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.
Build your models from initial prototype to enterprise-wide quicker. Accelerate time to train and deploy deep learning workloads with high accuracy.
Exploit an information architecture with integrated data and AI services. Push deep learning models for apps in a containerized, hybrid cloud foundation.
Unite data and model deployment anywhere. Share and optimize GPU and CPU allocations tuned to workload demands.
Speed large, high resolution image processing. Improve throughput, latency and availability with autoscaling.
Promote cross-business unit and enterprise use with multitenancy. Maximize use of GPU resources with elastic, distributed training and inference.
Increase transparency and visibility from data prep to model deployment. You can also lessen compliance, legal, security and reputational risks.
- 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
Start data science projects anywhere with a shared compute resource pool. Reduce training times and produce higher quality models. Scale-out, enterprise-class training and inference services with API support for batch, streaming and interactive deployment.
Deploy deep learning as part of data and AI services with support for popular frameworks. Aggregate open source and third-party tools in a unified, governed environment.
Run machine learning and deep learning models natively in Red Hat® OpenShift®. Deploy containerized models inside a firewall while keeping data on premises and maintaining cloud portability.
Increase the amount of memory available for deep learning models beyond the GPU footprint. Implement more complex models with larger, more high-resolution images.
Allocate and share compute powers tuned to model demands in a multitenant architecture. Securely share your compute resources across tenants to maximize use.
Enable dynamic scaling of resources, up or down, based on policies to ensure higher priority jobs run fast. Build real-time training visualization and runtime model monitoring. Automate hyperparameter search and optimization for faster development.
Prepare, build, run and manage machine learning and deep learning models. Run through the training cycle with more data to improve the model continuously.
Increase reliability and resiliency for model deployment with precompiled and validated machine learning and deep learning models. Accelerate performance with software optimized to run on target systems.
Manage and monitor deep learning models from small to enterprise-wide deployment. Monitor model fairness and explainability while mitigating model drift and risk.
Get an overview of Machine Learning Accelerator.