Categories:

• Agriculture and Forestry
• Automotive
• Biotechnology and Pharmaceuticals
• Chemicals
• Construction and Maintenance
• Consumer Goods and Services
• Electronics and Electrical
• Energy and Environment
• Food and Related Products
• Industrial Goods and Services
• Information Technology
• Materials
• Real Estate
• Telecommunications
• Textiles and Nonwovens
• Transportation and Logistics

Popular Tags:

• player
• mp4
• mp4 player
• screen
• player screen
• screen mp4
• 4gb
• player 4gb
• 4gb mp4
• card
• slot
• player card
• 18inch
• inch
• 15inch
• inch mp4
• inch player
• flash
• style
• builtin

Home › machine

 

Tag: machine

New machine learning method accurately predicts battery state of health

2021-04-12 11:55:38| Green Car Congress

Tags: state health method learning

FTS International Introduces Machine Health Automation Technology

2021-04-05 14:58:04| OGI

FTS International Inc. hosted a leading global automobile manufacturer on April 1 at its National Operations Center (NOC) in Fort Worth, which serves as the hub for FTSIs automation platform, to showcase its data and analytics capabilities.

Tags: international health technology machine

Four steps to AI and machine learning success

2021-03-16 16:16:15| The Webmail Blog

Four steps to AI and machine learning success nellmarie.colman Tue, 03/16/2021 - 10:16   AI and machine learning (AI/ML) are hot topics, as businesses bring together cloud-based compute, memory and networking with an explosion of new data. This powerful combination is helping businesses deliver superior customer-centric experiences, understand their business environment like never before and drive new levels of efficiency. But achieving these AI/ML-driven successes is tough. In a recent Rackspace Technology-sponsored study, only 17% of respondents report mature AI/ML capabilities. The majority of respondents (82%) are still exploring or are struggling to operationalize AI/ML models.   Why AI and machine learning efforts fail According to the study, businesses are struggling with their AI/ML efforts for several reasons: Failure to get the right data to the right app or point-of-analysis in real time Your machine learning training is only as good as the data you feed into your AI/ML frameworks and intelligent applications. If the data is bad, old or incomplete, the training will be poor and the answers and results generated will be (at best) equal to the quality of the data and perhaps flat out wrong.   Lack of organizational collaboration Designing the right machine learning training and AI algorithms requires a holistic understanding of the data and processes that youre automating, across organizational boundaries. This requires communication and buy-in across the business. Lack of collaboration often yields a poor implementation, lower-quality data and rejection of the applications/automation projects by key parts of the organization.   IT and business process immaturity If your IT and business processes are not well formed, then its likely your data is not complete, and the AI/ML execution will be sub-par. Also, AI/ML is best served with rapid iterations and improvements in the data and algorithms something that happens most effectively in a DevOps culture.   Lack of expertise in mathematics, algorithm design or data science and engineering Since AI and machine learning are built on high-quality, timely data and well-formed algorithmsrepresenting the best in processes and models of the real world skills are critical. And finding the talent is tough in todays market.   But with the right AI/ML strategy, you can overcome these challenges. Lets dive deeper into how you can make this happen.   Four steps to AI and machine learning success Step 1: Build the foundation You must start by preparing your data and applications to migrate to the right multicloud and data architecture environments. This includes getting to know and understand your current environment and requirements and defining a roadmap. Be sure that the data architecture supports the new application deployments appropriately, and that you can minimize ingress/egress fees while also maximizing performance and availability. This is also the stage when database transformations and data warehouse migrations are implemented.   Step 2: Modernize the data architecture Defining the modern data architecture, strategy and roadmap drives the transition into this phase. Here, youll focus on modernizing your data architecture defining, designing and building the data fabric. This includes pipelines and integration, data lakes and warehouses, and the analytics platform. You can start on this while youre working on Step 1, or at least execute the migration with an eye toward data architecture modernization.   Step 3: Set the stage for more innovation AI/ML prepares your organization for high-quality automation and predictive intelligence driving innovation to the next level. At this stage, youll be planning the data science by designing, training and deploying the models, and operationalizing machine learning (MLOps). This enables you to deliver greater value to the modern cloud and data architecture you built in Steps 1 and 2.   Step 4: Build intelligent applications Finally, youre ready to start delivering strategic value and capability, where you can realize the full value of this new cloud-based data fabric youve created. You can employ intelligent applications that incorporate chatbot services, natural language processing, machine vision, recommendation engines, predictive maintenance and even actions and get value from IoT data. Its all possible now and forms a new foundation for your business.   Expert guidance for your AI and machine learning journey When your data works harder for you, you can take your resources further, delivering intelligent applications, services and results. This, in turn, enables you to make smarter decisions, improve collaboration, deliver new revenue streams and business models, and transform customer experiences. Do you need help getting the right data to the right application at the right business moment, while delivering a new level of business insights? Our experts are here to help. Let our specialists help you harness the power of modern data architecture and AI.   Four steps to AI and machine learning successOrganizations are using AI and machine learning to make smarter decisions, improve collaboration, deliver new revenue streams and transform customer experiences. But the journey to success is tough. Heres how to set yourself up for success. How are businesses investing in AI and machine learning?/solve/succeeding-ai-mlRead the report

Tags: learning success machine steps

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 machine factory

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

Sites : [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] next »