that the Institute commenced today. Future operation of ORCD will incorporate a cost-recovery model.

In his new role, Fisher will report to Maria Zuber, MIT’s vice president for research, and coordinate closely with MIT Information Systems and Technology (IS&T), MIT Libraries, and the deans of the five schools and the MIT Schwarzman College of Computing, among others. He will also work closely with Provost Cynthia Barnhart.

“I am thrilled that Peter has agreed to take on this important role,” says Zuber. “Under his leadership, I am confident that we’ll be able to build on the important progress of recent years to deliver to MIT researchers best-in-class infrastructure, services, and expertise so they can maximize the performance of their research.”

MIT’s research computing capabilities have grown significantly in recent years. Ten years ago, the Institute joined with a number of other Massachusetts universities to establish the Massachusetts Green High-Performance Computing Center (MGHPCC) in Holyoke to provide the high-performance, low-carbon computing power necessary to carry out cutting-edge research while reducing its environmental impact. MIT’s capacity at the MGHPCC is now almost fully utilized, however, and an expansion is underway.

The need for more advanced computing capacity is not the only issue to be addressed. Over the last decade, there have been considerable advances in cloud computing, which is increasingly used in research computing, requiring the Institute to take a new look at how it works with cloud services providers and then allocates cloud resources to departments, labs, and centers. And MIT’s longstanding model for research computing — which has been mostly decentralized — can lead to inefficiencies and inequities among departments, even as it offers flexibility.

The Institute has been carefully assessing how to address these issues for several years, including in connection with the establishment of the MIT Schwarzman College of Computing. In August 2019, a college task force on computing infrastructure found a “campus-wide preference for an overarching organizational model of computing infrastructure that transcends a college or school and most logically falls under senior leadership.” The task force’s report also addressed the need for a better balance between centralized and decentralized research computing resources.

“The needs for computing infrastructure and support vary considerably across disciplines,” says Daniel Huttenlocher, dean of the MIT Schwarzman College of Computing and the Henry Ellis Warren Professor of Electrical Engineering and Computer Science. “With the new Office of Research Computing and Data, the Institute is seizing the opportunity to transform its approach to supporting research computing and data, including not only hardware and cloud computing but also expertise. This move is a critical step forward in supporting MIT’s research and scholarship.”

Over time, ORCD (pronounced “orchid”) aims to recruit a staff of professionals, including data scientists and engineers and system and hardware administrators, who will enhance, support, and maintain MIT’s research computing infrastructure, and ensure that all researchers on campus have access to a minimum level of advanced computing and data management.

The new research computing and data effort is part of a broader push to modernize MIT’s information technology infrastructure and systems. “We are at an inflection point, where we have a significant opportunity to invest in core needs, replace or upgrade aging systems, and respond fully to the changing needs of our faculty, students, and staff,” says Mark Silis, MIT’s vice president for information systems and technology. “We are thrilled to have a new partner in the Office of Research Computing and Data as we embark on this important work.”
Quelle: Massachusetts Institute of Technology

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Cloud Native 5 Minutes at a Time: Docker Compose and Next Steps

One of the biggest challenges for implementing cloud native technologies is learning the fundamentals—especially when you need to fit your learning into a busy schedule. In this series, we’ll break down core cloud native concepts, challenges, and best practices into short, manageable exercises and explainers, so you can learn five minutes at a time. These … Continued
Quelle: Mirantis

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Introducing new Google Cloud manufacturing solutions: smart factories, smarter workers

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

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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