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Course Outline
Introduction to Model Optimization and Deployment
- Overview of DeepSeek models and deployment challenges
- Understanding model efficiency: speed vs. accuracy
- Key performance metrics for AI models
Optimizing DeepSeek Models for Performance
- Techniques for reducing inference latency
- Model quantization and pruning strategies
- Using optimized libraries for DeepSeek models
Implementing MLOps for DeepSeek Models
- Version control and model tracking
- Automating model retraining and deployment
- CI/CD pipelines for AI applications
Deploying DeepSeek Models in Cloud and On-Premise Environments
- Choosing the right infrastructure for deployment
- Deploying with Docker and Kubernetes
- Managing API access and authentication
Scaling and Monitoring AI Deployments
- Load balancing strategies for AI services
- Monitoring model drift and performance degradation
- Implementing auto-scaling for AI applications
Ensuring Security and Compliance in AI Deployments
- Managing data privacy in AI workflows
- Compliance with enterprise AI regulations
- Best practices for secure AI deployments
Future Trends and AI Optimization Strategies
- Advancements in AI model optimization techniques
- Emerging trends in MLOps and AI infrastructure
- Building an AI deployment roadmap
Summary and Next Steps
Requirements
- Experience with AI model deployment and cloud infrastructure
- Proficiency in a programming language (eg, Python, Java, C++)
- Understanding of MLOps and model performance optimization
Audience
- AI engineers optimizing and deploying DeepSeek models
- Data scientists working on AI performance tuning
- Machine learning specialists managing cloud-based AI systems
14 Hours
Testimonials (1)
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.
