The video discusses cloud-based readily available tools for MLOps that are GUI-based and require minimal or no code.
The advantages of using these tools include easier monitoring and tracking of development progress, faster development time, and potential cost savings compared to hiring a team of ML engineers.
Disadvantages of using these tools include high cost, limited customization options, and potential resource-intensive requirements.
The video mentions various tools and technologies related to MLOps, such as Docker, Kubernetes, MLflow, TFX, Apache Beam, and Apache Airflow.
The deployment process for any application, including ML applications, involves steps such as version control, unit testing, integration testing, acceptance testing, and deployment to test and production environments.

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