Deploy Trained Models
Technology provides a competitive edge to organizations which makes the need to understand machine learning even more important. This course will help you better understand how to deploy trained machine learning models to a production environment.
What you'll learn
The ability to deploy trained machine learning models that are created using data and inputs from the organization is becoming increasingly relevant and giving companies an edge in their respective industry. In this course, Deploy Trained Models, you’ll gain the ability to better understand machine learning models, particularly, how to deploy trained machine learning models. First, you’ll be introduced to the challenges and considerations of deploying a trained model which involves looking into the transition from model training to production and determining the strategies for addressing deployment-related bottlenecks. Next, you’ll learn about the different model service techniques to make the model more accessible in production. Finally, you’ll be exposed to the concept of continuous deployment and rollbacks, particularly the strategies involved in rolling out new model versions while maintaining reliability. When you’re finished with this course, you’ll have the skills and knowledge needed to deploy trained machine learning models which would consequently help the organization in effectively and efficiently implementing initiatives and projects that utilize machine learning.
Table of contents
- Trained Model Deployment Overview 1m
- Importance of Model Versioning and Reproducibility 2m
- Trained Model Deployment Challenges and Considerations 2m
- Strategies for Addressing Deployment Bottlenecks 2m
- Model Serving Techniques 2m
- Use of REST APIs for Serving Models 2m
- Microservices Architecture and Its Role in Model Deployment 2m
- Options for Serverless Deployment 1m
- Use Case: Considerations and Serving Techniques for Trained Model Deployments 1m
- Scaling Strategies for Model Deployments 1m
- Horizontal and Vertical Scaling Strategies 1m
- Distributed Systems for Serving Models 1m
- Load Balancing in Distributed Systems 1m
- Importance of Monitoring Deployed Models 1m
- Metrics for Tracking: Model Accuracy and Responsiveness 1m
- Importance of A/B Testing for Model Evaluation 1m
- Techniques for Detecting Concept and Data Drift 2m
- Anomaly Detection Methods for Identifying Model Failures 1m
- Strategies for Rolling out New Model Versions 1m
- Continuous Deployment Significance for Model Updates 1m
- Techniques for Rollback and Recovery 2m
- Use Case: Scaling, Monitoring, and Continuous Deployment of Trained Models 1m
About the author
She is the Co-founder as well as Strategy Lead of AtoANI (sustainable agriculture) and AtoANI BioPack (biodegradable packaging). She is passionate about Social Innovation and using it to contribute to the UN Sustainable Development Goals. With a Chemical Engineering and MBA Degree, her corporate experience in multinational companies involves working on various topics in the areas of Strategy, Digitalization, Innovation, Finance, Material Management, Quality Assurance, Program and Project Managem... more