Managed Services
Elastic Operations for AI is a set of frameworks, processes, and experts for maintaining AI in operation.
Why do AI Applications Need Elastic Operations?
phData’s managed service teams work alongside you, offering the necessary personnel, processes, and frameworks to help you leverage your data and efficiently run your GenAI, ML, and Advanced Analytics applications. This approach frees up your development team, allowing them to continue to innovate and develop data applications that differentiate your business.
Elastic Operations for AI encompasses best practices from DataOps, MLOps, FinOps, and Security Ops to manage your data platform and GenAI applications. Our flexible managed service team is focused on automating and optimizing your data infrastructure. We have the ability to deploy specialized experts on-demand to address operational issues promptly, eliminating distractions on your development team and minimizing managerial tasks. Our dedicated approach ensures that your team is freed from complexities, allowing you to focus on your core activities.
Overcoming Common Operational Challenges
The initial step involves acknowledging the risks tied to operational challenges, like:
- Challenge of attracting & retaining skilled professionals
- Insufficient governance practices
- Lack of proper controls and guardrails to protect data and your business
- Escalating costs due to growing inefficiencies
- Slow progress due to poor GenAI development lifecycle processes
- The burden of ongoing model maintenance
- Manual access controls to GenAI tools and platform resources
- Inadequate data management practices
- Inability to meet the performance needs of the business velocity
Customer Success Stories
A Healthlake AI model for patient cohort selection and readmission risk prediction, ensuring scalability to serve millions of patients.
Saved time and resources by implementing an AI platform to enhance employee productivity in processing a large volume of invoices, leading to improved efficiency, accuracy, and quality.
Built MLOps framework for pricing algorithms that increase market share, shareholder value and save millions in operational expenses.
Extended an initial AI POC to be production ready, with an extensible AI platform to drive reliability and future use cases, improving the overall experience of learners working with a digital tutor.
Components of a successful AI Data Platform
Proactive Monitoring
Trained platform expert solution architects and data engineers backed with 24×7 real-time support.
Robust Governance
Trained platform expert solution architects and data engineers backed with 24×7 real-time support.
Cost Management
Trained platform expert solution architects and data engineers backed with 24×7 real-time support.
Streamlined Workflows
Trained platform expert solution architects and data engineers backed with 24×7 real-time support.
24/7 Support
Reference architecture, runbooks, guides, and best practice documentation instilled with every team and user.
Scale with Automation
Automation for onboarding, monitoring and reporting. SQL transformation tools and Infrastructure-as-Code programming.
Experts in AI and the Modern Data Stack Technologies
Want to learn more about managing an AI platform?
Take the next step
with phData.
Learn how phData can help solve your most challenging data analytics and machine learning problems.
