Applying Machine learning to IOT: End to End Distributed Distributed Pipeline...Carol McDonald
This discusses the architecture of an end-to-end application that combines streaming data with machine learning to do real-time analysis and visualization of where and when Uber cars are clustered, so as to analyze and visualize the most popular Uber locations.
Churn prediction is big business. It minimizes customer defection by predicting which customers are likely to cancel a service. Though originally used within the telecommunications industry, it has become common practice for banks, ISPs, insurance firms, and other verticals. More: http://info.mapr.com/WB_PredictingChurn_Global_DG_17.06.15_RegistrationPage.html
The prediction process is data-driven and often uses advanced machine learning techniques. In this webinar, we'll look at customer data, do some preliminary analysis, and generate churn prediction models – all with Spark machine learning (ML) and a Zeppelin notebook.
Spark’s ML library goal is to make machine learning scalable and easy. Zeppelin with Spark provides a web-based notebook that enables interactive machine learning and visualization.
In this tutorial, we'll do the following:
Review classification and decision trees
Use Spark DataFrames with Spark ML pipelines
Predict customer churn with Apache Spark ML decision trees
Use Zeppelin to run Spark commands and visualize the results
Demystifying AI, Machine Learning and Deep LearningCarol McDonald
Deep learning, machine learning, artificial intelligence - all buzzwords and representative of the future of analytics. In this talk we will explain what is machine learning and deep learning at a high level with some real world examples. The goal of this is not to turn you into a data scientist, but to give you a better understanding of what you can do with machine learning. Machine learning is becoming more accessible to developers, and Data scientists work with domain experts, architects, developers and data engineers, so it is important for everyone to have a better understanding of the possibilities. Every piece of information that your business generates has potential to add value. This and future posts are meant to provoke a review of your own data to identify new opportunities.
Predicting Flight Delays with Spark Machine LearningCarol McDonald
Apache Spark's MLlib makes machine learning scalable and easier with ML pipelines built on top of DataFrames. In this webinar, we will go over an example from the ebook Getting Started with Apache Spark 2.x.: predicting flight delays using Apache Spark machine learning.
Applying Machine learning to IOT: End to End Distributed Distributed Pipeline...Carol McDonald
This discusses the architecture of an end-to-end application that combines streaming data with machine learning to do real-time analysis and visualization of where and when Uber cars are clustered, so as to analyze and visualize the most popular Uber locations.
Churn prediction is big business. It minimizes customer defection by predicting which customers are likely to cancel a service. Though originally used within the telecommunications industry, it has become common practice for banks, ISPs, insurance firms, and other verticals. More: http://info.mapr.com/WB_PredictingChurn_Global_DG_17.06.15_RegistrationPage.html
The prediction process is data-driven and often uses advanced machine learning techniques. In this webinar, we'll look at customer data, do some preliminary analysis, and generate churn prediction models – all with Spark machine learning (ML) and a Zeppelin notebook.
Spark’s ML library goal is to make machine learning scalable and easy. Zeppelin with Spark provides a web-based notebook that enables interactive machine learning and visualization.
In this tutorial, we'll do the following:
Review classification and decision trees
Use Spark DataFrames with Spark ML pipelines
Predict customer churn with Apache Spark ML decision trees
Use Zeppelin to run Spark commands and visualize the results
Demystifying AI, Machine Learning and Deep LearningCarol McDonald
Deep learning, machine learning, artificial intelligence - all buzzwords and representative of the future of analytics. In this talk we will explain what is machine learning and deep learning at a high level with some real world examples. The goal of this is not to turn you into a data scientist, but to give you a better understanding of what you can do with machine learning. Machine learning is becoming more accessible to developers, and Data scientists work with domain experts, architects, developers and data engineers, so it is important for everyone to have a better understanding of the possibilities. Every piece of information that your business generates has potential to add value. This and future posts are meant to provoke a review of your own data to identify new opportunities.
Predicting Flight Delays with Spark Machine LearningCarol McDonald
Apache Spark's MLlib makes machine learning scalable and easier with ML pipelines built on top of DataFrames. In this webinar, we will go over an example from the ebook Getting Started with Apache Spark 2.x.: predicting flight delays using Apache Spark machine learning.
