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.
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
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.
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
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
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.
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
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
ML Workshop 2: Machine Learning Model Comparison & EvaluationMapR Technologies
How Rendezvous Architecture Improves Evaluation in the Real World
In this addition of our machine learning logistics webinar series we build on the ideas of the key requirements for effective management of machine learning logistics presented in the Overview webinar and in Part I Workshop. Here we focus on model-to-model comparison & evaluation, use of decoy models and more. Listen here: http://info.mapr.com/machine-learning-workshop2.html?_ga=2.35695522.324200644.1511891424-416597139.1465233415
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.
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.
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.
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?
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
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.
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
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.
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
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
ML Workshop 2: Machine Learning Model Comparison & EvaluationMapR Technologies
How Rendezvous Architecture Improves Evaluation in the Real World
In this addition of our machine learning logistics webinar series we build on the ideas of the key requirements for effective management of machine learning logistics presented in the Overview webinar and in Part I Workshop. Here we focus on model-to-model comparison & evaluation, use of decoy models and more. Listen here: http://info.mapr.com/machine-learning-workshop2.html?_ga=2.35695522.324200644.1511891424-416597139.1465233415
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.
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.
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.
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?
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
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.
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.
MapR is an ideal scalable platform for data science and specifically for operationalizing machine learning in the enterprise. This presentations gives specific reasons why.
Using Machine Learning to Understand and Predict Marketing ROIDATAVERSITY
Marketing is all about attracting, retaining and building profitable relationships with your customers, but how do you know which customers to target, which campaigns to run, and which marketing programs to invest in, to get most return for your dollar?
Join Alteryx and Keyrus as we demonstrate how to combine all relevant marketing, sales and customer data, and perform sophisticated analytics to deepen customer insight and calculate ROI of marketing programs.
You’ll walk away knowing how to:
Segment and profile your customers – take that raw data and translate it into real value
Build a marketing attribution model within Alteryx, creating a personal answer engine for your company.
Leverage R or Python code in an Alteryx workflow so data scientists can collaborate with non-coding stake holders in a code-friendly and code-free environment.
Join Alteryx and Keyrus and get the actionable insights you need to drive marketing ROI analytics, and answer million-dollar questions without spending millions of dollars on standardized solutions.
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.
Fighting financial fraud at Danske Bank with artificial intelligenceRon Bodkin
Danske Bank, the leader in mobile payments in Denmark, is innovating with AI. Danske Bank’s existing fraud detection engine is being enhanced with deep learning algorithms that can analyze potentially tens of thousands of latent features. Danske Bank’s current system is largely based on handcrafted rules created by the business, based on intuition and some light analysis. The system is effective at blocking fraud, but it has a high rate of false positives, which is expensive and inconvenient, and it has proved impractical to update and maintain as fraudsters evolve their capabilities. Moreover, the bank understands that fraud is getting worse in the near- and long-term future due to the increased digitization of banking and the prevalence of mobile banking applications and recognizes the need to use cutting-edge techniques to engage fraudsters not where they are today but where they will be tomorrow.
Application fraud is an important emerging trend, in which machines fill in transaction forms. There is evidence that criminals are employing sophisticated machine-learning techniques to attack, so it’s critical to use sophisticated machine learning to catch fraud in banking and mobile payment transactions.
Ron Bodkin and Nadeem Gulzar explore how Danske Bank uses deep learning for better fraud detection. Danske Bank’s multistep program first productionizes “classic” machine learning techniques (boosted decision trees) while in parallel developing deep learning models with TensorFlow as a “challenger” to test. The system was first tested in shadow production and then in full production in a champion-challenger setup against live transactions. Ron and Nadeem explain how the bank is integrating the models with the efforts already running, giving the bank and its investigation team the ability to adapt to new patterns faster than before and taking on complex highly varying functions not present in the training examples.
Azure Machine Learning is a cloud predictive analytics service that makes it possible to quickly create and deploy predictive models as analytics solutions. Getting started is easy. The first working prototype is an easy evening project. But Azure Machine Learning will grow to extremely complex projects. This session will demonstrate initial projects utilizing multiple data science principals.
Keynote presentation from ECBS conference. The talk is about how to use machine learning and AI in improving software engineering. Experiences from our project in Software Center (www.software-center.se).
How Data-Driven Approaches are Changing Your Data Management Strategies
Introducing data-driven strategies into your business model alters the way your organization manages and provides information to your customers, partners and employees. Gone are the days of “waterfall” implementation strategies from relational data to applications within a data center. Now, data-driven business models require agile implementation of applications based on information from all across an organization–on-premises, cloud, and mobile–and includes information from outside corporate walls from partners, third-party vendors, and customers. Data management strategies need to be ready to meet these challenges or your new and disruptive business models will fail at the most critical time: when your customers want to access it.
