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Nov 22 nd |
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This course offers a comprehensive introduction to MLOps, focusing on the effective deployment and management of machine learning models in production. It covers the entire ML lifecycle, including data preparation, model development, deployment, monitoring, and retraining. Students get hands-on experience with essential MLOps tools and frameworks such as LangChain, CrewAI, AutoGen, and retrieval-augmented generation (RAG), alongside core technologies like MLflow, Kubernetes, Docker, and cloud platforms, enabling them to build scalable, automated workflows that bridge data science and operations.
Key features:
This course is well-suited for programmers, developers, data analysts, statisticians, data scientists, and software engineers.
To excel in MLOps, you need Python skills, ML algorithm knowledge, experience with Docker, Kubernetes, and cloud platforms. A laptop with at least 8GB RAM, Intel i3+ processor, and stable internet is also required.
Practicals for this course will be implemented using various tools and detailed step-by-step installation guidesfor these tools are available on the LMS. In case you come across any doubt, the 24*7 support teams will promptly assist you.
A professional training course that teaches end-to-end MLOps such as development, deployment, monitoring, and operations, using key tools and techniques in line with industry standards.
Yes, It includes live classes, hands-on labs, quizzes, and end-to-end real-world projects.
This training course covers AWS SageMaker, Azure ML, Google Cloud Platform, MLflow, Docker, Kubernetes, Jenkins, and more with AI tools.
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