How Big Data is Reducing Costs and Improving Outcomes in Health CareCarol McDonald
There is no better example of the important role that data plays in our lives than in matters of our health and our healthcare. There’s a growing wealth of health-related data out there, and it’s playing an increasing role in improving patient care, population health, and healthcare economics.
Join this talk to hear how MapR customers are using big data and advanced analytics to address a myriad of healthcare challenges—from patient to payer.
We will cover big data healthcare trends and production use cases that demonstrate how to deliver data-driven healthcare applications
Analyzing Flight Delays with Apache Spark, DataFrames, GraphFrames, and MapR-DBCarol McDonald
Apache Spark GraphX made it possible to run graph algorithms within Spark, GraphFrames integrates GraphX and DataFrames and makes it possible to perform Graph pattern queries without moving data to a specialized graph database.
This presentation will help you get started using Apache Spark GraphFrames Graph Algorithms and Graph Queries with MapR-DB JSON document database.
Machine Learning for Chickens, Autonomous Driving and a 3-year-old Who Won’t ...MapR Technologies
Big data technologies are being applied to a wide variety of use cases. We will review tangible examples of machine learning, discuss an autonomous driving project and illustrate the role of MapR in next generation initiatives. More: http://info.mapr.com/WB_Machine-Learning-for-Chickens_Global_DG_17.11.02_RegistrationPage.html
State of the Art Robot Predictive Maintenance with Real-time Sensor DataMathieu Dumoulin
Our Strata Beijing 2017 presentation slides where we show how to use data from a movement sensor, in real-time, to do anomaly detection at scale using standard enterprise big data software.
How to create an enterprise data lake for enterprise-wide information storage and sharing? The data lake concept, architecture principles, support for data science and some use case review.
Learn about what technologies enable a new, modern Stream-based architecture to connect everything within application modules or across data centers and public clouds. Combine Kafka-style streaming and stream processing frameworks like Spark and Flink with Microservices and completely rethink your big data architecture away from state and into data flows.
We’re in the midst of an exciting paradigm shift in terms of how we process events data in real time to better react to business opportunities or risk. To stay ahead of your competition, you need the ability to react to business-critical events as they happen. These critical events are created through diverse sources such as social interaction, machine sensors, or a customer transaction. How can you understand the meaning and context of these events that ultimately define your business?
MapR is an ideal scalable platform for data science and specifically for operationalizing machine learning in the enterprise. This presentations gives specific reasons why.
Live Tutorial – Streaming Real-Time Events Using Apache APIsMapR Technologies
For this talk we will explore the power of streaming real time events in the context of the IoT and smart cities.
http://info.mapr.com/WB_Streaming-Real-Time-Events_Global_DG_17.08.02_RegistrationPage.html
Spark and MapR Streams: A Motivating ExampleIan Downard
Businesses are discovering the untapped potential of large datasets and data streams through the use of technologies for big data processing and storage. By leveraging these assets they’re creating a new generation of applications that derive value from data they used to throw away. In this presentation Ian Downard shows how to build operational environments for these types of applications with the MapR Converged Data Platform and he describes examples of a next-generation applications that use Java APIs for MapR Streams, Apache Spark, Apache Hive, and MapR-DB. He shows how these technologies can be used to join and transform unbounded datasets to find signals and derive new data streams for a financial scenario involving real-time algorithmic trading and historical analysis using SQL. He also discusses how MapR enables you to run real-time data applications with the speed, reliability, and security you need for a production environment.
How Big Data is Reducing Costs and Improving Outcomes in Health CareCarol McDonald
There is no better example of the important role that data plays in our lives than in matters of our health and our healthcare. There’s a growing wealth of health-related data out there, and it’s playing an increasing role in improving patient care, population health, and healthcare economics.
Join this talk to hear how MapR customers are using big data and advanced analytics to address a myriad of healthcare challenges—from patient to payer.
