MLOps engineers play a crucial role in the machine learning lifecycle, bridging development and operations. Their responsibilities include designing and implementing robust ML pipelines, ensuring seamless model deployment through tools like Docker and Kubernetes, and establishing effective CI/CD processes. They collaborate with data scientists to integrate models into production, monitor performance, and troubleshoot issues. Additionally, MLOps engineers manage version control, automate testing, and optimize infrastructure, ensuring efficient and scalable ML workflows while fostering collaboration between cross-functional teams. 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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