MLOps Training in Hyderabad | Machine Learning Operations Training

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MLOps

• Tools and Platforms A Comprehensive Overview

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Introduction to MLOps

• Definition MLOps combines Machine Learning (ML)
  with IT Operations (Ops)
• Objective Streamline the end-to-end ML lifecycle
• Importance Enhances collaboration, efficiency,
  and scalability in ML projects

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Key Components of MLOps

• Data Management Tools for data storage,
  processing, and versioning
• Model Development IDEs and frameworks for model
  training and experimentation
• Model Deployment Platforms for deploying models
  to production environments

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Data Management Tools

• Data Storage Apache Hadoop, AWS S3
• Data Processing Apache Spark, Databricks
• Data Versioning DVC (Data Version Control),
  Pachyderm

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Model Development Tools

• IDEs Jupyter Notebook, PyCharm
• Frameworks TensorFlow, PyTorch
• Experimentation MLflow, Weights Biases

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Model Deployment Tools

• Deployment Platforms Kubernetes, Docker
• Serving Models TensorFlow Serving, KFServing
• Monitoring Prometheus, Grafana

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Integrated MLOps Platforms

• End-to-End Platforms Databricks, Azure ML
• Cloud Platforms AWS SageMaker, Google AI
  Platform
• Open-Source Solutions Kubeflow, MLflow

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Conclusion

• Summary Recap of the importance and variety of
  MLOps tools
• Future Trends Growth in automation and AI-driven
  MLOps

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CONTAC

• Machine Learning Operations Training
• Address- Flat no 205, 2nd Floor,
• Nilgiri Block, Aditya Enclave,
• Ameerpet, Hyderabad-1
• Ph. No 91-9989971070 
• Visit www.visualpath.in
• E-Mail online_at_visualpath.in

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THANK YOU
Visit www.visualpath.in