We will cover big data healthcare trends and production use cases that demonstrate how to deliver data-driven healthcare applications
Analyzing Flight Delays with Apache Spark, DataFrames, GraphFrames, and MapR-DBCarol McDonald
Apache Spark GraphX made it possible to run graph algorithms within Spark, GraphFrames integrates GraphX and DataFrames and makes it possible to perform Graph pattern queries without moving data to a specialized graph database.
This presentation will help you get started using Apache Spark GraphFrames Graph Algorithms and Graph Queries with MapR-DB JSON document database.
Machine Learning for Chickens, Autonomous Driving and a 3-year-old Who Won’t ...MapR Technologies
Big data technologies are being applied to a wide variety of use cases. We will review tangible examples of machine learning, discuss an autonomous driving project and illustrate the role of MapR in next generation initiatives. More: http://info.mapr.com/WB_Machine-Learning-for-Chickens_Global_DG_17.11.02_RegistrationPage.html
State of the Art Robot Predictive Maintenance with Real-time Sensor DataMathieu Dumoulin
Our Strata Beijing 2017 presentation slides where we show how to use data from a movement sensor, in real-time, to do anomaly detection at scale using standard enterprise big data software.
How to create an enterprise data lake for enterprise-wide information storage and sharing? The data lake concept, architecture principles, support for data science and some use case review.
Learn about what technologies enable a new, modern Stream-based architecture to connect everything within application modules or across data centers and public clouds. Combine Kafka-style streaming and stream processing frameworks like Spark and Flink with Microservices and completely rethink your big data architecture away from state and into data flows.
We’re in the midst of an exciting paradigm shift in terms of how we process events data in real time to better react to business opportunities or risk. To stay ahead of your competition, you need the ability to react to business-critical events as they happen. These critical events are created through diverse sources such as social interaction, machine sensors, or a customer transaction. How can you understand the meaning and context of these events that ultimately define your business?
MapR is an ideal scalable platform for data science and specifically for operationalizing machine learning in the enterprise. This presentations gives specific reasons why.
Live Tutorial – Streaming Real-Time Events Using Apache APIsMapR Technologies
For this talk we will explore the power of streaming real time events in the context of the IoT and smart cities.
http://info.mapr.com/WB_Streaming-Real-Time-Events_Global_DG_17.08.02_RegistrationPage.html
Spark and MapR Streams: A Motivating ExampleIan Downard
Businesses are discovering the untapped potential of large datasets and data streams through the use of technologies for big data processing and storage. By leveraging these assets they’re creating a new generation of applications that derive value from data they used to throw away. In this presentation Ian Downard shows how to build operational environments for these types of applications with the MapR Converged Data Platform and he describes examples of a next-generation applications that use Java APIs for MapR Streams, Apache Spark, Apache Hive, and MapR-DB. He shows how these technologies can be used to join and transform unbounded datasets to find signals and derive new data streams for a financial scenario involving real-time algorithmic trading and historical analysis using SQL. He also discusses how MapR enables you to run real-time data applications with the speed, reliability, and security you need for a production environment.
Machine Learning Success: The Key to Easier Model ManagementMapR Technologies
Join Ellen Friedman, co-author (with Ted Dunning) of a new short O’Reilly book Machine Learning Logistics: Model Management in the Real World, to look at what you can do to have effective model management, including the role of stream-first architecture, containers, a microservices approach and a DataOps style of work. Ellen will provide a basic explanation of a new architecture that not only leverages stream transport but also makes use of canary models and decoy models for accurate model evaluation and for efficient and rapid deployment of new models in production.
Self-Service Data Science for Leveraging ML & AI on All of Your DataMapR Technologies
MapR has launched the MapR Data Science Refinery which leverages a scalable data science notebook with native platform access, superior out-of-the-box security, and access to global event streaming and a multi-model NoSQL database.
Converged and Containerized Distributed Deep Learning With TensorFlow and Kub...Mathieu Dumoulin
Docker containers running on Kubernetes combine with MapR Converged Data Platform allow any company to potentially enjoy the same sophisticated data infrastructure for enabling teams to engage in transformative machine learning and deep learning for production use at scale.
