Efficient MLOps for Continuous Learning: A Guide with TFX, Sagemaker, and Kubernetes

Embark on an enlightening odyssey through the realms of MLOps, DevOps, and practical Machine Learning applications, as this book, "Continuous Machine Learning with Kubeflow," unfolds. Key aspects include:

• A comprehensive guide to Kubernetes components, accompanied by illustrative examples, to deepen your understanding.
• Uncover the secrets of training and deploying ML pipelines using Docker and Kubernetes, in one convenient package.
• Engage with a plethora of MLOps projects, leveraging proven frameworks and exploring advanced deep learning concepts.

In this book, you'll discover how Kubernetes and the Kubeflow architecture facilitate modern AI/ML deployments, detailing TensorFlow's integration with Kubernetes for training and serving. It walks you through the entire process, from project initialization to completion, showcasing how to leverage Kubeflow components, deploy them in GCP, and deliver real-time predictions in production.

Throughout the journey, we delve into the power of KFserving, demonstrating serving techniques, constructing a computer vision-based UI with Streamlit, and ultimately deploying it across Google Cloud Platform, Kubernetes, and Heroku. Further exploration focuses on Explainable AI, using a What-if tool to assess fairness and bias.

Backed by practical scenarios, you'll learn to operationalize ML models, encompassing both training and deployment. Upon completion, you'll possess the skills to create ML projects in the cloud with Kubeflow and cutting-edge technologies. Moreover, you'll strengthen your grasp of DevOps and MLOps, equipping you for diverse career opportunities within companies.

What you'll acquire: - Grasp Kubernetes architecture and orchestration fundamentals. - Harness Docker and Google Cloud Platform to containerize and deploy from scratch. - Develop Kubeflow Orchestrator pipelines for TensorFlow models step-by-step. - Deploy AWS SageMaker pipelines, seamlessly transitioning from training to production. - Construct an NLP application's TFX pipeline using Tensorboard and TFMA.

Target Audience: This book caters to MLOps professionals, DevOps engineers, Machine Learning Engineers, and Data Scientists who aim to manage large-scale ML pipelines with Kubernetes. A solid foundation in machine learning is essential, while familiarity with Kubernetes is beneficial but not mandatory.

Book Outline: 1. Introduction to Kubeflow & Kubernetes Cloud Infrastructure 2. GCP-Powered Kubeflow Pipeline Development 3. Designing Computer Vision Models with Kubeflow 4. Crafting TFX Pipelines 5. Exploring Model Explainability and Interpretability 6. Weights & Biases Pipeline Development Insights 7. Harnessing AWS SageMaker for Applied ML 8. Streamlit & Heroku: Web App Development for ML Applications

Embrace the convergence of MLOps, DevOps, and Machine Learning in this comprehensive guide, ready to take your skills to new heights.

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