Mahad Khan completed all lectures in an IBM developerWorks course on IBM Bluemix essentials on August 3rd, 2016 at 10:21am CEST. The course was hosted on IBM developerWorks and covered essential topics related to IBM Bluemix.
Mahad Khan completed all lectures in an IBM developerWorks cloud application developer certification preparation course on August 24, 2016 at 11:39 CEST. The digitally signed document from IBM developerWorks confirms Khan finished the course lectures located on the IBM developerWorks website.
The document is a digital signature from Ushasree Kondapi confirming completion of all lectures in an IBM developerWorks blockchain course on February 9, 2017 at 18:34:19 CET from IBM developerWorks.
Aarti Vashisht completed all lectures in an IBM developerWorks course on IBM Bluemix essentials on January 9, 2017 at 2:27pm CET. The course covered the essentials of IBM Bluemix and upon completion, Vashisht digitally signed the document.
The document discusses applying blockchain technology to a business network. It was digitally signed by Kanthi L on November 23, 2016 at 07:46:51 CET after completing all lectures in an IBM developerWorks course. The signature includes a reason and location.
Had a great time discussing Azure ML with the Denver R User Group meeting this evening. We covered ways to use R in Azure ML to clean, transform and process data and to build machine learning models. This is a great meetup if you have a chance to attend a future meeting!
What's New to the SDK's and JavaScript API - Smart Development - Esri UK Annu...Esri UK
Last year, we saw exciting new releases of both our web and native development tools. We can't wait to show you some of our favourite new features, so come along to this session and tell you all about version 100 of our Runtime SDKs and version 4 of our JavaScript API.
Serverless + Machine Learning – Bringing the best of two worlds togetherVidyasagar Machupalli
This document discusses combining containers and machine learning by demonstrating a serverless platform that executes code in response to events. It shows how to write code in the language of your choice and expose actions as API endpoints using Cloud Functions. The demonstration uses a dataset to trigger a sequence of inputs that are processed by Cloud Functions and stored in a NoSQL database.
What is Machine Learning and how does it work? But even more importantly, what problems can ML solve for you and your company?
Once you have understood the potential use cases, we will briefly describe the main challenges in the world of Big Data.
Why is deploying ML models so hard and how can Cloud Computing help?
Many MLaaS options are available on the market (AWS, Google, Azure, BigML, etc.). We will see how they compare to each other and which may best fit your needs.
Whenever MLaaS is not enough, you can build your own ML models. We will briefly explain why Serverless is a great deployment strategy for this use case and what problems and limitation arise with it.
Furthermore, we will put these ideas into practice and build a model for Sentiment Analysis, based on Python (scikit-learn), and trained with a public dataset by Stanford University.
Mahad Khan completed all lectures in an IBM developerWorks cloud application developer certification preparation course on August 24, 2016 at 11:39 CEST. The digitally signed document from IBM developerWorks confirms Khan finished the course lectures located on the IBM developerWorks website.
The document is a digital signature from Ushasree Kondapi confirming completion of all lectures in an IBM developerWorks blockchain course on February 9, 2017 at 18:34:19 CET from IBM developerWorks.
Aarti Vashisht completed all lectures in an IBM developerWorks course on IBM Bluemix essentials on January 9, 2017 at 2:27pm CET. The course covered the essentials of IBM Bluemix and upon completion, Vashisht digitally signed the document.
The document discusses applying blockchain technology to a business network. It was digitally signed by Kanthi L on November 23, 2016 at 07:46:51 CET after completing all lectures in an IBM developerWorks course. The signature includes a reason and location.
Had a great time discussing Azure ML with the Denver R User Group meeting this evening. We covered ways to use R in Azure ML to clean, transform and process data and to build machine learning models. This is a great meetup if you have a chance to attend a future meeting!
What's New to the SDK's and JavaScript API - Smart Development - Esri UK Annu...Esri UK
Last year, we saw exciting new releases of both our web and native development tools. We can't wait to show you some of our favourite new features, so come along to this session and tell you all about version 100 of our Runtime SDKs and version 4 of our JavaScript API.
