Scaling Data Science

 How best in class companies innovate with machine learning

What is deep learning?

Deep learning is a subset of machine learning where neural networks — algorithms inspired by the human brain — learn from large amounts of data. Deep learning algorithms perform a task repeatedly and gradually improve the outcome, thanks to deep layers that enable progressive learning. It’s part of a broader family of machine learning methods based on neural networks.

Deep learning is making business impact across industries. In life sciences, deep learning can be used for advanced image analysis, scientific research, drug discovery, prediction of health problems and disease symptoms, and the acceleration of new insights from genomic sequencing. In transportation, it can help autonomous vehicles adapt to changing conditions. It is also used to protect critical infrastructure and speed response.

Businesses often outsource development of deep learning.  However, it is better to keep the deep learning development work for use cases that that are core to your business. These include fraud detection and recommendations, predictive maintenance and time series data analysis, optimizing content display from your recommendation system, customer relationship management and preventing customer churn, and predicting the clickthrough rate of online advertising.

You can get started with deep learning for free with IBM Watson Studio and Watson Machine Learning.


Deep learning features

Experiment Assistant

Initiate and monitor batch-training experiments, then compare cross-model performance in real time, without worrying about log transfers and scripts to visualize results. You focus on designing your neural networks; IBM will manage and track your assets.

Open and flexible

Use your preferred deep learning framework: Tensorflow, Keras, PyTorch, Caffe and more. Manage your deep learning experiments with the tools you prefer: command-line interface (CLI), Python library or an interactive user interface.

Elastic GPU compute

Train neural networks in parallel, using market-leading NVIDIA Tesla GPUs — K80, P100 and V100. Pay only for what you use. Auto-allocation means not having to remember to shut down your cloud training instances. There are no clusters or containers to manage.

Hyperparameter optimization

Efficiently automate the search of your network’s hyperparameter space to ensure the best model performance with the fewest training runs.

Neural Network Modeler (beta)

Visually design your neural networks. Drag and drop layers of your neural architecture, then configure and deploy, using the most popular deep learning frameworks.

Deep learning benefits

Save time, not just money

Use your preferred IDE and existing workflows. CLI, Python library and REST access is balanced by visual debugging tools. Design and optimize your networks better and faster.

Intelligence on demand

Managed training means you focus on designing optimal neural network structures. Training assets are stored for you. Auto-allocation means you pay only for the compute resources required by the job.

Trusted cloud infrastructure

Optimized for enterprise production environments, it runs on the same infrastructure that hosts IBM Watson cognitive services.

Graphs, not log files

Forget text logs. Overlay accuracy-and-loss graphs in real time and track, then view, model hyperparameters to explore more deeply the training of your neural networks.

Team collaboration

Share experiments, debug neural architectures, access common data within hosted object stores and forward versioned models to your team, helping them to feed data into a continuous learning flow.

Product offering images

Use your favorite framework 

In Watson Studio, popular frameworks are pre-installed and optimized for performance through the Watson Machine Learning Service, and it's easy to add custom dependencies to your environments. Try Watson Studio now to focus only on your task; IBM will take care of your environments.

Explore Watson Studio →

Tutorials and use cases

Use a notebook, Keras and TensorFlow to build a language model for text generation

How do you counter fraudulent issues, such as product reviews? By using the same generative models that are creating them. This code pattern explains how to train a deep learning language model in a notebook, using Keras and TensorFlow. Using downloaded data from Yelp, you’ll learn how to install TensorFlow and Keras, train a deep learning language model and generate new restaurant reviews. Although the scope of this code pattern is limited to an introduction to text generation, it provides a strong foundation for learning how to build a language model.

Go to the tutorial

Deep Learning

Build a handwritten digit recognizer in Watson Studio and PyTorch

Recognizing handwritten numbers is a simple, everyday skill for humans — but it can be a significant challenge for machines. Now that’s changing, with the advancement of machine learning and AI. There are mobile banking applications that can scan handwritten checks instantaneously, and accounting software that can extract dollar amounts from thousands of contracts in minutes. If you are interested in knowing how all of this works, please follow along with this code pattern as we take you through the steps to create a simple handwritten digit recognizer, using Watson Studio and PyTorch.

Go to the tutorial

Build a handwritten digit recognizer in Watson Studio and PyTorch

Get started with deep learning

Start executing your deep learning experiments now.