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Engineer, Machine Learning
2021-04-16 12:13:10| Space-careers.com Jobs RSS
ROLE DESCRIPTION SUMMARY The position is responsible for providing technical expertise for creating software to automate the generation of optimized solutions using a variety of algorithms and analytics best practices. The incumbent will develop code for an exciting project that will be key to plan and optimize SES satellite capacity for SESs most advanced fully digitized satellites, the new O3b mPOWER constellation and SES17 satellite to start. The position holder will leverage on a wide range of technical, mathematical, and analytical experience and will effectively communicate designs to management and to other organizational teams. The Machine Learning Engineer will provide organization leadership in the development of optimization systems. This role requires excellent communication skills, a strong foundation in computer science and mathematics and a wide range of experience. PRIMARY RESPONSIBILITIES KEY RESULT AREAS Develop highquality, comprehensive software designs and architectures to create satellite optimization systems Analyze engineering requirements and constraints to design, prototype and build data models and optimization solutions Provide technical guidance to the business on best use of constraint programming Ensure tools and techniques used are reliable and provide high quality results Evaluate a wide range of technologies as part of a solution design and document the resulting designs and conclusions Ensure reliability, maintainability, and security best practices are enforced Identify multiple technical solutions for a given problem and help document those solutions Provide crossteam guidance on machine learning best practices Effectively communicate designs and procedures in writing Test and peerreview proposed software implementations Keep up to date with latest technologies COMPETENCIES SelfStarter with a high level of personal accountability Ability to set priorities and focus Ability to take ownership and drive a task to conclusion without supervision Ability to work autonomously and independently, and to take initiatives when required Commitment to deadlines and willingness to meet tight development schedules Ability to work efficiently both autonomous and in interdisciplinary teams Excellent communication and presentation skills, ability to communicate clearly to technical and nontechnical audiences Excellent written communication skills Demonstrate effective intercultural awareness Proven mindset of helping others to succeedmentoring QUALIFICATIONS EXPERIENCE Masters Degree in Computer Science, Statistics, Data Science or equivalent qualifications At least 5 years experience in statistical, optimization or data science roles Expert in a use of advanced analytical techniques involving time series, constraint programming, and data streams Experience with machine learning and deep learning for time series is a plus Expert programming experience with Python and its statistical libraries Programming experience with C Proficient in large database interaction using SQL Preferred proficiency with Git Azure experience with Databricks Spark, PySpark and other platforms a plus Fluency in English, any other language is considered as an asset Willingness to travel internationally Apply HERE
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machine learning
New machine learning method accurately predicts battery state of health
2021-04-12 11:55:38| Green Car Congress
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health
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learning
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
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SLAC, MIT, TRI researchers advance machine learning to accelerate battery development; insights on fast-charging
2021-03-09 09:55:37| Green Car Congress
Tags: development
mit
learning
machine
Physics versus machine learning models
2021-02-27 09:12:08| Oil IT Journal - www.oilit.com
AAPG/SEG/SPE Energy in Data webinar hears from Hess on data-driven models in shale exploration. Corva on ROP drilling prediction. Schlumberger use both ML and physics! Xecta don't use ML on small data! Data-driven reserves reporting for anyone?
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