Serverless + Machine Learning – Bringing the best of two worlds togetherVidyasagar Machupalli
This document discusses combining containers and machine learning by demonstrating a serverless platform that executes code in response to events. It shows how to write code in the language of your choice and expose actions as API endpoints using Cloud Functions. The demonstration uses a dataset to trigger a sequence of inputs that are processed by Cloud Functions and stored in a NoSQL database.
What is Machine Learning and how does it work? But even more importantly, what problems can ML solve for you and your company?
Once you have understood the potential use cases, we will briefly describe the main challenges in the world of Big Data.
Why is deploying ML models so hard and how can Cloud Computing help?
Many MLaaS options are available on the market (AWS, Google, Azure, BigML, etc.). We will see how they compare to each other and which may best fit your needs.
Whenever MLaaS is not enough, you can build your own ML models. We will briefly explain why Serverless is a great deployment strategy for this use case and what problems and limitation arise with it.
Furthermore, we will put these ideas into practice and build a model for Sentiment Analysis, based on Python (scikit-learn), and trained with a public dataset by Stanford University.
Building Serverless Machine Learning Models in the Cloud [PyData DC]Alex Casalboni
You will learn how to efficiently design and train machine learning models in Python and deploy them to the cloud. This process reduces the development & operational efforts required to make your prototypes production-ready.
We will describe the main challenges faced by data scientists involved in deploying machine learning models into real production environments with specific references, examples of Python libraries, and multi-model systems requiring advanced features such as A/B testing and high scalability & availability.
While discussing the advantages and limitations of multiple deployment strategies in the cloud, we will focus on serverless computing (i.e. AWS Lambda) as a solution for simplifying your development & deployment workflows.
mabl's Machine Learning Implementation on Google Cloud PlatformJoseph Lust
mabl software engineer Joe Lust presents the mabl cloud architecture at the Cambridge Cloud Exchange: Machine Learning meetup at Google Cambridge. The talk takes an in-depth look at mabl’s machine learning and specifically how mabl uses numerous Google Cloud systems for its intelligent auto-healing tests and visual change detection.
This document outlines the architecture of a scalable e-learning platform that allows teachers to set up classrooms of 40 students in 1 hour. It compares the past/legacy stack of MySQL, Ruby, incremental APIs and homemade synchronization to the future lean stack of MongoDB, Node.js, REST APIs, universal authentication and analytics, and local CDNs. Key aspects of the platform include scalability, a universal authentication/analytics system, and deploying the first version for Android by March and making it production ready by May.
This document discusses using C# and Xamarin to build cross-platform mobile applications for iOS, Android, and Windows. Some key points covered include:
- C# can be used to write fully native mobile apps for these platforms
- Code sharing is enabled through .NET and C# libraries that can be reused across platforms
- The Xamarin toolset includes Visual Studio and Xamarin Studio IDEs for building mobile apps
- Best practices are presented for architectural considerations like code sharing strategies, platform-specific UX design, and testing on real devices.
This document summarizes a presentation on ASP.NET vNext by Sam Basu and Shayne Boyer. It introduces the presenters and discusses how ASP.NET vNext has been redesigned from the ground up as a lean .NET stack optimized for modern web apps and cloud deployment. Key aspects of ASP.NET vNext covered include using modular components, being open source, merging MVC and Web API, new routing and tag helpers, and how it supports both .NET Core and cross-platform.
Introduction to ArcGIS Developer Tools - Smart Development - Esri UK Annual C...Esri UK
It's been an exciting year for ArcGIS developers with some great new capabilities available to us! This introduction session is all about giving you the knowledge you need to get the best out of our developer tools. We'll take you through all our APIs and SDKs and discuss how you can use them to write powerful GIS apps.
How to deploy machine learning models in the CloudAlex Casalboni
Developing and experimenting with machine learning models in Python is easy and well supported by robust and agile libraries such as scikit-learn, although efficiently deploying multi-model systems at scale is still a challenge in the data science field.
