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Jun 28, 2016 IBM App Connect Enterprise with MQ client running on IBM Cloud Private calling to an external MQ service Installing fixpacks or i-fixes to IIB/ACE containers Installing IBM MQ for use with IBM App Connect Enterprise. Feb 25, 2020 IBM i Access Client Solutions provides a Java based, platform-independent interface that runs on most operating systems that support Java, including Linux, Mac, and Windows™. IBM i Access Client Solutions consolidates the most commonly used tasks for managing your IBM i into one simplified location. Open the IBM app store. Navigate to and download the IBM Verse app. Install and accept any prompts. On the 'I want to connect to.' Screen, click 'IBM Connections Cloud' 5. Log in using your IBM email and password Setting Up an iOS Device Note: IBM Mobile apps support iOS devices with an OS version iOS 9 or higher. To check the.

Support for connecting to IBM MQ in the cloud and IBM MQ on-premises (V8.0 or later) is now available with IBM App Connect. MQ is a messaging-oriented middleware product that enables you to deploy queue managers and connect your applications to them, for reliable data transfer between different parts of your enterprise application landscape.

You can connect to an MQ queue manager to achieve the following tasks:

  • Get messages from a queue.
  • Put messages to a queue.
  • Subscribe to messages on a topic.
  • Publish messages to a topic.

Requirements for connecting to MQ from App Connect

For

To see which events and actions are available for MQ and use them in your flows, you must connect App Connect to your MQ queue manager by setting up an account with the following connection details:

  • The queue manager host and port
  • The queue manager name
  • Your application MQ user name and password for an MQ on-premises server, or your user name and API key for an MQ cloud deployment
  • The server connection channel name
  • (Optional) The cipher spec
  • (Optional) The network name
    Note:
    • The cipher spec is applicable only for cloud connectivity, and is required if you have enabled SSL on the channel for connectivity. The default value is ECDHE_RSA_AES_128_CBC_SHA256. Currently, App Connect supports only server-side authenticated connections for the channel. Leave this field blank if you do not have SSL enabled.
    • The network name is required for connecting to an MQ on-premises deployment. You can configure a private network by using the IBM Secure Gateway Client.

If using an MQ on-premises deployment, you must also add the following line to the queue manager configuration file named qm.ini:

This file is generally located in var/mqm/qmgrs/QMNAME/qm.ini on UNIX and C:ProgramDataIBMMQqmgrsQMNAMEqm.ini on Windows, where QMNAME is the queue manager name.

Scenario

You can integrate MQ with any of the applications or APIs in the App Connect catalog, as required for your use case. To illustrate how easy it can be to set up a flow, we are going to describe a simple scenario with two MQ queues: DEV.QUEUE.1 and DEV.QUEUE.2. We will listen on DEV.QUEUE.1 for messages, send the retrieved messages to an HTTP endpoint for processing, and put the response to DEV.QUEUE.2.

We start our event-driven flow by adding an IBM MQ “New message on a queue” event as the trigger for the flow. In the Queue name field, we add the name of our first queue DEV.QUEUE.1.

Then, we add an HTTP node with a POST method and add the endpoint URL to the URL (fully qualified) field. The “New message on a queue” node will output MQMD headers and message data. Let’s use the Request body field of our HTTP node to map to the Message data response, which contains the message payload.

Next, we’re going to put the response received from the HTTP node to the queue named DEV.QUEUE.2. To do this, click (+) > IBM MQ > Put message to a queue. In the Queue name field, we add the name of our second queue DEV.QUEUE.2. In the Payload type field, we select Text because we are going to input a message in text rather than in binary code. In the Message data field, map to the Response body response from the HTTP node.

Now let’s use the MQMD header section in the IBM MQ node to add MQMD headers. We click Add property to provide the headers that we want to work with. (These header names must be valid because the flow will fail if an invalid header is provided.) In this flow, we add the following headers: “Correlation identifier” (represented by the CorrelId property) and “Name of the ReplyToQ” (represented by the ReplyToQ property).

We can then click Edit mappings to provide values for the headers that we just added, and can enter either text or mapped values. In this flow, the value for “CorrelId” is mapped to Message ID from the “IBM MQ / New message on a queue” response, and the value of “ReplyToQ” is provided as a text value.

We can now start the flow and test it as follows:

  1. Put a message to “DEV.QUEUE.1” on our queue manager.
  2. Check if the response message is available in “DEV.QUEUE.2”.
  3. Finally, return to the App Connect dashboard and locate the tile for the flow. Notice that the flow tile shows a green tick to indicate that the flow ran successfully.

IBM Watson Services for Core ML

With Watson Services for Core ML, it’s easy to build apps that access powerful Watson capabilities right from iPhone and iPad, so you can provide dynamic, intelligent insights that improve over time. And with the IBM Cloud Developer Console for Apple, you can quickly tap into Watson Services for Core ML and other services on IBM Cloud.

Build iOS apps that keep getting smarter.

You can build apps that leverage Watson models on iPhone and iPad, even when offline. Your apps can quickly analyze images, accurately classify visual content, and easily train models using Watson Services. Get started with pre-trained Watson models, or customize and train models that continuously learn over time.

Tag and classify images.
Watson Visual Recognition lets you quickly and accurately understand visual content. Visual Recognition comes with pre-trained models that let your app analyze images for scenes, objects, faces, colors, food, and various other types of content.

Customize models.
Different uses call for different models. You can create and train custom image classifiers using your own image collections to suit your business needs.

Train models with continuous learning.
Start building models with a single line of code. Easily create, evaluate, and manage custom models that continuously learn and improve over time.

Watson Services
Train, Test,
and Deploy

Core ML
Identify, Analyze, and Recommend

Get everything you need to build
apps that integrate with IBM Cloud.

You can build apps that seamlessly integrate with IBM Cloud using the IBM Cloud Developer Console for Apple. This allows you to quickly tap into Watson Services for Core ML, as well as other IBM cloud services including authentication, data, analytics, and more. The console provides a catalog of starter kits designed for common frameworks that integrate with IBM Cloud.

Learn More About Core ML
Find out how to use the Core ML framework to easily integrate machine learning models into your app with just a few lines of code.

Begin with Watson Starters
Run a basic model to perform image classification locally or integrate continuous learning with the Watson Model APIs.

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Go Further with Watson Services
Create more advanced models by using your own images to make a custom model that improves over time with continuous learning.