Introduction to AI Engineering

About the course

To create an impact with data analytics it is essential to have a good data technology foundation. In this one-day badge, you will be introduced to the data analytics platform framework, the typical challenges that occur in such a platform, define requirements for operationalizing models and design applications for your output.

The content of this course covers all the data and technology elements needed to generate impact from AI, along with an introduction to data pipelines and orchestration and model operationalization.

 

Why this is for you

The key challenge for scalable impact from AI is building AI production systems. This challenge has several root causes but two very important (if not the most important) are a lack of knowledge and experience on automation of dataflows and bringing predictive models into production.

One of the solutions to overcome this challenges is to work in cross-functional teams with a mix of skills and perspectives. Having business and operational people work side by side with analytics and engineering experts will ensure that initiatives address broad organizational priorities, not just isolated business issues.

 

For whom

This training is designed for Business Professionals who have completed the AI Foundation course and are keen to learn more about the data and technology side of creating true impact from end-2-end solutions. Participants must have prior knowledge of Machine learning (following the Introduction to Machine Learning course) and Data Management. To have a complete overview before starting this cours it is a requirement to complete the Use Case Identification & Design course.

 

What you’ll learn

1. Explain AI Engineering challenges to create impact
2. Design application of AI model output
3. Challenge data pipeline designs
4. Define requirements for model operationalization

Learning Goals

• Explain AI Engineering challenges to create impact – Give arguments to explain the AI Engineering challenges in creating impact with E2E AI solutions
• Design application of AI model output – Develop high-over design for process and presentation of AI model output to (business) consumers
• Challenge data pipeline designs – Challenge designs for data pipelines by applying principles for Data Analytics Platforms running AI successfully
• Define requirements for model operationalization – Define requirements for processes, data and software used to operationalize models

 

Theory and practical use

All trainings in the GAIn portfolio combine high-quality standardized training material with theory sessions from experts and hands-on experience where you directly apply the material to real-life cases. Each training is developed by top of the field practitioners which means they are full of industry examples along with practical challenges and know-how, fueling the interactive discussions during training. We believe this multi-level approach creates the ideal learning environment for participants to thrive.