Predictive Maintenance Using Recurrent Neural NetworksJustin Brandenburg
My presentation from AnacondaCON 2018 where I discussed using Recurrent Neural Networks, Python, Tensorflow and the MapR Platform to develop deploy a predictive maintenance model for an IoT device in the manufacturing industry.
The logistics of machine learning typically take waaay more effort than the machine learning itself. Moreover, machine learning systems aren't like normal software projects so continuous integration takes on new meaning.
The Hive Think Tank: Rendezvous Architecture Makes Machine Learning Logistics...The Hive
Think Tank Event 10/23/2017, hosted by The Hive and presented by Ted Dunning, Chief Application Architect of MapR Technologies and Ellen Friedman of MapR Technologies.
An Introduction to the MapR Converged Data PlatformMapR Technologies
Listen to the webinar on-demand: http://info.mapr.com/WB_Partner_CDP_Intro_EMEA_DG_17.05.31_RegistrationPage.html
In this 90-minute webinar, we discuss:
- The MapR Converged Data Platform and its components
- Use cases for the Converged Data Platform
- MapR Converged Partner Program
- How to get started with MapR
- Becoming a partner
Fast Cars, Big Data - How Streaming Can Help Formula 1 - Tugdual Grall - Code...Codemotion
Modern race cars produce lot of data, and all this in real time. In this presentation I will show you how data could be generated and used by various applications in the car, on the track or team head quarter. The demonstration will show how to move data using messaging systems like Apache Kafka, process the data using Apache Spark and use various storage technics: Distributed File System, NoSQL Database. This presentation is a great opportunity to see how to build a " near real time big data application". The code from this talk will be made available as open source.
3 Benefits of Multi-Temperature Data Management for Data AnalyticsMapR Technologies
SAP® HANA and SAP® IQ are popular platforms for various analytical and transactional use cases. If you’re an SAP customer, you’ve experienced the benefits of deploying these solutions. However, as data volumes grow, you’re likely asking yourself: How do I scale storage to support these applications? How can I have one platform for various applications and use cases?
How Spark is Enabling the New Wave of Converged ApplicationsMapR Technologies
Apache Spark has become the de-facto compute engine of choice for data engineers, developers, and data scientists because of its ability to run multiple analytic workloads with a single compute engine. Spark is speeding up data pipeline development, enabling richer predictive analytics, and bringing a new class of applications to market.
Changes in how business is done combined with multiple technology drivers make geo-distributed data increasingly important for enterprises. These changes are causing serious disruption across a wide range of industries, including healthcare, manufacturing, automotive, telecommunications, and entertainment. Technical challenges arise with these disruptions, but the good news is there are now innovative solutions to address these problems. http://info.mapr.com/WB_Geo-distributed-Big-Data-and-Analytics_Global_DG_17.05.16_RegistrationPage.html
Fast Cars, Big Data - How Streaming Can Help Formula 1 - Tugdual Grall - Code...Codemotion
Modern race cars produce lot of data, and all this in real time. In this presentation I will show you how data could be generated and used by various applications in the car, on the track or team head quarter. The demonstration will show how to move data using messaging systems like Apache Kafka, process the data using Apache Spark and use various storage technics: Distributed File System, NoSQL Database. This presentation is a great opportunity to see how to build a " near real time big data application". The code from this talk will be made available as open source.
Industrial IoT is currently transforming how businesses capitalize their big data. Changes in how business is done, combined with multiple technology drivers make geo-distributed data increasingly important for enterprises. These changes are causing serious disruption across a wide range of industries.
You know that a single number isn't a good summary of a measurement. T-digest and other non-uniform histograms can make it easy to keep track of an entire distribution and can be combined in OLAP queries.
The latest t-digest is faster, more accurate and has hard bounds on size.
Similar to Streaming Machine learning Distributed Pipeline for Real-Time Uber Data Using Apache APIs: Kafka, Spark, HBase (20)
Streaming Patterns Revolutionary Architectures with the Kafka APICarol McDonald
Building a robust, responsive, secure data service for healthcare is tricky. For starters, healthcare data lends itself to multiple models:
• Document representation for patient profile view or update
• Graph representation to query relationships between patients, providers, and medications
• Search representation for advanced lookups
Keeping these different systems up to date requires an architecture that can synchronize them in real time as data is updated. Furthermore, meeting audit requirements in Healthcare requires the ability to apply granular cross-datacenter replication policies to data and be able to provide detailed lineage information for each record. This post will describe how stream-first architectures can solve these challenges, and look at how this has been implemented at a Health Information Network provider.
