IBM Launches Deep Learning as a Service for ML and AI Developers
- IBM has rolled out it’s Deep Learning as a Service platform for AI developers
- It aims to help developers run hundreds of deep learning models at the same time
- The service is available on Watson Studio
IBM has announced the launch of it’s Deep Learning as a Service (DLaaS) platform for AI developers.
It aims to help developers run hundreds of deep learning models at the same time while building their neural networks. With DLaaS, developers can now train their deep neural networks using the usual popular frameworks like TensorFlow, Caffe2, and PyTorch without having to splurge money on expensive hardware.
What makes this an attractive choice is that users only have to pay for the GPU time while using only those resources which they need to train their models.
Developers can choose from a set of deep learning frameworks, a neural network model, training data and cost constraints. DLaaS takes care of the rest, and provides them an interactive experience.
The service saves a ton of time as well for the developers. They just have to clean their data, upload it, begin training and then download and view the training results. It’s a fairly straightforward process and does not require extremely advanced machine learning techniques.
It takes potentially days and weeks to run iterative training models. According to IBM:
This training process has been a challenge for data scientists and developers. To simplify this neural network building process and making it possible even for professionals without deep coding experience to do it, Deep Learning as a Service now includes a unique Neural Network Modeler. Neural Network Modeler is an intuitive drag-and-drop interface that enables a non-programmer to speed up the model-building process by visually selecting, configuring, designing and auto-coding their neural network using the most popular deep learning frameworks.
Our take on this
IBM has joined the likes of Google, Facebook and Amazon in trying to take the difficulty out of training deep neural network models. Finding experienced data scientists and machine learning practitioners is a challenging and expensive task for organizations. So models like this DLaaS, Google’s AutoML and H2O’s Driverless AI are making machine learning as autonomous as possible.
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