Level: Intermediate to Advanced
Understanding of data platform concepts and uses
Some familiarity with the concepts of SQL
In this course, you will learn how to select a data platform that meets the unique requirements of your organization for artificial intelligence (AI), machine learning (ML) and advanced analytics.
Recent advances in AI and advanced analytics have opened vast new opportunities for companies to benefit from data. However, there are challenges on the path to those great business advances. For example, training and running AI/ML models, particularly at large scale, can be extraordinarily expensive. Your current data platform for BI and reporting may not be suitable, depending on the scale, complexity, and other factors of your AI/ML-related initiatives.
The key to managing the costs and the time to value—for classic and generative AI models as well as other forms of advanced analytics—is the data platform. Some data platforms will perform well at large scale and others will not. Further, customer needs will vary in complex ways. So, a data platform that supports the AI operations of company A may not work well for company B.
This half-day course will cover the concepts, architecture, and key requirements for the modern data platform that supports AI, machine learning, and advanced analytics. The capabilities of the most widely used data platforms in this area will be reviewed. A process for platform evaluation, testing, and selection will be discussed.
You Will Learn
- Examples of key problems in AI, ML, and advanced analytics
- The key issues for the data platform supporting AI, ML, and advanced analytics
- Architectural features and their effects at scale
- Key data platform requirements for AI, ML, and advanced analytics
- Brief review of the most widely used data platforms and their relevant features
- Approaches for data platform evaluation and testing
- Data strategists
- Data architects
- AI stakeholders