The IBM Analytics Engine team is excited to announce the General Availability (GA) of IBM Analytics Engine, the next generation of IBM’s Apache Spark and Apache Hadoop cloud service in the London DC.
By 2019, about 60 percent of all enterprise workloads will be in the cloud, according to 451 Research. Only 45 percent of workloads are in the cloud now1. As this transition to cloud continues to grow, speed is an increasingly important factor for companies staying on top of their data.
Today, we are enhancing our product to accelerate the value of AI in your companies and announcing Watson Studio.
This post is an excerpt from our solution tutorial that walks you through the process of building a predictive machine learning model, deploying it as an API to be used in applications, testing the model and retraining the model with feedback data. All of this happening in an integrated and unified self-service experience on IBM Cloud.
Putting the engine to work: how IBM Analytics Engine can help you harness Hadoop and Spark for business benefit
For many companies, the potential of big data analytics may seem both exciting and overwhelming. Technologies like Hadoop and Spark promise to unearth new sources of value from the vast mountains of unstructured data your business generates every day. The newfound opportunity to get insight from data that has been dormant for years could act as an energy source to power the next phase of business growth.
This blog post is an excerpt from our solution tutorial - "Gather, visualize, and analyze IoT data". The tutorial walks you through setting up an IoT device, gathering mobile sensor data in the Watson IoT Platform, exploring data and creating visualizations and then using advanced machine learning services to analyze data and detect anomalies in the historical data.
Taming your neural networks: how controlled experimentation can help you build better machine learning models
Businesses today are eager to harness machine learning and deep learning for competitive advantage—yet few businesspeople realize that building a machine learning model or neural network is a marathon, not a sprint.
IBM Data Refinery, a feature of Watson Data Platform, helps reduce reliance on IT and give knowledge workers faster access to high-quality data