Deploying Enterprise Scale Deep Learning in Actuarial Modeling at NationwideDatabricks
The traditional approach to insurance pricing involves fitting a generalized linear model (GLM) to data collected on historical claims payments and premiums received. The explosive growth in data availability and increasing competitiveness in the marketplace are challenging actuaries to find new insights in their data and make predictions with more granularity, improved speed and efficiency, and with tighter integration among business units to support strategic decisions.
In this session we will share our experience implementing deep hierarchical neural networks using TensorFlow and PySpark on Databricks. We will discuss the benefits of the ML Runtime, our experience using the goofys mount, our process for hyperparameter tuning, specific considerations for the large dataset size and extreme volatility present in insurance data, among other topics.
Authors: Bryn Clark, Krish Rajaram
Fighting Financial Crime with Artificial IntelligenceDataWorks Summit
How can we take the state of the art in deep learning and AI research, and transplant it into a large bank to deliver useful results which impact the general public? To answer this broad-reaching question, we take the viewer through a solution Think Big Analytics recently deployed at a major European bank for fraud detection, using state of the art AI techniques and a near-real time open-source architecture. We show how financial transactions can be transposed into a form where the latest AI techniques in image recognition can be leveraged, in surprisingly novel ways. We have been able to more accurately detect fraud and reduce financial crime, cutting losses and improving customer experience. We describe some architectures which can be used to do this in production, at scale, in global financial institutions.
Speaker:
Tim Seears, Director of Data Science, Think Big Analytics, a Teradata Company
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
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.
AI Pilot Review: The World’s First Virtual Assistant Marketing SuiteGoogle
AI Pilot Review: The World’s First Virtual Assistant Marketing Suite
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-pilot-review/
AI Pilot Review: Key Features
✅Deploy AI expert bots in Any Niche With Just A Click
✅With one keyword, generate complete funnels, websites, landing pages, and more.
✅More than 85 AI features are included in the AI pilot.
✅No setup or configuration; use your voice (like Siri) to do whatever you want.
✅You Can Use AI Pilot To Create your version of AI Pilot And Charge People For It…
✅ZERO Manual Work With AI Pilot. Never write, Design, Or Code Again.
✅ZERO Limits On Features Or Usages
✅Use Our AI-powered Traffic To Get Hundreds Of Customers
✅No Complicated Setup: Get Up And Running In 2 Minutes
✅99.99% Up-Time Guaranteed
✅30 Days Money-Back Guarantee
✅ZERO Upfront Cost
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
We describe the deployment and use of Globus Compute for remote computation. This content is aimed at researchers who wish to compute on remote resources using a unified programming interface, as well as system administrators who will deploy and operate Globus Compute services on their research computing infrastructure.
Navigating the Metaverse: A Journey into Virtual Evolution"Donna Lenk
Join us for an exploration of the Metaverse's evolution, where innovation meets imagination. Discover new dimensions of virtual events, engage with thought-provoking discussions, and witness the transformative power of digital realms."
Launch Your Streaming Platforms in MinutesRoshan Dwivedi
The claim of launching a streaming platform in minutes might be a bit of an exaggeration, but there are services that can significantly streamline the process. Here's a breakdown:
Pros of Speedy Streaming Platform Launch Services:
No coding required: These services often use drag-and-drop interfaces or pre-built templates, eliminating the need for programming knowledge.
Faster setup: Compared to building from scratch, these platforms can get you up and running much quicker.
All-in-one solutions: Many services offer features like content management systems (CMS), video players, and monetization tools, reducing the need for multiple integrations.
Things to Consider:
Limited customization: These platforms may offer less flexibility in design and functionality compared to custom-built solutions.
Scalability: As your audience grows, you might need to upgrade to a more robust platform or encounter limitations with the "quick launch" option.
Features: Carefully evaluate which features are included and if they meet your specific needs (e.g., live streaming, subscription options).
Examples of Services for Launching Streaming Platforms:
Muvi [muvi com]
Uscreen [usencreen tv]
Alternatives to Consider:
Existing Streaming platforms: Platforms like YouTube or Twitch might be suitable for basic streaming needs, though monetization options might be limited.
Custom Development: While more time-consuming, custom development offers the most control and flexibility for your platform.
Overall, launching a streaming platform in minutes might not be entirely realistic, but these services can significantly speed up the process compared to building from scratch. Carefully consider your needs and budget when choosing the best option for you.
Gamify Your Mind; The Secret Sauce to Delivering Success, Continuously Improv...Shahin Sheidaei
Games are powerful teaching tools, fostering hands-on engagement and fun. But they require careful consideration to succeed. Join me to explore factors in running and selecting games, ensuring they serve as effective teaching tools. Learn to maintain focus on learning objectives while playing, and how to measure the ROI of gaming in education. Discover strategies for pitching gaming to leadership. This session offers insights, tips, and examples for coaches, team leads, and enterprise leaders seeking to teach from simple to complex concepts.
