Course Outline
Introduction to Model Optimization and Deployment
- Overview of DeepSeek models and common deployment challenges.
- Understanding model efficiency: balancing speed versus accuracy.
- Key performance metrics for AI models.
Optimizing DeepSeek Models for Performance
- Techniques for reducing inference latency.
- Strategies for model quantization and pruning.
- Utilizing optimized libraries for DeepSeek models.
Implementing MLOps for DeepSeek Models
- Version control and model tracking.
- Automating model retraining and deployment processes.
- Establishing CI/CD pipelines for AI applications.
Deploying DeepSeek Models in Cloud and On-Premise Environments
- Selecting the appropriate infrastructure for deployment.
- Deploying solutions using Docker and Kubernetes.
- Managing API access and authentication protocols.
Scaling and Monitoring AI Deployments
- Load balancing strategies for AI services.
- Monitoring for model drift and performance degradation.
- Implementing auto-scaling mechanisms for AI applications.
Ensuring Security and Compliance in AI Deployments
- Managing data privacy within AI workflows.
- Ensuring 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.
- Developing an AI deployment roadmap.
Summary and Next Steps
Requirements
- Experience with AI model deployment and cloud infrastructure.
- Proficiency in a programming language (e.g., Python, Java, C++).
- Understanding of MLOps principles and model performance optimization.
Audience
- AI engineers focused on optimizing and deploying DeepSeek models.
- Data scientists engaged in AI performance tuning.
- Machine learning specialists responsible for managing cloud-based AI systems.
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.
