Summary In the healthcare domain, there is a lot of real-time data that is generated. This data needs to be monitored to generate predictions and alerts for healthcare professionals. Manual monitoring of such data is difficult. In this code pattern, we add AI-based predictions and automate the monitoring of healthcare data. To demonstrate IBM Cloud Pak for Data technology on AWS Cloud, we have taken the use case of predicting cardiac events based on real-time monitoring of patients’ health data. Description In this developer code pattern, you will learn to build a machine learning model with n ..read more

Description As a deep learning practitioner, you want reliability and scalability while orchestrating your training jobs. In addition, you would want to do this in a consistent manner across multiple libraries. With Fabric for Deep Learning (FfDL) on Kubernetes, you can achieve this by giving users the ability to leverage deep learning libraries such as Caffe, Torch, and TensorFlow in the cloud in a resilient manner with minimal effort. The platform uses a distribution and orchestration layer that facilitates learning from a large amount of data in a reasonable amount of time across compute no ..read more

Summary This code pattern teaches developers to quickly train a machine learning algorithm using PowerAI virtualization software through Nimbix. You can increase speeds over a non-Power architecture when running unsupervised learning iterations using NVIDIA GPUs and the CUDA parallel computing platform. Description This code pattern is designed for anyone who wants to increase their machine learning speed, showing you how to leverage IBM’s new PowerAI for machine learning. You’ll use a Jupyter Notebook to showcase an example of machine learning with a time series on IBM Power8® systems. The no ..read more

Summary This code pattern was built for developers looking to leverage the new PowerAI offering from IBM. We will use a Jupyter Notebook to showcase an example of transfer learning with the TensorFlow Inception model on IBM POWER8® systems. The notebook will focus on creating a custom classifier to recognize houses with pools vs. houses without pools from JPEG images. The intended audience is application developers who need to efficiently build powerful deep-learning applications, but may not have an abundance of time or data science experience. Description Transfer learning is the process of ..read more

Summary The IBM Model Asset eXchange (MAX) models that are hosted on the Machine Learning eXchange (https://ml-exchange.org/models/) have given application developers without data science experience easy access to prebuilt machine learning models. This code pattern shows how to create a simple web application to visualize the text output of a MAX model. The web app uses the Object Detector from MAX and creates a simple web UI that displays bounding boxes around detected objects in an image and lets you filter the objects based on their label and probable accuracy given by the model. Descriptio ..read more

Summary The introduction of the IBM Model Asset eXchange (MAX) that is hosted on the Machine Learning eXchange has given application developers without data science experience easy access to prebuilt machine learning models. This code pattern shows how simple it can be to create a web app that utilizes a MAX model. The web app uses the Image Caption Generator from MAX and creates a simple web UI that lets you filter images based on the descriptions given by the model. Description Every day 2.5 quintillion bytes of data are created, based on an IBM study. A lot of that data is unstructured data ..read more

Summary In this developer code pattern, we will show how to create a database of ecommerce clickstream data with DataStax Enterprise or Apache Cassandra. Using Red Hat OpenShift and the DataStax Kubernetes Operator for Apache Cassandra, you can deploy this distributed database on-premises or on your cloud provider of choice with a unified OpenShift experience. If you prefer a database-as-a-service provider, get up and running quickly with IBM Cloud Databases for DataStax. Description The provided ecommerce website is built using server-side rendering (SSR) with Next.js and React components. As ..read more

Summary In this code pattern, build a framework that lets users send voice queries using the WhatsApp application and get a response from IBM Watson Assistant. The query from the user is sent to the Watson Speech to Text Service through a custom application. The output from the Watson Speech to Text Service is then fed to Watson Assistant. Watson Assistant responds back to the user with an appropriate answer. Description Conversational AI voicebots are helping businesses automate repetitive and time-consuming queries. This gives your team more time to tackle more complex, high priority strateg ..read more

Summary In this code pattern, learn how to authenticate users on your chatbot with an SMS one-time passcode (OTP). Description Chatbots are prevalent everywhere. And, some conversations are confidential, so chatbots must verify the authenticity of its users. One example of this is a customer who holds an insurance policy who uses a chatbot to get more information about that insurance policy. Sending a one-time passcode through SMS is a popular way to identify a user. This code pattern shows how to build a chatbot that authenticates users through an SMS one-time passcode. You also learn to use ..read more

Summary In this code pattern, learn how to use the Cortex Certifai Toolkit to create scans to evaluate the performance of multiple predictive models using IBM Watson Studio. Description Explainability of AI models is a difficult task that is made simpler by Cortex Certifai. The Cortex Certifai Tookit evaluates AI models for robustness, fairness, and explainability, and allows users to compare different models or model versions for these qualities. Certifai can be applied to any black-box model including machine learning models and predictive models, and works with a variety of input data sets ..read more