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    Production Machine Learning Systems

    BY
    Google via Coursera

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    Lavel

    Expert

    Mode

    Online

    Quick Facts

    particular details
    Medium of instructions English
    Mode of learning Self study
    Mode of Delivery Video and Text Based

    Course and certificate fees

    certificate availability

    Yes

    certificate providing authority

    Coursera

    The syllabus

    Module 1: Introduction to Advanced Machine Learning on Google Cloud

    Videos
    • Specialization: Advanced Machine Learning on Google Cloud
    • Welcome
    Readings
    • How to download course resources
    • How to send feedback

    Module 2: Architecting Production ML Systems

    Videos
    • Architecting ML systems
    • Data extraction, analysis, and preparation
    • Model training, evaluation, and validation
    • Trained model, prediction service, and performance monitoring
    • Training design decisions
    • Serving design decisions
    • Designing from scratch
    • Using Vertex AI
    • Lab Introduction: Structured data prediction
    • Getting Started with Google Cloud and Qwiklabs
    Reading
    • Architecting production ML systems
    Practice Exercise
    • Architecting production ML systems

    Module 3: Designing Adaptable ML Systems

    Videos
    • Introduction
    • Adapting to data
    • Changing distributions
    • Lab: Adapting to data
    • Right and wrong decisions
    • System failure
    • Concept drift
    • Actions to mitigate concept drift
    • TensorFlow data validation
    • Components of TensorFlow data validation
    • Lab Introduction: Introduction to TensorFlow data validation
    • Lab Introduction: Advanced visualizations with TensorFlow data validation
    • Mitigating training-serving skew through design
    • Lab Introduction: Serving ML predictions in batch and real-tIme
    • Lab Debrief: Serving ML predictions in batch and real-time
    • Diagnosing a production model
    Reading
    • Designing adaptable ML systems
    Practice Exercise
    • Designing adaptable ML systems

    Module 4: Designing High-Performance ML Systems

    Videos
    • Introduction
    • Training
    • Predictions
    • Why distributed training is needed
    • Distributed training architectures
    • TensorFlow distributed training strategies
    • Mirrored strategy
    • Multi-worker mirrored strategy
    • TPU strategy
    • Parameter server strategy
    • Lab Introduction: Distributed training with Keras
    • Lab Introduction: Distributed training using GPUs on Google Cloud’s AI Platform (Multi-Worker)
    • Training on large datasets with tf.data API
    • Lab introduction: TPU-speed data pipelines
    • Inference
    Reading
    • Designing high-performance ML systems
    Practice Exercise
    • Designing high-performance ML systems

    Module 5: Building Hybrid ML Systems

    Videos
    • Introduction
    • Machine Learning on Hybrid Cloud
    • KubeFlow
    • Lab Introduction: Kubeflow Pipelines with AI Platform
    • Embedded models
    • TensorFlow Lite
    • Optimizing TensorFlow for mobile
    • Summary
    Reading
    • Hybrid ML systems
    Practice Exercise
    • Hybrid ML systems

    Module 6: Summary

    Video
    • Course summary
    Readings
    • Production Machine Learning systems - readings
    • All quiz questions and answers

    Instructors

    Google Cloud Training
    Instructor
    Google Cloud

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