July 26, 2018 | Written by: Armand Ruiz Gabernet
Categorized: News and Updates
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Artificial intelligence (AI) has already begun disrupting the way entire industries operate, fueled by constantly growing quantities of data and advancements in the training of deep neural networks –a field known as deep learning. Despite the growing integration of AI-powered applications in the business world, enterprises are still working through how to take advantage of these latest advancements in deep learning.
The reality is, to do AI at scale, you need the right people, enough data, and the right algorithms. At IBM, we think of integrating enterprise AI as more than just building and deploying models –it’s about creating and managing an entire system.Your teams need to be comfortable interfacing with multiple services to create that AI system, so you need to be sure you’re equipping them with the right tools and knowledge.
When it comes to training and building the models you’ll need, there are very few people with all the right skills. That’s where IBM Watson Studio comes in. The goal of Watson Studio is to provide tools for data scientists, application developers and subject matter experts to collaboratively and easily work with data and use that data to build and train models at scale. Watson Studio enables accessible data science and machine learning, and embraces some of the most popular open source libraries in the market, including TensorFlow, PyTorch, Keras and Caffe. It combines the flexibility, ease-of-use, and economy of a cloud service with the compute power of the latest innovations in deep learning.
Within Watson Studio is Deep Learning as a Service, which enables organizations to overcome barriers to deep learning deployment, including skills gaps, lack of standardization, and complexity. The service allows you to kick off a large number of training runs simultaneously on multiple containers and automate hyper parameter optimization, making integration of deep learning more efficient for your business.
Putting deep learning to work
Once you have the right tooling, there are actionable steps you can take to put deep learning to work.In our “Deep Learning in the Real World”webinar, you’ll learn about the latest trends in machine learning and how businesses are applying deep learning to real-world use cases. We’ll help you understand the importance of equipping your data science teams with the tools they need to build powerful models in an accessible, open AI environment. Learn how to build the right teams, prepare your data for AI, and apply the latest in deep learning to solve your most complex business challenges.
I’ll be joined by my deep learning colleague,Anthony Stevens, for a discussion on:
- The latest trends in the field of data science
- Understanding deep learning relative to machine learning
- The challenges enterprises face in building and deploying the right models (including what has worked and what has failed)
- The importance of open frameworks in AI
- Building the right teams to develop, integrate, and deploy AI, within existing lifecycle management processes
- How to build and manage an entire AI system –not just implement models
Don’t miss the live Q&A following the webinar, where you’ll have the opportunity to engage with us live and have your questions answered.
Register for our “Deep Learning in the Real World” webinar that takes place Wednesday, 1 Aug at 1 p.m. EDT.