MLOps leverages a variety of tools and technologies to streamline the machine learning lifecycle. Version control systems like Git facilitate collaborative code development, while containerization tools such as Docker and orchestration platforms like Kubernetes ensure consistent deployment across diverse environments. CI/CD pipelines, exemplified by Jenkins or GitLab CI, automate the integration and delivery processes. Cloud platforms like AWS, Azure, or Google Cloud provide scalable infrastructure, and monitoring tools like Prometheus aid in tracking model performance. MLOps engineers often use platforms like MLflow for experiment tracking and model management. If you are aspiring to get into this profile, then check out this Shyam Prakash Dubey’s video. He was successfully able to switch his career from Data Engineer to MLOps Engineer just after completing Advanced Certification in Data Science and AI from Intellipaat.
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