This talk will focus on the main issues related to deploying machine learning models and how to make scikit-learn production-ready with minimal operational efforts, by means of Cloud Computing services, in particular Amazon Web Services.
Prerequisites: basic Machine Learning understanding (modeling and training), minimal knowledge about scikit-learn and Python utilities such as Pandas and boto.
Hybride Cloud Infrastrukturen ermöglichen es Unternehmen flexibel und schnell auf Veränderungen der Märkte zu regieren.
Die Präsentation zeigt die Sichtweise der IBM auf dieses Thema und gib Beispiele von Unternehmen die hybride Cloud Lösungen einsetzen um Innovationen schnell umzusetzen, bessere Entscheidungen zu treffen oder neue Geschäftsmodelle zu entwickeln.
Openstack days sv building highly available services using kubernetes (preso)Allan Naim
This document discusses Google Cloud Platform's Kubernetes and how it can be used to build highly available services. It provides an overview of Kubernetes concepts like pods, labels, replica sets, volumes, and services. It then describes how Kubernetes Cluster Federation allows deploying applications across multiple Kubernetes clusters for high availability, geographic scaling, and other benefits. It outlines how to create clusters, configure the federated control plane, add clusters to the federation, deploy federated services and backends, and perform cross-cluster service discovery.
IBM Bluemix and Docker Guest Lecture at Cork Institute of TechnologySanjay Nayak
This document discusses IBM Bluemix and Docker. It provides an overview of Bluemix, describing it as an open-standard cloud platform for building, managing and running applications. It notes that developers use Bluemix to rapidly deploy applications, continuously deliver new functionality, and integrate with on-premise systems. The document also introduces Docker, describing it as a tool for packaging applications into lightweight Linux containers that can run anywhere. It provides examples of building Docker images and deploying containerized applications on Bluemix.
Out of the Blue: Getting started with IBM Bluemix developmentOliver Busse
The document discusses two workflows for developing applications using IBM Bluemix:
1. Start developing the application directly in Bluemix, then continue working on it locally by cloning the code repository.
2. Start developing the application locally, then deploy it to Bluemix either using the Eclipse plugin or the Cloud Foundry command line interface.
Both workflows utilize tools like Git, Eclipse, and the Cloud Foundry CLI to develop, build, and deploy applications to Bluemix. The document provides step-by-step instructions for sample applications using each approach.
Docker allows applications to be packaged into standardized units called containers that can run on any infrastructure. IBM Bluemix supports Docker containers and provides services for building, managing, and hosting containerized applications in a hybrid cloud environment. Key benefits of Docker containers include increased portability and efficiency in development and deployment across physical and cloud infrastructure.
Präsentation auf den IBM Developer Days 2014 in Wien zum Thema Industrie 4.0.
Praktische Use-Cases und Integrationsbeispiele zwischen LineMetrics und IBM Technologien.
This document discusses Docker Containers as a Service (CaaS). It begins by showing how Docker aims to build tools for mass innovation by creating a software layer to program the internet across various hardware and applications. It then discusses how CaaS provides an IT-managed secure environment for developers to build and deploy applications. Finally, it outlines the key characteristics of Docker's CaaS platform which supports the full application lifecycle across languages, operating systems and infrastructures through open APIs and ecosystem support.
The document discusses BlueMix, IBM's platform as a service (PaaS) offering. It contrasts a PaaS model where developers focus on writing application code and the platform handles services, with an infrastructure as a service (IaaS) model where developers also manage virtualization, hardware, and operating systems. It encourages requesting access to BlueMix to take advantage of its PaaS capabilities and avoid managing lower level infrastructure.
IBM BlueMix Architecture and Deep Dive (Powered by CloudFoundry) Animesh Singh
meetup.com/Bluemix
meetup.com/CloudFoundry/
In this meetup, we discussed the architecture and demonstrated IBM BlueMix, public Platform-as-a-Service offering based on Cloud Foundry
Docker Datacenter Overview and Production Setup SlidesDocker, Inc.
An overview on Docker Data Center and Universal Control Plane. We will cover how to install for production and integrate Docker Trusted Registry.
