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Tag: learning
Reinforcement learning at the Distillation Gym
2021-02-27 09:12:08| Oil IT Journal - www.oilit.com
AI proof of concept from Cambridge University showcases Cape Open simulation of hydrocarbon processing.
Tags: learning
gym
reinforcement
distillation
Is learning a foreign language worth it?
2021-02-24 21:49:00| National Hog Farmer
Process is doable for producer and helpful for operation if person is willing to put in the time.
Tags: it
language
learning
worth
Machine Learning Engineer
2021-02-19 09:12:18| Space-careers.com Jobs RSS
WHO ARE WE? GMV www.gmv.com is a technological and engineering company with an international presence employing more than 2300 staff. Founded in 1984, GMV works in many different sectors, like Space, Defense, Telecommunications, Security and Transportation. GMV provides systems and solutions, specialist hitech products and services. Our activities take in the whole life cycle, the design and development of software and hardware, integration of systems and subsystems, verification and testing, operational support and maintenance. We are one of the worlds top suppliers working for space organizations and agencies and also for the major satellite manufacturers and operators covering the whole range of activities and services within the space sector like flight segment, navigation, ground segment, data processing and operational support. GMV currently runs 8 work centers in Spain and offices in France, Germany, Malaysia, Poland, Portugal, Romania, USA, Colombia and United Kingdom. We recruit and hire excellent engineers, and encourage innovation, technical excellence and continuing education. Our engineers regularly present papers at technical conferences, continue their education and we reinvest more than 12 of budget in IRD projects. This striving for excellence, innovation and flexibility is part of our culture. JOB DESCRIPTION Responsibilities Duties Tasks If you want to know more about Big Data, artificial intelligence or machine learning and how they are changing the world, your place is here! We are an engineering and innovation company working in different areas. Within the IT sector we have reinvented the future of data, integrating the most advanced techniques in artificial intelligence, machine learning, cognitive services IBM Watson, AWS AI, Google AI, etc., and big data architectures to respond to the needs of a society that is evolving more and more rapidly. GMV is looking for a highly motivated MSc software engineer with specific background and interest in the application of Artificial Intelligence and Machine Learning techniques to space applications. You will be primarily enrolled to work on GMVs space debris and space flight dynamics activities in the frame of projects with ESA and UKSADstl making extensive use of AIML for diverse applications. Required knowledge If you want this position to be yours, we would like you to have the following knowledgeexperience Artificial Intelligence Machine Learning Relational databases SQL Programming SW Engineering Python Desirable Knowledge Project Management FortranJavaC Orbital Mechanics Space Debris Environment WHAT DO WE OFFER? The possibility to work in an international team, with innovative projects. We also give you the chance to develop your career abroad in our many other working centers. Professional Career Development. We help you directing your career, starting from a technical base, passing through the team and project management, the technical expertise or the commercial area, keeping contact with our clients. Its up to you! Access to the companys training program. You can acquire and sharpen your knowledge in the technologies we use, as well as your language skills. Social benefits. Flexible working schedule, and possibility to work from home. Possibility to participate in team activities and sports competitions with us. Our personnel policies guarantee equal treatment of all our staff and encourages diversity, from the jobselection process and then throughout their whole careers in the company. If you would like to work in an interesting, fun, fastpaced, challenging, diverse and global company which is growing rapidly, please send us your resume to httpsgmv.csod.comuxatscareersite4homerequisition112?cgmv For more information about GMV, please visit our website at www.gmv.com and our positions at httpsgmv.csod.comuxatscareersite4home?cgmv
Tags: learning
machine
engineer
machine learning
Machine learning: Accelerating your model deployment
2021-02-10 19:18:30| The Webmail Blog
Machine learning: Accelerating your model deployment nellmarie.colman Wed, 02/10/2021 - 12:18 Business models rely on data to drive decisions and make projections for future growth and performance. Traditionally, business analytics has been reactive guiding decisions in response to past performance. But todays leading companies are turning to machine learning (ML) and AI to harness their data for predictive analytics. This shift, however, comes with significant challenges. According to IDC, almost 30% of AI and ML initiatives fail. The primary culprits behind this failure are poor-quality data, low experience and challenging operationalization. They also require a lot of time to maintain, since you need to repeatedly train ML models with fresh data through the development cycle, due to data quality degradation over time. Lets explore the challenges presented when developing ML models and how the Rackspace Technology Model Factory Framework simplifies and accelerates the process so you can overcome these challenges. Machine learning challenges Among the most difficult aspects of machine learning is the process of operationalizing developed ML models that accurately and rapidly generate insights to serve your business needs. Youve probably experienced some of the most prominent hurdles, such as: Inefficient coordination in lifecycle management between operations teams and ML engineers. According to Gartner, 60% of models dont make it to production due to this disconnect. A high degree