ModelOps techniques are used for lifecycle management of artificial intelligence and machine learning projects. It helps in automating the entire steps involved in Data Science.ModelOps techniques are used for lifecycle management of artificial intelligence and machine learning projects. It helps in automating the entire steps involved in Data Science.
In this section we are going to learn fast growing ModelOps techniques which are used for the complex lifecycle management of models such as data processing, model training, model evaluation and model deployment. The ModelOps is going to be trending technologies in 2021 and Data Scientist must learn the techniques for better management of their artificial intelligence model. In this section we are going to learn ModelOps techniques and see how it can be used in the business environment for better ROI.
ModelOps is branch of DevOps which follows the same CI/CD steps for training and deploying the model in the production. The ModelOps is also known as machine learning operations (MLOps) and it comprises tools, technologies, and practices that enable the business in training, validating and deploying the models.
Model operations (ModelOps) are very important today it is very useful in operationalizing AI at scale and with little effort. In today's fast changing environment machine learning model used by the business much be up to date with the latest data to produce better result. The manual model training and deployment process is very hard. So, ModelOps is here to help the streamline the process and run it on demand. This helps the organizations in training, evaluating and deploying the model without the help of Data Scientist. Once the ModelOps pipeline is setup and configured by Data Scientist it can be run by domain experts easily which helps organizations in saving a lot of money and time.