This talk will go over the Kafka API with these design patterns:
• Turning the database upside down
• Event Sourcing , Command Query Responsibity Separation , Polyglot Persistence
• Kappa Architecture
Unleash Unlimited Potential with One-Time Purchase
BoxLang is more than just a language; it's a community. By choosing a Visionary License, you're not just investing in your success, you're actively contributing to the ongoing development and support of BoxLang.
Check out the webinar slides to learn more about how XfilesPro transforms Salesforce document management by leveraging its world-class applications. For more details, please connect with sales@xfilespro.com
If you want to watch the on-demand webinar, please click here: https://www.xfilespro.com/webinars/salesforce-document-management-2-0-smarter-faster-better/
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI AppGoogle
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI App
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-fusion-buddy-review
AI Fusion Buddy Review: Key Features
✅Create Stunning AI App Suite Fully Powered By Google's Latest AI technology, Gemini
✅Use Gemini to Build high-converting Converting Sales Video Scripts, ad copies, Trending Articles, blogs, etc.100% unique!
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✅Automatically create & sell AI content, graphics, websites, landing pages, & all that gets you paid non-stop 24*7.
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See My Other Reviews Article:
(1) AI Genie Review: https://sumonreview.com/ai-genie-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
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Enhancing Research Orchestration Capabilities at ORNL.pdfGlobus
Cross-facility research orchestration comes with ever-changing constraints regarding the availability and suitability of various compute and data resources. In short, a flexible data and processing fabric is needed to enable the dynamic redirection of data and compute tasks throughout the lifecycle of an experiment. In this talk, we illustrate how we easily leveraged Globus services to instrument the ACE research testbed at the Oak Ridge Leadership Computing Facility with flexible data and task orchestration capabilities.
Top Features to Include in Your Winzo Clone App for Business Growth (4).pptxrickgrimesss22
Discover the essential features to incorporate in your Winzo clone app to boost business growth, enhance user engagement, and drive revenue. Learn how to create a compelling gaming experience that stands out in the competitive market.
First Steps with Globus Compute Multi-User EndpointsGlobus
In this presentation we will share our experiences around getting started with the Globus Compute multi-user endpoint. Working with the Pharmacology group at the University of Auckland, we have previously written an application using Globus Compute that can offload computationally expensive steps in the researcher's workflows, which they wish to manage from their familiar Windows environments, onto the NeSI (New Zealand eScience Infrastructure) cluster. Some of the challenges we have encountered were that each researcher had to set up and manage their own single-user globus compute endpoint and that the workloads had varying resource requirements (CPUs, memory and wall time) between different runs. We hope that the multi-user endpoint will help to address these challenges and share an update on our progress here.
AI Genie Review: World’s First Open AI WordPress Website CreatorGoogle
AI Genie Review: World’s First Open AI WordPress Website Creator
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-genie-review
AI Genie Review: Key Features
✅Creates Limitless Real-Time Unique Content, auto-publishing Posts, Pages & Images directly from Chat GPT & Open AI on WordPress in any Niche
✅First & Only Google Bard Approved Software That Publishes 100% Original, SEO Friendly Content using Open AI
✅Publish Automated Posts and Pages using AI Genie directly on Your website
✅50 DFY Websites Included Without Adding Any Images, Content Or Doing Anything Yourself
✅Integrated Chat GPT Bot gives Instant Answers on Your Website to Visitors
✅Just Enter the title, and your Content for Pages and Posts will be ready on your website
✅Automatically insert visually appealing images into posts based on keywords and titles.
✅Choose the temperature of the content and control its randomness.
✅Control the length of the content to be generated.
