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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
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UKs first 5G immersive classroom brings richer learning experience to pupils
2021-03-10 01:00:00| Total Telecom industry news
With schools reopening across the UK this month, pupils in North Lanarkshire can now experience what it’s like to be in outer space, under the ocean, on a World War 1 battlefield or even on top of Everest - thanks to a new initiative which is the first of its kind in the UK. The new immersive classroom has been developed within the Muirfield Centre in Cumbernauld, North Lanarkshire, where a room has been transformed – using innovative technology - into an engaging and digital learning environment. …read more on TotalTele.com »
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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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versus
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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energy
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conference
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