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.
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.
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.
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.
