Deployment and automation

Question	Example response
What are the requirements for scaling and load balancing?	Intelligent request routing; automatic scaling system; optimizing for fast cold starts by employing techniques such as model caching, lazy loading, and distributed storage systems; designing the system to handle bursty, unpredictable traffic patterns.
What are the requirements for updating and rolling out new versions?	Blue/green deployments, canary releases, rolling updates, and so on.
What are the requirements for disaster recovery and business continuity?	Backup and restore procedures, failover mechanisms, high availability configurations, and so on.
What are the requirements for automating the training, deployment, and management of the generative AI model?	Automated training pipeline, continuous deployment, automatic scaling, and so on.
How will the generative AI model be updated and retrained as new data becomes available?	Through periodic retraining, incremental learning, transfer learning, and so on.
What are the requirements for automating monitoring and management?	Automated alerts, automatic scaling, self-healing, and so on.
What is your preferred deployment environment for generative AI workloads?	A hybrid approach that uses AWS for model training and our on-premises infrastructure for inference to meet data residency requirements.
Are there any specific cloud platforms you prefer for generative AI deployments?	AWS services, particularly Amazon SageMaker AI for model development and deployment, and Amazon Bedrock for foundation models.
What containerization technologies are you considering for generative AI workloads?	We want to standardize on Docker containers that are orchestrated with Kubernetes to ensure portability and scalability across our hybrid environment.
Do you have any preferred tools for CI/CD in your generative AI pipeline?	GitLab for version control and CI/CD pipelines, integrated with Jenkins for automated testing and deployment.
What orchestration tools are you considering for managing generative AI workflows?	Apache Airflow for workflow orchestration, particularly for data preprocessing and model training pipelines.
Do you have any specific requirements for on-premises infrastructure to support generative AI workloads?	We're investing in GPU-accelerated servers and high-speed networking to support on-premises inference workloads.
How do you plan to manage model versioning and deployment across different environments?	We plan to use MLflow for model tracking and versioning, and integrate it with our Kubernetes infrastructure for seamless deployment across environments.
What monitoring and observability tools are you considering for generative AI deployments?	Prometheus for metrics collection and Grafana for visualization, with additional custom logging solutions for model-specific monitoring.
How are you addressing data movement and synchronization in a hybrid deployment model?	We will use AWS DataSync for efficient data transfer between on-premises storage and AWS, with automated synchronization jobs that are scheduled based on our training cycles.
What security measures are you implementing for generative AI deployments across different environments?	We will use IAM for cloud resources, integrated with our on-premises Active Directory to implement end-to-end encryption and network segmentation to secure data flows.