Shadi Copty discussed one IDE and one runtime for AI and data science across the enterprise as part of IBM’s AI approach. Shadi identified three major trends that are impacting data science and ML:
- Diversity of skillsets and workflows with demand remaining high and new approaches like deep learning posing additional challenges.
- IBM: Visual productivity and automation of manual work
- IBM: Improved collaboration and support for tools of choice
- Data movement is costly, risky and slow as enterprise data is fragmented and duplication brings risk
- IBM: Bring data science to the data
- Operationalizing models is hard with most models never deployed
- IBM: Ease of deployment
- IBM: Model management
Two key product packages:
- Watson Studio is building and training – exploration, preparation etc. Samples, tutorials, support for open source, editors, integrations, frameworks for building models etc.
- Watson Machine Learning is the execution platform. One runtime for open source and IBM algorithms with standard APIs etc.
- Data refinery for better data management
- SPSS Modeler UI integrated into Watson Studio. One click deployment and spark execution
- ML Experiment Assistant to find optimal neural networks, compare performance, use GPUs etc
- Neural Network Modeler to provide a drag and drop environment for Neural Networks across TensorFlow, Pytorch etc
- Watson Tools to provide some pre-trained models for visual recognition
The direction here is to deliver all these capabilities in Watson Studio and Watson Machine Learning and integrate this into ICP for Data so it is all available across private, public and on-premise deployments. APIs and applications layer on top.
Ritika Gunnar and Bill Lobig came up to discuss trust and transparency but this is all confidential for a bit… I’ll post next week [Posted here].
Sam Lightstone and Jason Tavoularis wrapped up the session talking about the effort to deliver AI everywhere. Products, like databases, are being improved using AI in a variety of ways. For instance, ML/AI can be used to find the best strategy to execute SQL. For some queries, this can be dramatically faster. In addition, SQL can be extended with ML to take advantage of unsupervised learning models and return rows that are analytically similar for instance. This can reduce the complexity of SQL and provide more accurate results. IBM Cognos Analytics is also being extended with AI/ML. A new assistant based on conversational AI helps find the available assets that the user can access. As assets are selected the conversation focuses on the selected assets, suggesting related fields for analysis, appropriate visualizations or related visualizations, for instance. Good to see IBM putting is own tech to work in its tech.