Led by DDC + UCP Champ:
Vivek Saraswat
Experience Level: Attendees need no prior experience with Docker, but should be familiar with basic linux command-line.
Seit einiger Zeit sind Docker Container komplett in IBM Bluemix verfügbar. Zusammen schauen wir uns diese Art des Applikationen-Deployments im Detail an. Was ist Docker, was macht es so stark und wie komplettiert IBM den Service für Enterprise-Kunden?
Building Serverless Machine Learning Models in the Cloud [PyData DC]Alex Casalboni
You will learn how to efficiently design and train machine learning models in Python and deploy them to the cloud. This process reduces the development & operational efforts required to make your prototypes production-ready.
We will describe the main challenges faced by data scientists involved in deploying machine learning models into real production environments with specific references, examples of Python libraries, and multi-model systems requiring advanced features such as A/B testing and high scalability & availability.
While discussing the advantages and limitations of multiple deployment strategies in the cloud, we will focus on serverless computing (i.e. AWS Lambda) as a solution for simplifying your development & deployment workflows.
mabl's Machine Learning Implementation on Google Cloud PlatformJoseph Lust
mabl software engineer Joe Lust presents the mabl cloud architecture at the Cambridge Cloud Exchange: Machine Learning meetup at Google Cambridge. The talk takes an in-depth look at mabl’s machine learning and specifically how mabl uses numerous Google Cloud systems for its intelligent auto-healing tests and visual change detection.
This document outlines the architecture of a scalable e-learning platform that allows teachers to set up classrooms of 40 students in 1 hour. It compares the past/legacy stack of MySQL, Ruby, incremental APIs and homemade synchronization to the future lean stack of MongoDB, Node.js, REST APIs, universal authentication and analytics, and local CDNs. Key aspects of the platform include scalability, a universal authentication/analytics system, and deploying the first version for Android by March and making it production ready by May.
This document discusses using C# and Xamarin to build cross-platform mobile applications for iOS, Android, and Windows. Some key points covered include:
- C# can be used to write fully native mobile apps for these platforms
- Code sharing is enabled through .NET and C# libraries that can be reused across platforms
- The Xamarin toolset includes Visual Studio and Xamarin Studio IDEs for building mobile apps
- Best practices are presented for architectural considerations like code sharing strategies, platform-specific UX design, and testing on real devices.
This document summarizes a presentation on ASP.NET vNext by Sam Basu and Shayne Boyer. It introduces the presenters and discusses how ASP.NET vNext has been redesigned from the ground up as a lean .NET stack optimized for modern web apps and cloud deployment. Key aspects of ASP.NET vNext covered include using modular components, being open source, merging MVC and Web API, new routing and tag helpers, and how it supports both .NET Core and cross-platform.
Introduction to ArcGIS Developer Tools - Smart Development - Esri UK Annual C...Esri UK
It's been an exciting year for ArcGIS developers with some great new capabilities available to us! This introduction session is all about giving you the knowledge you need to get the best out of our developer tools. We'll take you through all our APIs and SDKs and discuss how you can use them to write powerful GIS apps.
How to deploy machine learning models in the CloudAlex Casalboni
Developing and experimenting with machine learning models in Python is easy and well supported by robust and agile libraries such as scikit-learn, although efficiently deploying multi-model systems at scale is still a challenge in the data science field.
This talk will focus on the main issues related to deploying machine learning models and how to make scikit-learn production-ready with minimal operational efforts, by means of Cloud Computing services, in particular Amazon Web Services.
Prerequisites: basic Machine Learning understanding (modeling and training), minimal knowledge about scikit-learn and Python utilities such as Pandas and boto.
Hybride Cloud Infrastrukturen ermöglichen es Unternehmen flexibel und schnell auf Veränderungen der Märkte zu regieren.
Die Präsentation zeigt die Sichtweise der IBM auf dieses Thema und gib Beispiele von Unternehmen die hybride Cloud Lösungen einsetzen um Innovationen schnell umzusetzen, bessere Entscheidungen zu treffen oder neue Geschäftsmodelle zu entwickeln.
