Targeting the AI skills gap, IBM launches deep learning-as-a-service
A growing number of organizations are embracing artificial intelligence. Meanwhile, the rapid pace of technology advances and shortage of workers with the right skills for many emerging technologies is prompting many firms to invest in software-as-a-solution options.
With both of those trends in mind, IBM has announced the launch of its new deep learning-as-a-service, within the IBM Watson studio.
The new service announced came in the form of a blog from Ruchir Puri, chief architect, IBM Watson and IBM Fellow. “Drawing from advances made at IBM Research, deep learning-as-a-service enables organizations to overcome the common barriers to deep learning deployment: skills, standardization and complexity,” Purri said. “It embraces a wide array of popular open source frameworks like TensorFlow, Caffe, PyTorch and others, and offers them truly as a cloud-native service on IBM Cloud, lowering the barrier to entry for deep learning.”
Artificial intelligence will be the most disruptive class of technologies over the next decade, Purri said, “fueled by near-endless amounts of data, and unprecedented advances in deep learning. The rise of deep learning has been fueled by three recent trends: the explosion in the amount of training data; the use of accelerators such as graphics processing units (GPUs); and the advancement in the training algorithms and neural network architectures.”
While artificial intelligence has gotten plenty of press in recent months, it is actually not a technology, but a family of a dozen or so technologies. Deep learning is one of them, but perhaps one of the least understood.
“Training of deep neural networks, known as deep learning, is currently highly complex and computationally intensive,” Puri explained in his blog. “It requires a highly-tuned system with the right combination of software, drivers, compute, memory, network and storage resources. To realize the full potential of this rising trend, we want this technology to be more easily accessible to developers and data scientists so they can focus more on doing what they do best – concentrating on data and its refinements, training neural network models with automation over these large datasets, and creating cutting edge models.”
IBM’s goal is to make it easier for software developers to build deep learning models.
“Deep learning-as-a-service has unique features, such as Neural Network Modeler, to lower the barrier to entry for all users, not just a few experts. The enhancements live within Watson Studio, our cloud-native, end-to-end environment for data scientists, developers, business analysts and SMEs to build and train AI models that work with structured, semi-structured and unstructured data — while maintaining an organization’s existing policy/access rules around the data.”
IBM unveiled a few new technologies and capabilities this week that the firm said will “democratize AI for developers, data scientists and enterprise professionals.” These included:
- “IBM’s newly-launched Watson Data Kits provide industry-specific datasets to help enterprises train and develop AI within their company. Faster and more streamlined AI training workflows for data scientists let business leaders quickly extract valuable insight from their data. Watson Data Kits for travel and food will be available in Q2, with additional industry-specific kits launching later this year.”
- “We’ve also open-sourced the core of our Deep Learning as a Service offering -- Fabric for Deep Learning (Ffdl or fiddle for short). The move not only embraces a wide array of popular open source frameworks, but also lowers the barrier to entry for deep learning. It’s a truly cloud-native offering and the latest in IBM’s long history of establishing open source centers of gravity in Cloud, Data, AI and Transactions.”
- “Taking our open-source commitment two steps further we unveiled MAX (Model Asset Xchange) and CODAIT (the Center for Open-Source Data & AI Technologies). MAX is like an app store for machine learning models. Free of cost, the exchange will let users easily discover, rate and deploy available models. CODAIT expands the mission of the Spark Technology Center, making AI models dramatically easier to create, deploy and manage. With partners like Google, CODAIT will create much-needed AI industry standards.”