12 Steps to Navigating Your Generative AI Deployment

1. Assess and Evaluate Your Compute Requirements

Generative AI requires significant GPU/TPU performance estimate workload needs to ensure adequate capacity.

2. Evaluate Your Data Pipelines

Quality data is critical for training generative models. Audit data sources, ETL processes, labeling, etc.

3. Implement MLOps

To successfully build, deploy, and monitor generative AI models, MLOps processes like version control, experiment tracking, and model monitoring need to be in place.

4. Assess Model Risks

Generative models come with risks like bias, toxicity, and hallucinations. Put guardrails in place through testing and monitoring.

5. Evaluate AI Ethics

Consider generative models and mitigate via ethics reviews procedures.

6. Audit Security Posture

Generative models may create security risks. Review IAM, networks security, user authentication, and access controls.

7. Plan for Scalability

Design infrastructure for rapid scaling of compute storage and network to meet growing demands.

8. Enable Collaboration

Generative AI requires collaboration between data scientists, engineers, business teams, and technical leaders. Ensure tools are in place.

9. Consider Platforms

Leverage cloud-based AI platforms like Vertex AI to accelerate development with pre-trained models from Google Cloud.

10. Develop Responsible AI Principles

Create and commit to a responsible series of principles aligning to your organization’s values.

11. Invest in Skills Development

Sponsor training in MLOps, prompt engineering, and learning paths about generative AI to increase familiarity among employees.

12. Partnerships

Connect with a trusted technology partner to leverage their technical expertise to ensure that your deployment is not only technically sound, but aligns with your organization’s business needs and strategic objectives. We at dtclai are happy to help!

dtclai is a company that helps businesses with generative AI deployments.