The first impulse of many artificial intelligence (AI) and machine learning (ML) model builders and application developers, says Dell Technologies, is to turn to easy-to-consume public cloud services, but there may be cost issues and integration challenges with existing systems and data.
Additionally, it adds, the rise of large language models such as ChatGPT and Bard necessitates increased compute power and big data sets for AI/ML training. “This raises cost and privacy concerns – driving even more businesses to own their AI/ML operations and management.
“But when organizations build a private cloud to address these concerns, they often find they have a shortage of cloud skills to drive solutions at the speed they need.”
To combat these and other challenges, the company recently announced three new managed services:
- Dell Managed Developer Cloud: This offering contains self-service virtual machines and containers in an API-based cloud environment, with built-in infrastructure-as-code infrastructure management. This accelerates innovation by freeing developers from managing infrastructure so they can spend more time coding, the company says.
- Dell Managed Services for ML Ops: A fit-for-purpose platform for ML model development with “integrated lifecycle management based on Dell validated designs. This gets models to production faster by reducing the complexity of deploying and maintaining AI/ML systems.”
- Colocation with Dell Technologies Services: This offering “streamlines cloud integration, simplifies deployment and makes operations more efficient.”
“Contemporary developers spend less than 20 per cent of their time coding,” a release stated. “The rest of the time is spent waiting for IT resources and approvals, or managing underlying infrastructure. Similarly, only about 36 per cent have deployed machine learning beyond the model stage and many ML projects never make it to production.”