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2020 Energy Conference Networks Machine Learning in Oil and Gas
2021-02-27 09:12:08| Oil IT Journal - www.oilit.com
Quantum Reservoir, 'oilfield data is convoluted'. Shell Tech Ventures' cash for innovators. WalMart's NexTech unit minimizes vendor dependence. Riverford on Bureau of Economic Geology's TORA, 'big data for small companies'. Warwick, Neo4J Graph Technology for leasehold analysis. LANL's 'fat neurons', physics-informed neural nets. Texas A&M, drones, AI and oil spills.
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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
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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
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AI and machine learning are revolutionizing modern businesses heres how to get ahead
2021-02-04 22:08:23| The Webmail Blog
AI and machine learning are revolutionizing modern businesses heres how to get ahead nellmarie.colman Thu, 02/04/2021 - 15:08 Fierce competition means every business must adapt to succeed. AI and machine learning have emerged as modern, vital ways for organizations to get ahead. Many businesses today prioritize data, analytics and AI/machine learning projects to power new business models, enhance product and service offerings, improve efficiency, drive revenue and deliver superior customer experiences. But analyst figures on project implementation make for sobering reading. Gartner predicts that under half of modern data analytics and machine learning projects will be successfully deployed in production by 2022. Less than a fifth will move piloted AI projects into production without delays caused by a range of problems from technical skills gaps and lack of IT/business process maturity, to insufficient organizational collaboration. For example, these businesses may not have expertise in mathematics, algorithm design or data science and engineering. Or their data may not be in a unified data lake infrastructure for ready access. These conditions create challenges for any organization looking to advance in the market and derive value from AI and machine learning. This combination of pressure and challenges can overwhelm your business, especially if youre at the start of your AI and machine learning journey. So lets dig into why your business should make the effort and how doing so might require different skills sets and data from what you might think. What are AI, machine learning and deep learning? Lets start with the basics. When a machine completes tasks based on a set of stipulated rules that solve problems, were into the realm of artificial intelligence. This might include understanding and interpreting natural language, recognizing when objects move and providing intelligent answers. Business benefits follow, such as analyzing data sets that are too large for humans to process, answering questions in real time that draw from existing data and experiences, and automation that can reduce costs and boost productivity. Machine learning is a discipline within the AI domain. It enables machines to learn by themselves using data. They use this knowledge to make increasingly accurate predictions and drive actions. For this to happen, you need a model thats trained on existing data, after which point it can process additional data and make predictions. Throughout the process, its important to track and understand your model, building quality and eliminating bias. Finally, deep learning is a subfield of machine learning. It structures algorithms in layers to create an artificial neural network that can learn and make intelligent decisions on its own. The many use cases of AI and machine learning Weve so far explored AI, machine learning and deep learning in the abstract, but in what specific ways can they benefit your business? Answering questions, thereby improving customer support and buying journeys, through faster, higher-quality answers and experiences. Speech recognition, including text-to-speech and speech-to-text translation, enabling you to work with voice/audio data more widely and productively. Document summaries that effectively extract key concepts to use in countless ways, improving productivity and document data use. Image recognition for biotech, satellite/drone imagery and face recognition, to quicken emergency responses and prevent crime. Image processing to improve the presentation and utilization of images through enhanced resolution and colorization. Data classification in medicine, to yield better diagnoses, faster and more targeted treatments and health preservation. Superior search to get customers to what they want more rapidly, whether thats a product recommendation or a web page. Strategic analysis that can be a boon to the games industry, driving more challenging and educational entertainment. Financial and logistical forecasting to improve financial management, planning and resource allocation/utilization. AI and machine learning require skills and data, but not what you think If youre looking to machine learning and deep learning but have concerns about your existing data, be mindful that they dont always need massive data sets. While completely new models with no data nor training do require tens of thousands to millions of data points, trained models exist that can give a project leader a head start. Even if you have just 100 or so examples for a specific use case, building on a general models foundation could yield more accurate results than human experts would provide. Additionally, its worth thinking differently about hiring for the delivery of AI/machine learning enabled applications and solutions. Theres an assumption you need PhD-level data scientists. Although they do add value and can be necessary in some circumstances, existing staff can often be trained in about 100 hours, building on high-school math and a year of coding experience. With modern tools on AWS or Google Cloud including AutoML, they can build the solutions you need. In all, its as much about changing your mindset as anything else. You must