Udemy - MLOps Certification- Pipeline basics to MLOps Toolbox

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[ DevCourseWeb.com ] Udemy - MLOps Certification- Pipeline basics to MLOps Toolbox
  • Get Bonus Downloads Here.url (0.2 KB)
  • ~Get Your Files Here ! 1. Rationale for MLOps
    • 1. Introduction.mp4 (210.4 MB)
    • 1. Introduction.srt (11.6 KB)
    10. MLOps- Kubernetes & HELM Package Manager
    • 1. MLOps HELM as Package Manager.mp4 (91.7 MB)
    • 1. MLOps HELM as Package Manager.srt (3.9 KB)
    2. MLOps Vs DevOps- How to Implement MLOps
    • 1. Why MLOps are necessary.mp4 (143.8 MB)
    • 1. Why MLOps are necessary.srt (6.9 KB)
    3. MLOps Continuous X and Versioning
    • 1. Continuous X and Versioning in MLOps.mp4 (123.9 MB)
    • 1. Continuous X and Versioning in MLOps.srt (5.8 KB)
    4. Levels & Components of MLOps
    • 1. Three Levels of MLOps.mp4 (152.3 MB)
    • 1. Three Levels of MLOps.srt (7.2 KB)
    5. MLOps- ML Models and Code Pipelines
    • 1. Code & ML Pipelines in MLOps.mp4 (179.4 MB)
    • 1. Code & ML Pipelines in MLOps.srt (8.8 KB)
    6. MLOps Toolbox
    • 1. MLOps Toolbox.mp4 (60.8 MB)
    • 1. MLOps Toolbox.srt (11.0 KB)
    8. TensorFlow X- ML Production Pipelines
    • 1. TensorFlow X- ML Production Pipelines.mp4 (48.3 MB)
    • 1. TensorFlow X- ML Production Pipelines.srt (7.9 KB)
    9. MLFlow- MLOps Lifecycle Platform
    • 1. ML Flow- A Platform for ML Life Cycle.mp4 (22.2 MB)
    • 1. ML Flow- A Platform for ML Life Cycle.srt (4.3 KB)
    • Bonus Resources.txt (0.4 KB)

Description

MLOps Certification- Pipeline basics to MLOps Toolbox



https://DevCourseWeb.com

Instructors: Junaid Zafar
15 sections • 15 lectures • 1h 2m
Video: MP4 1280x720 44 KHz | English + Sub
Published 3/2022 | Size: 1 GB

MLOps: Components & Levels of MLOps, CI/CD practices in the context of ML systems, reliable training workflows for MLOps

What you'll learn
MLOps- What are MLOps (Machine Learning Opeartions)?
MLOps: Components including Continuous X & Versioning
MLOps: Life Cycle Process ( End to End Learning Flow)
MLOps: Model Testing & Model Packaging in PMML and ONNX
MLOps: Workflow Decomposition & Production Environment
MLOps: Pre- Computing Serving Patterns
MLOps: Data, Machine Learning and Code Pipelines
MLOps: Offline & Live Evaluation & Monitoring

Requirements
No prior experience is needed. You will learn everything you need to know.
Description
This course introduces participants to MLOps concepts and best practices for deploying, evaluating, monitoring and operating production ML systems on both cloud and Edge. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Machine Learning Engineering professionals use tools for continuous improvement and evaluation of deployed models. They work with Data Scientists, who develop models, to enable velocity and rigor in deploying the best performing models.



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