Utilocate offers a comprehensive solution for locate ticket management by automating and streamlining the entire process. By integrating with Geospatial Information Systems (GIS), it provides accurate mapping and visualization of utility locations, enhancing decision-making and reducing the risk of errors. The system's advanced data analytics tools help identify trends, predict potential issues, and optimize resource allocation, making the locate ticket management process smarter and more efficient. Additionally, automated ticket management ensures consistency and reduces human error, while real-time notifications keep all relevant personnel informed and ready to respond promptly.
The system's ability to streamline workflows and automate ticket routing significantly reduces the time taken to process each ticket, making the process faster and more efficient. Mobile access allows field technicians to update ticket information on the go, ensuring that the latest information is always available and accelerating the locate process. Overall, Utilocate not only enhances the efficiency and accuracy of locate ticket management but also improves safety by minimizing the risk of utility damage through precise and timely locates.
Software Engineering, Software Consulting, Tech Lead, Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Transaction, Spring MVC, OpenShift Cloud Platform, Kafka, REST, SOAP, LLD & HLD.
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.
Code reviews are vital for ensuring good code quality. They serve as one of our last lines of defense against bugs and subpar code reaching production.
Yet, they often turn into annoying tasks riddled with frustration, hostility, unclear feedback and lack of standards. How can we improve this crucial process?
In this session we will cover:
- The Art of Effective Code Reviews
- Streamlining the Review Process
- Elevating Reviews with Automated Tools
By the end of this presentation, you'll have the knowledge on how to organize and improve your code review proces
Custom Healthcare Software for Managing Chronic Conditions and Remote Patient...Mind IT Systems
Healthcare providers often struggle with the complexities of chronic conditions and remote patient monitoring, as each patient requires personalized care and ongoing monitoring. Off-the-shelf solutions may not meet these diverse needs, leading to inefficiencies and gaps in care. It’s here, custom healthcare software offers a tailored solution, ensuring improved care and effectiveness.
Listen to the keynote address and hear about the latest developments from Rachana Ananthakrishnan and Ian Foster who review the updates to the Globus Platform and Service, and the relevance of Globus to the scientific community as an automation platform to accelerate scientific discovery.
Cyaniclab : Software Development Agency Portfolio.pdfCyanic lab
CyanicLab, an offshore custom software development company based in Sweden,India, Finland, is your go-to partner for startup development and innovative web design solutions. Our expert team specializes in crafting cutting-edge software tailored to meet the unique needs of startups and established enterprises alike. From conceptualization to execution, we offer comprehensive services including web and mobile app development, UI/UX design, and ongoing software maintenance. Ready to elevate your business? Contact CyanicLab today and let us propel your vision to success with our top-notch IT solutions.
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.
Developing Distributed High-performance Computing Capabilities of an Open Sci...Globus
COVID-19 had an unprecedented impact on scientific collaboration. The pandemic and its broad response from the scientific community has forged new relationships among public health practitioners, mathematical modelers, and scientific computing specialists, while revealing critical gaps in exploiting advanced computing systems to support urgent decision making. Informed by our team’s work in applying high-performance computing in support of public health decision makers during the COVID-19 pandemic, we present how Globus technologies are enabling the development of an open science platform for robust epidemic analysis, with the goal of collaborative, secure, distributed, on-demand, and fast time-to-solution analyses to support public health.
Top 7 Unique WhatsApp API Benefits | Saudi ArabiaYara Milbes
Discover the transformative power of the WhatsApp API in our latest SlideShare presentation, "Top 7 Unique WhatsApp API Benefits." In today's fast-paced digital era, effective communication is crucial for both personal and professional success. Whether you're a small business looking to enhance customer interactions or an individual seeking seamless communication with loved ones, the WhatsApp API offers robust capabilities that can significantly elevate your experience.
In this presentation, we delve into the top 7 distinctive benefits of the WhatsApp API, provided by the leading WhatsApp API service provider in Saudi Arabia. Learn how to streamline customer support, automate notifications, leverage rich media messaging, run scalable marketing campaigns, integrate secure payments, synchronize with CRM systems, and ensure enhanced security and privacy.
GraphSummit Paris - The art of the possible with Graph TechnologyNeo4j
Sudhir Hasbe, Chief Product Officer, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
In software engineering, the right architecture is essential for robust, scalable platforms. Wix has undergone a pivotal shift from event sourcing to a CRUD-based model for its microservices. This talk will chart the course of this pivotal journey.
Event sourcing, which records state changes as immutable events, provided robust auditing and "time travel" debugging for Wix Stores' microservices. Despite its benefits, the complexity it introduced in state management slowed development. Wix responded by adopting a simpler, unified CRUD model. This talk will explore the challenges of event sourcing and the advantages of Wix's new "CRUD on steroids" approach, which streamlines API integration and domain event management while preserving data integrity and system resilience.
Participants will gain valuable insights into Wix's strategies for ensuring atomicity in database updates and event production, as well as caching, materialization, and performance optimization techniques within a distributed system.
Join us to discover how Wix has mastered the art of balancing simplicity and extensibility, and learn how the re-adoption of the modest CRUD has turbocharged their development velocity, resilience, and scalability in a high-growth environment.