think about what AI and machine learning can bring to your business and the most effective way to achieve that, thereby keeping your company ahead. Machine learning is today driving change in thinking of data as code where machine learning uses data to write the program, which is the output. This methodology coupled with the tools and education I mentioned earlier set the stage for many more people collaborating to fashion a new generation of intelligent solutions that will revolutionize business for years to come. For more information on AI and machine learning, check out our panel discussion, which dives deep into these topics. The discussion covers: toolsets and methodologies; capabilities and constraints; data, computer and expertise requirements; examples of successful applications; and how to get started. AI and machine learning are revolutionizing modern businesses heres how to get aheadDiscover why AI and machine learning are worth the effort, and why they require different skills sets and data from what you might think.Discover how businesses are using AI and machine learning.https://www.brighttalk.com/webcast/17680/420320Watch the presentation
AWS re:Invent 2020 Recap: Machine Learning Keynote
2020-12-10 19:13:50| The Webmail Blog
AWS re:Invent 2020 Recap: Machine Learning Keynote nellmarie.colman Thu, 12/10/2020 - 12:13 While this years AWS re:Invent may be entirely virtual, AWS has not disappointed this Data Scientist in the slightest. The long list of new releases in AWS machine learning stack will undoubtedly benefit all users ranging from novices to experts in the field. During Tuesdays Machine Learning Keynote, Swami Sivasubramanian, VP of Amazon Machine Learning, structured his message around three tenets which, together, give builders the freedom to invent. Under each tenet, he announced the new releases for machine learning, while also explaining how they fit together with the events other announcements helping the audience weave together the bigger picture. Provide firm foundations The first tenet, Provide firm foundations, was the basis for the first announcement: faster distributed training with Amazon SageMaker. Using Habana Gaudi processors from Intel, AWS will soon offer EC2 instances built for machine learning (ML) training yielding a performance increase of up to 40% over current GPU-based EC2 ML training instances for training deep learning workloads. This helps provide a firm foundation upon which developers can build and deploy machine learning, faster, while also reducing costs. Create the shortest path to success The second tenet, Create the shortest path to success, got the audience (particularly football fans like me) excited, as Sivasubramanian shared how AWS and the NFL are collaborating to achieve game and player simulation that can predict, treat and ultimately prevent player injury. In the spirit of creating the shortest path to success for critical projects like this, AWS announced Amazon SageMaker Data Wrangler. This is a tool that Im particularly eager to experiment with, as AWS suggests its a huge time saver for data transformation and discovery. Im also pleased to see that Data Wrangler will soon integrate with Snowflake, MongoDB and Databricks historically, AWS required AWS databases to seamlessly leverage their tools. Another time saver is Amazon SageMaker Clarify, Amazons bias detection tool across the entire ML workflow. Not only does bias detection save time, if done well it improves overall model quality, flagging any drift that may occur as models age. The next SageMaker release that I plan to utilize as an education tool is model profiling for Amazon SageMaker Debugger. This capability maximizes resources for training, GPU, CPU, network and I/O memory, by analyzing resource utilization and then making recommendations on how to adjust. (Wow!) We also learned about Amazon SageMaker Edge Manager, a new feature that manages and monitors machine learning models across fleets of smart devices up to 25 times faster when compared to hand-tuning said models. Expand machine learning to more builders The third tenet, Expand machine learning to more builders, is a principle Im particularly passionate about. AWS has attempted to achieve this by releasing Amazon Redshift ML which Im enthusiastic to test out. Its crucial to be able to more-easily experiment and deploy machine learning models however, Im wary of the suggestion that certain experts are no longer required in the process. Selecting, refining and deeply understanding a model is fundamental to extracting the most value possible from the output. As a launch partner for Redshift ML, Rackspace Technology can help you make the most of this new feature: At Rackspace Technology we help companies elevate their AI/ML operations. Were excited about the new Amazon Redshift ML feature because it will make it easier for our mutual Redshift customers to use ML on their Redshift with a familiar SQL interface. The seamless integration with Amazon SageMaker will empower data analysts to use data in new ways, and provide even more insight back to the wider organization. Nihar Gupta General Manager for Data Solutions, Rackspace Technology AWS continues to pave the way for streamlined development and deployment. Im thrilled to see the increasing number of capabilities across platforms. As a Data Science consultant, I am constantly interacting with different frameworks and infrastructure. AWS is my go-to solution for model development and as systems are increasingly compatible, the more I can focus on model refinement. This years re:Invent is a three-week event, all virtual, and free. To watch the event live or view recordings, register here and dont forget to visit the Rackspace Technology virtual booth to learn more about our new AWS solutions and enjoy an immersive interactive experience. AWS re:Invent 2020 Recap: Machine Learning KeynoteGet up-to-speed on the latest AWS machine learning announcements from AWS re:Invent 2020. Explore re:Invent announcements and updates.https://events.rackspace.com/reinventFind out more
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