MLOps Full Courser [2025] - 12 hour | MLOps for Beginners | What is MLOps? | Edureka Live
๐ฅPGP in Generative AI and ML in collaboration with Illinois Tech: https://www.edureka.co/executive-programs/pgp-generative-ai-machine-learning-certification-training๐ฅGenerative AI Course: Masters Program: https://www.edureka.co/masters-program/generative-ai-prompt-engineering-training: https://www.edureka.co/executive-programs/pgp-generative-ai-machine-learning-certification-training
๐ฅ MLOps Certification Course: https://www.edureka.co/mlops-certification-training-course
In this comprehensive course on *What is MLOps*, participants will gain a strong foundational understanding of the principles and practices that bridge the gap between machine learning development and real-world operations. The course begins with an in-depth overview of MLOps, emphasizing its critical role in streamlining collaboration between data science and IT operations to enable the seamless deployment of machine learning models at scale. From there, learners will explore the **Key Components of MLOps**, uncovering the essential building blocks such as version control, CI/CD pipelines, model tracking, and reproducibility. The curriculum then delves into the **MLOps Lifecycle**, walking through each stageโfrom data preparation and model training to deployment, monitoring, and continuous improvementโillustrating how to manage ML models effectively in production environments. To empower practical implementation, the course introduces popular **Tools & Frameworks** like MLflow, Kubeflow, TFX, and others that are transforming the automation and scalability of ML workflows. Finally, the course addresses the **Challenges and Real-World Hurdles** in adopting MLOps, offering actionable strategies to overcome issues such as data drift, model degradation, and infrastructure bottlenecks. Designed with beginners in mind, this full-course roadmap provides not just theoretical insights, but also the operational know-how needed to successfully integrate and scale machine learning in enterprise environments.
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