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Scale AI in your organization Microsoft Quiz Answers
Get Scale AI in your organization Microsoft Quiz Answers
In this learning path, you’ll get a high-level overview of how to manage an AI organization, team, department, or center of excellence. You’ll learn about fueling innovation at all levels, evaluating and prioritizing AI investments, establishing technical processes for AI, and distributing AI-related responsibilities across the organization.
Prerequisites:
None
Module 1: Implement AI in your organization
Everyone in an organization has a role to play in AI transformation. At the highest levels, leaders need to prioritize AI use cases strategically and create an environment where innovative ideas can flourish. Then, those ideas come to life and succeed long-term thanks to collaboration across lines of business and technical teams. This module provides insight into managing AI-related processes and responsibilities in an organization.
Learning objectives:
In this module, you will:
- Apply best practices for fueling innovation in your organization.
- Evaluate and prioritize AI investments.
- Establish AI-related roles and responsibilities.
Prerequisites:
None
This module is part of these learning paths:
Quiz 1: Knowledge check
Q1. Why is it beneficial for people from technical teams and lines of business to collaborate on AI initiatives?
- The technical teams need to make sure the solution has the latest and greatest AI capabilities.
- All relevant teams need to be involved in design, implementation, and ongoing maintenance to make sure the solution functions properly and achieves its business objectives.
- Line-of-business leaders need to make sure the solution costs as little as possible.
- Line-of-business leaders need to make sure the solution doesn’t change any existing work processes.
Q2. What is a crucial step to connecting R&D with real business challenges?
- Make sure employees on the ground can share ideas related to their jobs.
- Members of C-suite should ensure research efforts are geared towards relevant needs for the company.
- Hire data scientists to drive innovation projects.
- Publish academic papers to establish your company as an innovative business.
Q3. Why is it helpful to prioritize Horizon 1 initiatives over Horizon 2 or Horizon 3 when starting out with AI?
- Horizon 1 initiatives can create new business models and revenue streams.
- New customer needs may be generated during Horizon 1 initiatives, which can fuel business growth during H2 and H3.
- H1 initiatives can be carried out quickly, driving immediate value to the business.
- Horizon 1 initiatives can grow capabilities and get buy-in with simpler technologies before moving on to more complex projects.
Module 2: Start the machine learning lifecycle with MLOps
In this module, we discuss best practices for creating and managing machine learning (ML) models using MLOps processes. MLOps is the practice of collaboration between data scientists, ML engineers, software developers, and other IT teams to manage the end-to-end ML lifecycle.
Learning objectives:
In this module, you will learn to:
- Identify the different steps of the ML lifecycle.
- Describe how to create and manage machine learning models using MLOps processes.
- Articulate processes to monitor models and respond to incidents.
Prerequisites:
None
This module is part of these learning paths:
Quiz 1: Knowledge check
Q1. What is a benefit of a machine learning pipeline?
- Machine learning pipelines create a dependent workflow process to ensure each stage of a workflow is tested and completed before moving on to the next stage.
- Machine learning pipelines serve as an audit trail for a model’s history and make it possible to automatically trigger workflows after certain events.
- Machine learning pipelines require data scientists to begin their work from the beginning to make changes, which improves their accuracy and performance.
- Having independent steps saved to a pipeline allows multiple data scientists to work on the same pipeline concurrently.
Q2. What is the recommended approach for monitoring machine learning models?
- Machine learning models should be monitored by automatic analysis with established performance thresholds that trigger alerts.
- Machine learning models should be monitored by manual spot check analysis with someone testing the model and applying their own analysis.
- A combination of automatic monitoring and manual spot checks.
- Machine learning models should be monitored by continuous integration and continuous delivery (CI/CD).
Q3. What is a model registry?
- A central place to save every version of every model.
- A method of testing and validating model performance.
- A shared environment where individuals can work on models at the same time.
- A requirement for deploying ML models on the cloud.
Conclusion:
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This Problem is intended for audiences of all experiences who are interested in learning about Data Science in a business context; there are no prerequisites.
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