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Tag: framework
StoreDot announces new framework agreement with EVE Energy to manufacture silicon-dominant Extreme Fast Charge (XFC) Battery for EVs
2021-05-20 11:55:36| Green Car Congress
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Fisker and Foxconn sign framework agreements for Project PEAR; new FP28 platform
2021-05-14 09:55:43| Green Car Congress
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Java Software Engineer with Spring Framework
2021-04-09 15:13:33| Space-careers.com Jobs RSS
Job Description We are looking for an experienced Java Software Engineer with focus on the Spring Framework to join our Engineering Division in Germany. This is foreseen as a fulltime position, but parttime applications will be considered. This position has a strong focus on developing performing server applications to support our customers in their operations. Creative flexibility ensures that you can develop innovative solutions in an exciting domain. You will be involved in all phases of the software development lifecycle in current and future projects. You will work closely with other members of the Engineering Division in Germany, Italy and Switzerland. This role has a lot of potential for career development, including growing involvement and responsibility in the company activities. If you are passionate about software technologies, enthusiastic about space, and you want a versatile role in an international and distributed team, this is the position for you! Who we are Solenix is an independent and international company providing innovative engineering and consulting services in the space market. Among our customers are space agencies like ESA and EUMETSAT. We are specialised in distributed systems and client applications, using modern technologies with a focus on high performing, robust and light solutions. Our Engineering Division is a group of motivated, dynamic and creative people who enjoy highquality work, as well as a relaxed and flexible work atmosphere. Required Skills and Experience Bachelor or Master in Computer Sciences, preferably with a focus on software engineering Proactive attitude with initiative and interest in challenging solutions Excellent communication skills in an international environment Fluency in English, both spoken and written particularly in technical documentation Knowledge and practical experience in the following technologies is required Java 11 Spring Boot, Framework, Data, and Security applied to Web applications RESTful Services including JSON and XML SQL Databases PostgreSQL andor MariaDB Maven andor Gradle Desirable Skills and Experience Spring Integration, Batch, AMQP JPA, JMS and JMX Relevant network protocols TCP, HTTP, DNS Docker containers Continuous integration environments and source control management systems and their respective tools, e.g. GitLab and SonarQube Developing in LinuxUnix environments Web applications development Python and shell scripting Work Location Darmstadt, Germany andor in home office with regular meetings in Darmstadt. Dates Application Deadline 30 April 2021 Start of Work As soon as possible Important Notes Before applying to this position, please read the page How to Apply on the Solenix website under Career. Applicants must be EU citizens or have a valid work and residence permit in Germany. Security, identity and reference checks on the candidates are part of the recruitment process. Job Application Please send your applications electronically to careersolenix.ch before the application deadline 30 April 2021. Learn more about us in Facebook.
Tags: software
spring
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Operationalize machine learning with the Model Factory Framework
2021-03-10 16:19:53| The Webmail Blog
Operationalize machine learning with the Model Factory Framework nellmarie.colman Wed, 03/10/2021 - 09:19 Businesses increasingly rely on data to make decisions, as they attempt to re-create successes and avoid failures of the past. Traditionally, this means businesses have taken a reactive approach, where they make decisions for tomorrow, based on performance data from the past. But with machine learning, businesses can now harness their data to peek into possible future outcomes. From financial forecasting, churn prevention and predictive maintenance, to inventory management and simply identifying the next best action, machine learning is empowering businesses to make better-informed decisions. While machine learning is an incredibly powerful tool, implementing machine learning models for real-world application can be highly challenging. In fact, according to IDC, over a fourth of AI and machine learning initiatives fail. The culprits are multi-faceted: Lack of developer experience with machine learning Poor data quality and challenging operationalization Time-consuming processes, such as the need to repeatedly train new datasets Lack of a standardized set of best practices that integrate CI/CD, DevOps, DataOps and software engineering practices An abundance of tooling, processes and frameworks and data and operations teams that have their own, unique preferences In order to address these challenges and bridge the gap between teams, you need a standardized framework, agnostic of platform or tooling. The Model Factory Framework The Rackspace Technology Model Factory Framework is designed with all of these challenges in mind. It provides a coherent mechanism, so that your organizations data and operations teams can collaborate, develop models, automate packaging and deploy to multiple environments while preventing deployment delays, incompatibilities and other problems. Its a cloud-based machine learning lifecycle management solution an architectural pattern rather than a product. Also, since its open and modular, you can integrate it with AWS services and industry-standard automation tools such as Jenkins, Airflow, AWS CodePipeline for data processing. And given that the machine learning lifecycle is complex, with multiple building, training, testing and validation stages across data analysis, model development, deployment and monitoring the Model Factory Framework integrates Amazon SageMaker, an AI and machine learning services stack that includes: AI services that provide pre-trained models for ready-made vision, speech, language processing, forecasting and recommendation engine capabilities Machine learning services that provide pre-configured environments within which you can build, train and deploy deep learning capabilities into your applications The Amazon SageMaker stack also supports all the leading machine learning frameworks, interfaces and infrastructure options, for maximum flexibility. Key benefits of the Model Factory Framework The Model Factory Framework can help you cut the entire machine learning lifecycle from more than 25 steps, down to under 10. It further accelerates the process by automating handoffs between the different teams involved and by simplifying troubleshooting which it achieves by supplying a single source of truth for machine learning management. For data scientists, the Model Factory Framework provides a standardized model development environment, the ability to track experiments, training runs and resulting data, automated model retraining and up to 60% savings on compute costs through scripted access to spot instance training and hyperparameter optimization (HPO) training jobs in QA. For operations teams, the framework automates model deployment across development, QA and production environments. It also provides a registry for model version history tracking as well as tools for diagnostics, performance monitoring and mitigating model drift. For the organization, the framework provides a model lineage for governance and regulatory compliance, improves time to insights and accelerates ROI, while reducing effort to get machine learning models into production. Get started with the Model Factory Framework If you would like to learn about the Rackspace Technology Model Factory Framework in more detail and explore how it improves processes from model development to deployment, monitoring and governance download our whitepaper, Moving from machine learning models to actionable insights faster, where we explore: An overview of the machine learning lifecycle and its challenges How DevOps practices are misaligned to the machine learning lifecycle The Model Factory Framework overview, tools and processes How the Model Factory Framework cuts model deployment from 25 to as few as 10 steps Operationalize machine learning with the Model Factory FrameworkWhile machine learning is an incredibly powerful tool, implementing machine learning models for real-world application is tough. Discover how the Model Factory Framework addresses these challenges, while also accelerating the process reducing the entire machine learning lifecycle by 60%.Move from machine learning models to actionable insights, faster./lp/automating-production-level-mlops-aws-whitepaperDownload the whitepaper
Tags: model
learning
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Israel, Cyprus Agree on Framework for Settling Offshore Gas Dispute
2021-03-09 15:14:09| OGI
Development of the Aphrodite gas field in Cypriot waters has been held up because a small part of it stretches into Israels maritime zone and another gas field there. The Cypriot field was first discovered in 2011.
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