MLOps Full Course [2025] - 12 hour | MLOps for Beginners | What is MLOps? | Edureka Live
๐ฅPGP in Generative AI and ML in collaboration with Illinois Tech: https://ift.tt/yjXK96w ๐ฅGenerative AI Course: Masters Program: https://ift.tt/VtRXE9j: https://ift.tt/yjXK96w ๐ฅ MLOps Certification Course: https://ift.tt/sJ0yjoX 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 stage—from data preparation and model training to deployment, monitoring, and continuous improvement—illustrating 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. 00:01:49 What is MLOps? 00:13:53 What is Machine learning? 00:30:28 Types of Machine Learning Models 00:37:41 Top Machine Learning Tools & Frameworks 00:47:37 Introduction to TensorFlow 01:11:13 Introduction to Jupyter Notebook 01:32:40 Python Flask Explained 02:05:55 What is Git? 03:00:14 GitHub Basics 03:29:47 What is Jenkins? 04:31:16 What is Docker? 05:10:45 Introduction to Kubernetes 05:21:19 Prometheus Explained 06:28:18 AWS Services 07:00:34 Azure Boards 07:15:14 Azure DevOps Pipeline 07:27:49 Azure DevOps Test Plans 07:53:20 Azure DevOps Artifacts 08:17:15 PyTest Explained 08:38:31 Azure Machine Learning 09:04:50 AWS Machine Learning 09:45:18 Google Cloud AI Platform Explained 10:01:10 Deploy Machine Learning Model using Amazon SageMaker 10:30:08 How to Become a Machine Learning Engineer? 10:39:38 Machine Learning Interview Question & Answers ๐ด ๐๐๐๐ซ๐ง ๐๐ซ๐๐ง๐๐ข๐ง๐ ๐๐๐๐ก๐ง๐จ๐ฅ๐จ๐ ๐ข๐๐ฌ ๐
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