AI News, So what exactly is IBM doing different with machine learning?

So what exactly is IBM doing different with machine learning?

In a similar way, suppose you are working on a complex model for fraud detection.

It would make sense to dramatically accelerate training on Power machines leveraging GPUs and the high speed NVLink connections (see, but then you might want to deploy the model right into a CICS in-transaction call on the mainframe for sub-millisecond scoring of a credit card transaction.

This keeps customers from working against data gravity: We let them bring the tools, intelligence, and analytics to the data rather than forcing them to drag data slowly — and insecurely — from place to place.

In a third use case, Watson Speech-to-Text and advanced NLP capabilities in Watson Explorer (WEX) integrate with DSX to combine unsupervised and supervised techniques for understanding deep intent in customer calls into the call center.

Another strength is a steady stream of innovation coming in from IBM Research, ranging from automatic feature engineering to cognitive databases to distributed deep learning for scaling acceleration and more.

Doing feature engineering and building models and infusing them into the applications and processes of a business needs an intimate knowledge of the business and its dynamics — both at the high level of the industry sector and at the low level of transaction flow, regulation, and so on.

Create and deploy a predictive model using IBM Data Science Experience and Watson Machine Learning

The new code pattern Create and deploy a scoring model to predict heartrate failure, demonstrates how to build an end-to-end application that utilizes a predictive machine learning model deployed into production.

Next, as a data scientist using Apache Spark, you’ll learn how to select features and build a pipeline for training, and evaluate a machine learning model.

You’re transforming disparate data sources into a single format that can be used by modeling tools while selecting attributes, removing invalid data, and ensuring consistency across sources.

This additional preparation requires extensive understanding of machine learning algorithms, and tools, to ensure the input provides the best possible outcome.

Extracting insights from data by creating a model is only one piece, the model needs to be continuously evaluated for accuracy as new data emerges, and validated against assumptions.

Pipelines have recently accelerated the discipline and adoption of machine learning, by bundling the task of transforming raw input into a format understandable by a trained model.

Before pipelines it was extremely difficult to quickly deploy models for consumption, each model required the app or the caller to perform all the transformations on the input data before calling the model.

After a model is selected and it’s determined that it achieves the defined business objectives, it’s usually thrown over the wall to a development team.

Put on your data engineering, data scientist, and developer hats, and accelerate your path to production and insights with the Create and deploy a scoring model to predict heartrate failure code pattern.

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