Openstack days sv building highly available services using kubernetes (preso)Allan Naim
This document discusses Google Cloud Platform's Kubernetes and how it can be used to build highly available services. It provides an overview of Kubernetes concepts like pods, labels, replica sets, volumes, and services. It then describes how Kubernetes Cluster Federation allows deploying applications across multiple Kubernetes clusters for high availability, geographic scaling, and other benefits. It outlines how to create clusters, configure the federated control plane, add clusters to the federation, deploy federated services and backends, and perform cross-cluster service discovery.
IBM Bluemix and Docker Guest Lecture at Cork Institute of TechnologySanjay Nayak
This document discusses IBM Bluemix and Docker. It provides an overview of Bluemix, describing it as an open-standard cloud platform for building, managing and running applications. It notes that developers use Bluemix to rapidly deploy applications, continuously deliver new functionality, and integrate with on-premise systems. The document also introduces Docker, describing it as a tool for packaging applications into lightweight Linux containers that can run anywhere. It provides examples of building Docker images and deploying containerized applications on Bluemix.
Out of the Blue: Getting started with IBM Bluemix developmentOliver Busse
The document discusses two workflows for developing applications using IBM Bluemix:
1. Start developing the application directly in Bluemix, then continue working on it locally by cloning the code repository.
2. Start developing the application locally, then deploy it to Bluemix either using the Eclipse plugin or the Cloud Foundry command line interface.
Both workflows utilize tools like Git, Eclipse, and the Cloud Foundry CLI to develop, build, and deploy applications to Bluemix. The document provides step-by-step instructions for sample applications using each approach.
Docker allows applications to be packaged into standardized units called containers that can run on any infrastructure. IBM Bluemix supports Docker containers and provides services for building, managing, and hosting containerized applications in a hybrid cloud environment. Key benefits of Docker containers include increased portability and efficiency in development and deployment across physical and cloud infrastructure.
Präsentation auf den IBM Developer Days 2014 in Wien zum Thema Industrie 4.0.
Praktische Use-Cases und Integrationsbeispiele zwischen LineMetrics und IBM Technologien.
This document discusses Docker Containers as a Service (CaaS). It begins by showing how Docker aims to build tools for mass innovation by creating a software layer to program the internet across various hardware and applications. It then discusses how CaaS provides an IT-managed secure environment for developers to build and deploy applications. Finally, it outlines the key characteristics of Docker's CaaS platform which supports the full application lifecycle across languages, operating systems and infrastructures through open APIs and ecosystem support.
The document discusses BlueMix, IBM's platform as a service (PaaS) offering. It contrasts a PaaS model where developers focus on writing application code and the platform handles services, with an infrastructure as a service (IaaS) model where developers also manage virtualization, hardware, and operating systems. It encourages requesting access to BlueMix to take advantage of its PaaS capabilities and avoid managing lower level infrastructure.
IBM BlueMix Architecture and Deep Dive (Powered by CloudFoundry) Animesh Singh
meetup.com/Bluemix
meetup.com/CloudFoundry/
In this meetup, we discussed the architecture and demonstrated IBM BlueMix, public Platform-as-a-Service offering based on Cloud Foundry
Docker Datacenter Overview and Production Setup SlidesDocker, Inc.
An overview on Docker Data Center and Universal Control Plane. We will cover how to install for production and integrate Docker Trusted Registry.
Led by DDC + UCP Champ:
Vivek Saraswat
Experience Level: Attendees need no prior experience with Docker, but should be familiar with basic linux command-line.
Seit einiger Zeit sind Docker Container komplett in IBM Bluemix verfügbar. Zusammen schauen wir uns diese Art des Applikationen-Deployments im Detail an. Was ist Docker, was macht es so stark und wie komplettiert IBM den Service für Enterprise-Kunden?
1. Mahad Khan
IBM Bluemix essentials
03 August 2016
Digitally signed by
IBM developerWorks
Date: 2016.08.03
10:21:08 CEST
Reason: Completed
all lectures in IBM
developerWorks
course
Location: IBM
developerWorks
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