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AI Workflow: AI in Production
  • Level Expert
  • المدة 17 ساعات hours
  • الطبع بواسطة IBM
  • Offered by

عن

This is the sixth course in the IBM AI Enterprise Workflow Certification specialization.   You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones.     This course focuses on models in production at a hypothetical streaming media company.  There is an introduction to IBM Watson Machine Learning.  You will build your own API in a Docker container and learn how to manage containers with Kubernetes.  The course also introduces  several other tools in the IBM ecosystem designed to help deploy or maintain models in production.  The AI workflow is not a linear process so there is some time dedicated to the most important feedback loops in order to promote efficient iteration on the overall workflow.   By the end of this course you will be able to: 1.  Use Docker to deploy a flask application 2.  Deploy a simple UI to integrate the ML model, Watson NLU, and Watson Visual Recognition 3.  Discuss basic Kubernetes terminology 4.  Deploy a scalable web application on Kubernetes  5.  Discuss the different feedback loops in AI workflow 6.  Discuss the use of unit testing in the context of model production 7.  Use IBM Watson OpenScale to assess bias and performance of production machine learning models. Who should take this course? This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses.   What skills should you have? It is assumed that you have completed Courses 1 through 5 of the IBM AI Enterprise Workflow specialization and you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process.

الوحدات

Feedback loops and unit testing

    • 2 Videos
    • 6 Readings
    • 1 Assignment
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1 Assignment

  • Check for Understanding

2 Videos

  • Feedback Loops and Unit Testing
  • Feedback Loops and Unit Tests

6 Readings

  • Feedback Loops and Unit Tests: Through the Eyes of Our Working Example
  • Feedback Loops
  • Unit tests
  • Unit Testing in Python
  • Test-Driven Development (TDD)
  • CI/CD

Performance Monitoring and Business Metrics

    • 2 Videos
    • 7 Readings
    • 1 Assignment
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1 Assignment

  • Check for Understanding

2 Videos

  • Performance Monitoring and Business Metrics
  • Performance Drift

7 Readings

  • Performance Monitoring: Through the Eyes of Our Working Example
  • Logging
  • Minimal Requirements for Log Files
  • Logging in Python (Hands-On)
  • Model Performance Drift
  • Performance Drift Notebook Review
  • Security and Machine Learning Models

CASE STUDY: Performance Monitoring

    • 1 Videos
    • 2 Readings
    • 1 Assignment
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1 Assignment

  • Check for Understanding

1 Videos

  • Performance Monitoring Case Study

2 Readings

  • Performance Monitoring Case Study: Through the Eyes of Our Working Example
  • Getting Started (Hands-On)

End of module review & evaluation

    • 1 Readings
    • 1 Assignment
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1 Assignment

  • End of Module Quiz

1 Readings

  • Summary/Review

TUTORIAL: Watson Openscale

    • 1 Videos
    • 2 Readings
    • 1 Assignment
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1 Assignment

  • Check for Understanding

1 Videos

  • Operationalize Trusted AI with IBM Watson OpenScale

2 Readings

  • Watson OpenScale: Through the eyes of our Working Example
  • Getting started (hands-on)

TUTORIAL: Kubernetes

    • 2 Videos
    • 3 Readings
    • 1 Assignment
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1 Assignment

  • Check for Understanding

2 Videos

  • Kubernetes Explained
  • Kubernetes vs. Docker: It's Not an Either/Or Question

3 Readings

  • Kubernetes Explained: Through the Eyes of Our Working Example
  • Introduction to Kubernetes
  • Getting Started (Hands-On)

End of module review & evaluation

    • 1 Readings
    • 1 Assignment
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1 Assignment

  • End of Module Quiz

1 Readings

  • Summary/Review

Preparing for the Capstone (A full specialization review)

    • 7 Readings
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7 Readings

  • Capstone: Through the Eyes of Our Working Example
  • What is in the Capstone and Associated Review?
  • Review of Course 1: Business Priorities and Data Ingestion
  • Review of Course 2: Data Analysis and Hypothesis Testing
  • Review of Course 3: Feature Engineering and Bias Detection
  • Review of Course 4: Machine Learning, Visual Recognition, and NLP
  • Review of Course 5: Enterprise Model Deployment

Capstone Part 1: Data investigation

    • 3 Readings
    • 1 Assignment
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1 Assignment

  • Capstone - Part 1 Quiz

3 Readings

  • About the Data
  • Capstone Assignment 1: Through the Eyes of Our Working Example
  • Capstone Part 1: Getting Started (Hands-On)

Capstone Part 2: Model building and selection

    • 2 Readings
    • 1 Assignment
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1 Assignment

  • Capstone - Part 2 Quiz

2 Readings

  • Capstone Assignment 2: Through the Eyes of Our Working Example
  • Capstone Part 2: Getting Started (Hands-On)

Capstone Part 3: Model production

    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Capstone - Part 3 Quiz

1 Readings

  • Capstone Part 3: Getting Started (Hands-On)

End of course project submission, review & evaluation

    • 1 Readings
    • 1 PeerReview
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1 Peer Review

  • Capstone Project Peer Review

1 Readings

  • Solution Files

Auto Summary

"AI Workflow: AI in Production" is an advanced course within the IBM AI Enterprise Workflow Certification specialization, designed for seasoned data science practitioners. Led by Coursera, the course delves into deploying AI models in production, utilizing tools like IBM Watson Machine Learning, Docker, and Kubernetes. Learners will gain hands-on experience building APIs, managing containers, and deploying scalable web applications. The course spans 1020 minutes and requires prior completion of the first five courses in the series. This expert-level training is ideal for those with robust machine learning and Python skills, aiming to master AI deployment in large enterprises.

Mark J Grover
Instructors

Mark J Grover

Ray Lopez, Ph.D.
Instructors

Ray Lopez, Ph.D.