Why deep learning on a data and AI platform?
With advancements in compute, algorithm and data access, enterprises are adopting deep learning more widely to extract and scale insight through speech recognition, natural language processing and image classification. Deep learning can interpret text, images, audio and video at scale, generating patterns for recommendation engines, sentiment analysis, financial risk modeling and anomaly detection.
High computational power has been required to process neural networks due to the number of layers and the volumes of data to train the networks. Furthermore, businesses are struggling to show results from deep learning experiments implemented in silos. IBM Machine Learning Accelerator, a deep learning capability in IBM Watson Studio on IBM Cloud Pak® for Data, helps a business:
- Scale compute, people and apps dynamically across any cloud.
- Manage and unify large data sets and models with transparency and visibility.
- Adapt models continuously with real-time data from edge to hybrid clouds.
- Optimize cloud and AI investments with faster training and inference.
Benefits
Speed time to deep learning results
Build your models from initial prototype to enterprise-wide quicker. Accelerate time to train and deploy deep learning workloads with high accuracy.
Scale AI powered insights and prediction
Exploit an information architecture with integrated data and AI services. Push deep learning models for apps in a containerized, hybrid cloud foundation.
Simplify AI and cloud investments
Unite data and model deployment anywhere. Share and optimize GPU and CPU allocations tuned to workload demands.
Expand use and increase accuracy of models
Speed large, high resolution image processing. Improve throughput, latency and availability with autoscaling.
Boost system use and resiliency
Promote cross-business unit and enterprise use with multitenancy. Maximize use of GPU resources with elastic, distributed training and inference.
Govern and secure mission critical AI workloads
Increase transparency and visibility from data prep to model deployment. You can also lessen compliance, legal, security and reputational risks.
Use cases
- Image classification for disease diagnostics, public safety and social media
- Speech-to-text recognition for call center management, mobile apps and automated transcriptions
- Optical character recognition (OCR) for anomaly and fraud detection, automated document validation and cybersecurity
- Financial risk modeling for regulatory compliance, credit assessment and portfolio management
- Natural language processing for sentiment analysis, tone analysis and brand monitoring
- Recommendation engine for behavioral predictions, customized offers and next best actions
- Video analysis for public safety, theft prevention, worker safety and inventory management
Key features
- Rapid prototyping and deployment
- End-to-end information architecture
- Containerized infrastructure management
- High resolution, large model support
- Multitenant deployment
- Elastic, distributed training and inference
- Autoscaling, autosearch and load balancing
- AI lifecycle management
- Explainable AI with model monitoring