AI Infrastructure plays a key role in the speed and cost-competitiveness of developing and deploying advanced AI models. The current demand for powerful AI infrastructure for model training is driven by the emergence of generative AI and foundational models, where on occasion thousands of GPUs must cooperate on a single training job for the model to be trained in a reasonable time. Delivering efficient and high-performing AI training requires an end-to-end solution that combines hardware, software and holistic telemetry to cater for multiple types of AI workloads. In this report, we describe IBM's hybrid cloud infrastructure that powers our generative AI model development. This infrastructure includes (1) Vela: an AI-optimized supercomputing capability directly integrated into the IBM Cloud, delivering scalable, dynamic, multi-tenant and geographically distributed infrastructure for large-scale model training and other AI workflow steps and (2) Blue Vela: a large-scale, purpose-built, on-premises hosting environment that is optimized to support our largest and most ambitious AI model training tasks. Vela provides IBM with the dual benefit of high performance for internal use along with the flexibility to adapt to an evolving commercial landscape. Blue Vela provides us with the benefits of rapid development of our largest and most ambitious models, as well as future-proofing against the evolving model landscape in the industry. Taken together, they provide IBM with the ability to rapidly innovate in the development of both AI models and commercial offerings.

The uploaded CSV file is stored in IBM Cloud Object Storage service as a dataset. The dataset is then used to build and deploy a machine learning model. This course is intended for anyone with basic to intermediate experience in machine learning who wants to improve skills in regression but most importantly learn how to deploy machine learning models on IBM Cloud whether they be regressors or classifiers. By the end of this course you will know how to do that and have a little refresher on data visualization and data analysis. Do not worry if you pay attention in one of the videos I show you how to solve and prevent many problems when registering the model as well as many other things.

Using AI, IBM Watson and hybrid cloud, IBM is expanding the real-time data insights available to fans of the US Open Tennis tournament so they can get even deeper, match-by-match details on their favorite players and rounds as the event unfolds. IBM has been working with the US Open and its host, the United States Tennis Association (USTA), for 30 years to help bring the matches to tennis fans. For 2021 the company has enhanced its digital offerings with new IBM Power Rankings that fans can use to see what the data is saying about players ahead of upcoming matches. Among the newly available insights are "Likelihood to Win," "Ones to Watch," and "Upset Alerts." In addition, the first-time US Open Fantasy Tennis experience was also launched on the USOpen.org

Alex Migitko started playing tabletop role-playing games (RPGs) 15 years ago. But as life got more demanding, he couldn't commit to the time needed for preparation and play, both as a game facilitator and player. Though passionate about gaming, he ultimately stopped. These "aging out" stories are all too common. Players fall in love with gaming because it provides such depth and breadth of creativity and escape.

Auto insurance fraud costs companies billions of dollars every year. Those losses trickle down to policyholders who absorb some of that risk in policy rate increases. Thélem assurances, a French property and casualty insurer whose motto is "Thélem innovates for you", has launched an artificial intelligence program, prioritizing a fraud detection use case as its initial project. Fraud detection is a model that lends itself well to online machine modeling and is a project that would allow us to enter into artificial intelligence starting with the analytical field that we have prioritized. A successful fraud detection project would deliver immediate, significant financial gains for the company.

IBM Developer Advocates Anam Mahmood and Sidra Ahmed conducted a workshop on 3rd February. Their goal was to show how everyone can easily use Jupyter Notebooks in IBM Watson Studio to run small pieces of code that process your data and immediately show you the results of your computation in an interactive environment and quickly build machine learning models. The session was divided into two sections. The first half of the workshop was led by Sidra, where she welcomed the audience and talked about the agenda. She then explained to them about Data Science, Artificial Intelligence, Machine Learning, and Deep Learning.

To enable mobile users to leverage IBM Watson services through a messenger app, complete this developer code pattern and build a framework that can act as an intermediator in connecting Watson services to WhatsApp Messenger. There are currently 2.4 billion users on WhatsApp, and the number keeps climbing. For medium and large businesses, WhatsApp introduced WhatsApp Business, which powers communication with customers all over the world so they can connect with businesses on WhatsApp in a simple, secure, and reliable way. To make the conversations smarter, Watson AI can be infused as the back end to deliver advanced AI capabilities to customers. In this code pattern, you will learn to build a framework to connect Watson Machine Learning, deploy a simple house price-prediction model, and access it from your WhatsApp Messenger.

"Two roads diverged in a wood and I – I took the one less traveled by." It may not be obvious at first, but Robert Frost's poem about standing at the crossroads of choice applies particularly well to the telecommunications industry. With the rise of 5G cellular networks and the need for more agility during COVID-19, Telco companies have to make a crucial decision: stay in the traditional lanes of providing connectivity or evolve with AI-powered digital transformation. For many Telcos, the "road less traveled" via AI isn't just a question of innovation; it's critical to developing new business models that are sustainable and scalable for the future. The Telco industry's AI reinvention lies in three key strategies: monetizing at the edge, saving costs through automation and improving customer engagement.

In this tutorial, we present the high-level steps that are involved in connecting an Android device to the cloud and developing analytics models to analyze sensor data. By the end of this tutorial you should be able to set up your own IoT hub for streaming, storing and processing device data. The following figure shows the architecture of our sample app. This tutorial requires an Android device (smartphone), an internet connection, and an IBM Cloud account. In Step 1 you will create an account on IBM Cloud and install an application on your Android phone.

The All England Tennis Club (AELTC) took the decision to cancel Wimbledon 2020 on April 1, to the dismay of sports fans around the world, who were looking forward to a fortnight of world class tennis, celebrity spotting, and strawberries and cream. However, since the cancellation, the AELTC has been working hard behind the scenes in partnership with IBM to deliver tennis fans a Wimbledon experience that could go some way to filling the void left behind by the event. The pair today unveiled'The Greatest Championships', a digital recreation of the famed tournament to take place over the course of two weeks, starting Monday June 29 (the original start date for Wimbledon 2020). Using IBM technology, the AELTC has remastered a selection of the greatest matches to play out at Wimbledon, which will be released round-by-round to replicate the tournament format. "It's not going to be a substitute for the real thing but it's going to be our way of providing something for our fans and recreating Wimbledon in the best way we can," said Alex Willis, Head of Communications, Content & Digital at AELTC.