Create and deploy custom annotation models without writing a single line of code. With our new IBM Watson Knowledge Studio free plan, developers can create custom annotator components and five machine learning projects using 5GBs of storage. And there's no time restriction to do so. Use both machine learning and rule-based approaches to create custom language models using a cloud-based application. The rule-based approach (currently experimental) gets results fast, while the machine learning approach helps the model scale.
With Watson Machine Learning Accelerator you can drive faster time to results and accuracy, running in special AI hardware in the Cloud on On-Premises. WML Accelerator comes with SnapML library. We have developed an effi cient, scalable machine-learning library that enables very fast training of generalized linear models. We have demonstrated that our library can remove the training time as a bottleneck for machine-learning workloads, paving the way to a range of new applications. For instance, it allows more agile development, faster and more fine-grained exploration of the hyper-parameter space, enables scaling to massive datasets and makes frequent retraining of models possible in order to adapt to events as they occur.
Today, we are enhancing our product to accelerate the value of AI in your companies and announcing Watson Studio. IBM Watson Studio is an integrated environment designed to make it easy to develop, train, manage models and deploy AI-powered applications and is a SaaS solution delivered on the IBM Cloud. IBM Services has already been using Watson Studio to train business patterns. In specific client situations, we are able to train business patterns and encode into end user applications in a few hours. Our consultants now embed Watson-powered AI across all processes in our client business with simplicity and speed, so our consultants spend more time creating incremental value, rather then coding applications.
It was too much fun creating my own convolutional neural network with Watson Studio! The Neural Network Modeler provides expressive and intuitive graphical tools for building powerful deep learning models. For example, you can save and deploy models as a REST API with a few clicks, minimizing the time it takes to put machine learning models to work. In this example, I used the familiar MNIST data set (images of hand-written digits) and created a three layers deep CNN. I also used the downloaded code to train my model locally in my Jupyter Macbook environment.
As data people, we very typically spend a great deal of time summarizing our findings to stakeholders in a clear, concise and impactful way. Often times, due to the lack of infrastructure, we end up using presentation files with chart images. This can become a real pain when we need to make modifications or when the analysis needs to "live on". Typically, this is where BI (business intelligence) or dashboard tools shine. Unfortunately, this can be a major stumbling block for smaller shops who rely on a lot of local analysis and may not have the budget for a BI tool.