Course Outline
Introduction to Model Optimization and Deployment for Government
- Overview of DeepSeek models and associated deployment challenges
- Understanding model efficiency: balancing speed and accuracy
- Key performance metrics for AI models in government applications
Optimizing DeepSeek Models for Performance for Government
- Techniques for reducing inference latency in government systems
- Model quantization and pruning strategies for enhanced efficiency
- Utilizing optimized libraries to enhance DeepSeek models for government use
Implementing MLOps for DeepSeek Models for Government
- Version control and model tracking in government environments
- Automating the retraining and deployment of AI models for government operations
- CI/CD pipelines for AI applications within government agencies
Deploying DeepSeek Models in Cloud and On-Premise Environments for Government
- Selecting appropriate infrastructure for deployment in government settings
- Deploying with Docker and Kubernetes for secure and scalable operations
- Managing API access and authentication to ensure data security for government
Scaling and Monitoring AI Deployments for Government
- Load balancing strategies to support high-demand AI services for government
- Monitoring model drift and performance degradation in government systems
- Implementing auto-scaling solutions to optimize resource utilization for government applications
Ensuring Security and Compliance in AI Deployments for Government
- Managing data privacy in AI workflows within government agencies
- Compliance with federal regulations and standards for AI deployments
- Best practices for secure AI implementations in government environments
Future Trends and AI Optimization Strategies for Government
- Advancements in AI model optimization techniques relevant to government operations
- Emerging trends in MLOps and AI infrastructure for government use
- Building a strategic roadmap for AI deployment in government agencies
Summary and Next Steps for Government
Requirements
- Experience with AI model deployment and cloud infrastructure for government applications
- Proficiency in a programming language (e.g., Python, Java, C++)
- Understanding of MLOps and model performance optimization techniques
Audience
- AI engineers optimizing and deploying DeepSeek models for government projects
- Data scientists working on AI performance tuning in public sector environments
- Machine learning specialists managing cloud-based AI systems for government agencies
Testimonials (2)
the ML ecosystem not only MLFlow but Optuna, hyperops, docker , docker-compose
Guillaume GAUTIER - OLEA MEDICAL
Course - MLflow
I enjoyed participating in the Kubeflow training, which was held remotely. This training allowed me to consolidate my knowledge for AWS services, K8s, all the devOps tools around Kubeflow which are the necessary bases to properly tackle the subject. I wanted to thank Malawski Marcin for his patience and professionalism for training and advice on best practices. Malawski approaches the subject from different angles, different deployment tools Ansible, EKS kubectl, Terraform. Now I am definitely convinced that I am going into the right field of application.