✅Never Worry About Paying Huge Money Monthly To Top Content Creation Platforms
✅100% Easy-to-Use, Newbie-Friendly Technology
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See My Other Reviews Article:
(1) TubeTrivia AI Review: https://sumonreview.com/tubetrivia-ai-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
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Globus Connect Server Deep Dive - GlobusWorld 2024Globus
We explore the Globus Connect Server (GCS) architecture and experiment with advanced configuration options and use cases. This content is targeted at system administrators who are familiar with GCS and currently operate—or are planning to operate—broader deployments at their institution.
Graspan: A Big Data System for Big Code AnalysisAftab Hussain
We built a disk-based parallel graph system, Graspan, that uses a novel edge-pair centric computation model to compute dynamic transitive closures on very large program graphs.
We implement context-sensitive pointer/alias and dataflow analyses on Graspan. An evaluation of these analyses on large codebases such as Linux shows that their Graspan implementations scale to millions of lines of code and are much simpler than their original implementations.
These analyses were used to augment the existing checkers; these augmented checkers found 132 new NULL pointer bugs and 1308 unnecessary NULL tests in Linux 4.4.0-rc5, PostgreSQL 8.3.9, and Apache httpd 2.2.18.
- Accepted in ASPLOS ‘17, Xi’an, China.
- Featured in the tutorial, Systemized Program Analyses: A Big Data Perspective on Static Analysis Scalability, ASPLOS ‘17.
- Invited for presentation at SoCal PLS ‘16.
- Invited for poster presentation at PLDI SRC ‘16.
A Study of Variable-Role-based Feature Enrichment in Neural Models of CodeAftab Hussain
Understanding variable roles in code has been found to be helpful by students
in learning programming -- could variable roles help deep neural models in
performing coding tasks? We do an exploratory study.
- These are slides of the talk given at InteNSE'23: The 1st International Workshop on Interpretability and Robustness in Neural Software Engineering, co-located with the 45th International Conference on Software Engineering, ICSE 2023, Melbourne Australia
Providing Globus Services to Users of JASMIN for Environmental Data AnalysisGlobus
JASMIN is the UK’s high-performance data analysis platform for environmental science, operated by STFC on behalf of the UK Natural Environment Research Council (NERC). In addition to its role in hosting the CEDA Archive (NERC’s long-term repository for climate, atmospheric science & Earth observation data in the UK), JASMIN provides a collaborative platform to a community of around 2,000 scientists in the UK and beyond, providing nearly 400 environmental science projects with working space, compute resources and tools to facilitate their work. High-performance data transfer into and out of JASMIN has always been a key feature, with many scientists bringing model outputs from supercomputers elsewhere in the UK, to analyse against observational or other model data in the CEDA Archive. A growing number of JASMIN users are now realising the benefits of using the Globus service to provide reliable and efficient data movement and other tasks in this and other contexts. Further use cases involve long-distance (intercontinental) transfers to and from JASMIN, and collecting results from a mobile atmospheric radar system, pushing data to JASMIN via a lightweight Globus deployment. We provide details of how Globus fits into our current infrastructure, our experience of the recent migration to GCSv5.4, and of our interest in developing use of the wider ecosystem of Globus services for the benefit of our user community.
In the ever-evolving landscape of technology, enterprise software development is undergoing a significant transformation. Traditional coding methods are being challenged by innovative no-code solutions, which promise to streamline and democratize the software development process.
This shift is particularly impactful for enterprises, which require robust, scalable, and efficient software to manage their operations. In this article, we will explore the various facets of enterprise software development with no-code solutions, examining their benefits, challenges, and the future potential they hold.
Understanding Nidhi Software Pricing: A Quick Guide 🌟
Choosing the right software is vital for Nidhi companies to streamline operations. Our latest presentation covers Nidhi software pricing, key factors, costs, and negotiation tips.
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Key factors influencing Nidhi software price
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Affordable and customizable pricing options with Vector Nidhi Software
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Zoom is a comprehensive platform designed to connect individuals and teams efficiently. With its user-friendly interface and powerful features, Zoom has become a go-to solution for virtual communication and collaboration. It offers a range of tools, including virtual meetings, team chat, VoIP phone systems, online whiteboards, and AI companions, to streamline workflows and enhance productivity.