Companies are drowning in data. Clouds, lakes, vaults, and warehouses contain trillions of data points from thousands of sources that are just waiting to be processed and analyzed to improve productivity, customer experience, profitability, and growth.
Businesses in every industry are considering strategies to apply Artificial Intelligence (AI) and Decision Intelligence (DI) solutions to take advantage of investments in big data. As they move forward with these efforts, there are governance and compliance strategies that, while applicable to most technology projects, are especially important when considering AI/DI applications that include IoT. Personal and private data must be protected, and transfer must be secured to eliminate vulnerabilities.
AI/DI solutions rely on inference to rapidly process and model massive amounts of data to and from potentially thousands of devices and storage locations. In this new era of inference, security, and compliance cannot be left to chance. Inference is the key to unlocking big data and marrying it with real-time sources. Interoperability, scalability, security, and compliance ensure that businesses benefit from technology. As companies unlock the power of big data, governance becomes more important than ever.
Managing momentum
Enterprises that want to take full advantage of AI/DI solutions to harness big data understand that AI/DI applications must deliver trusted results and experiences for customers and employees. Secure access and transport of private and privileged data between and among enterprises, cloud storage, and users are of utmost concern, and enterprises are obligated to transport and manage data securely and observe all rules governing data privacy and protection. As AI/DI momentum builds, challenges faced by businesses include:
- Vulnerability – Poor data governance in the past has made enterprises vulnerable to increasingly sophisticated data breach efforts. Secure, private connections minimize exposure of sensitive data.
- Prioritizing security – Cloud providers may not have adequately considered security in the interest of reducing cost or time to market. Market damage from a security breach is felt by the enterprise that implements the system, not the cloud service provider.
- Compliance – Governments are mandating specific data storage, protection, management, and access requirements. Global enterprises and supply chains must navigate and comply with a complex set of data sovereignty and protection rules.
As enterprises deploy more sophisticated AI/DI solutions, flexibility becomes even more important. Given the wide variety of data sources and storage platforms, enterprises insist on interoperability. Due to the scale and geographic distribution of IoT elements and data, AI/DI deployments for IoT will be fairly long-lived; and the flexibility to train models and infer data from anywhere without reconfiguration is critical to reducing costs and maintaining momentum.
Whether detecting fraud in financial transactions, optimizing operations, managing inventories, predicting customer preferences, or identifying a medical condition before it becomes critical, AI/DI applications deployed without the complexities of infrastructure management enable enterprises to focus on innovation, productivity, and growth.
Vultr serverless inference
Vultr Serverless Inference, tailored for AI/DI applications, offers a global, self-optimizing platform for effortless model deployment and serving. Leveraging a serverless architecture, it eliminates the complexities of scaling and managing infrastructure, allowing businesses to focus on their business.
Turnkey retrieval augmented generation enables users to upload data or documents as inputs to deployed models without requiring model training or risking data leakage to public models. Vultr Serverless Inference also allows for the integration of any trained AI model, and its OpenAI-compatible API simplifies deployment and integration. An extensive global network guarantees efficient, reliable performance with minimal latency across six continents. Users benefit from superior performance, reduced operational costs, and autonomous scalability.
With Vultr Serverless Inference, businesses can leverage AI/DI investments by integrating models trained anywhere, whether on a Vultr platform, other cloud providers, or on-premises. By eliminating the need to retrain or reconfigure AI/DI models for deployment, AI/DI solutions can be implemented sooner, reducing operational costs. Without modifications, highly customized models specific to each business are more accurate and remain aligned with the unique needs of the business’s unique needs. Companies can also securely upload data to a private retrieval-augmented generation vector database and leverage a pre-trained public model for custom outputs without the cost of training a model.
Unlocking value from big data requires a sophisticated, integrated AI/DI solution that is quickly deployed, easily maintained, compliant, and secure. Ensuring interoperability, scalability, security, compliance, and data residency while reducing latency must be met across all geographies. Not having to manage complex infrastructure and interconnection frees businesses to focus on training AI/DI solutions to meet unique business needs and create differentiation.