of model sprawl, which is a complex situation where multiple models are run simultaneously across different environments, with different datasets and hyperparameters. Keeping track of all these models and their associatives can be challenging. Models may be developed quickly, but the process of deployment can often take months limiting time to value. Organizations lack defined frameworks for data preparation, model training, deployment and monitoring, along with strong governance and security controls. The DevOps model for application development doesnt work with ML models. The standardized linear approach is made redundant by the need for retraining across a model lifecycle with fresh datasets, as data ages and becomes less usable. The ML model lifecycle is fairly complex, starting with data ingestion, transformation and validation so that it fits the needs of the initiative. A model is then developed and validated, followed by training. Depending on the length of development time, you may need to repeatedly perform training as a model moves across development, testing and deployment environments. After training, the model is then set into production, where it begins serving business objectives. Through this stage, the models performance is logged and monitored to ensure suitability. Rapidly Build Models with Amazon SageMaker Among the available tools to help you accelerate this process is Amazon SageMaker. This ML platform from Amazon Web Services (AWS) offers a more comprehensive set of capabilities towards rapidly developing, training and running your ML models in the cloud or at the edge. The Amazon SageMaker stack comes packaged with models for AI services such as computer vision, speech and recommendation engine capabilities, as well as models for ML services that help you deploy deep learning capabilities. It also supports leading ML frameworks, interfaces and infrastructure options. But employing the right toolsets is only half the story. Significant improvements in ML model deployment can only be achieved when you also consider improving the efficiency of lifecycle management across the teams that work on them. Different teams across organizations prefer different sets of tooling and frameworks, which can introduce lag through a model lifecycle. An open and modular solution agnostic of the platform, tooling or ML framework allows for easy tailoring and integration into proven AWS solutions. A solution such as this will allow your teams to use the tools they are comfortable with. Thats where the Rackspace Technology Model Factory Framework comes in, by providing a CI/CD pipeline for your models that makes them easier to deploy and track. Lets take a closer look at exactly how it improves efficiency and speed across model development, deployment, monitoring and governance, to accelerate getting ML models into production. End-to-end ML blueprint When in development, ML models flow from data science teams to operational teams. As previously noted, preferential variances across these teams can introduce a large amount of lag in the absence of standardization. The Rackspace Technology Model Factory Framework provides a model lifecycle management solution in the form of a modular architectural pattern, built using open source tools that are platform, tooling and framework agnostic. It is designed to improve the collaboration between your data scientists and operations teams so they can rapidly develop models, automate packaging and deploy to multiple environments. The framework allows integration with AWS services and industry-standard automation tools such as Jenkins, Airflow and Kubeflow. It supports a variety of frameworks such as TensorFlow, scikit-learn, Spark ML, spaCy, and PyTorch, and it can be deployed into different hosting platforms such as Kubernetes or Amazon SageMaker. Benefits of the Rackspace Technology model factory framework The Rackspace Technology Model Factory Framework affords large gains in efficiency, cutting the ML lifecycle from an average of 15 or more steps to as few as five. Employing a single source of truth for management, it also automates the handoff process across teams, simplifies maintenance, and troubleshooting. From the perspective of data scientists, the Model Factory Framework makes their code standardized and reproducible across environments, and it enables experiment and training tracking. It can also result in up to 60% of compute cost savings through scripted access to spot instance training. For operations teams, the framework offers built-in tools for diagnostics, performance monitoring and model drift mitigation. It also offers a model registry to track models versions over time. Overall, this helps your organization improve its model deployment time and reduce effort, accelerating time to business insights and ROI. Solution overview from development and deployment, to monitoring and governance The Model Factory Framework employs a curated list of Notebook templates and proprietary domain-specific languages, simplifying onboarding, reproduction across environments, tracking experiments, tuning hyperparameters and consistently packaging models and code agnostic to the domain. Once packaged, the framework can execute the end-to-end pipeline which will run the pre-processing, feature engineering and training jobs, log generated metrics and artifacts, and deploy the model across multiple environments. Development: The Model Factory Framework supports multiple avenues of development. Users can either develop locally, integrate with Notebooks Server using Integrated Development Environments (IDEs) or use SageMaker Notebooks. They may even utilize automated environment deployment using AWS tooling such as AWS CodeStar. Deployment: Multiple platform backends are supported for the same mode
Tags: model
learning
machine
deployment
Renesas updates R-Car V3H SoC with improved deep learning performance for smart camera applications
2021-02-10 10:55:40| Green Car Congress
Tags: performance
applications
camera
learning
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