Data Analysis Workshop with SAS
Machine learning is a core component of many AI applications. Especially in the early phases it is a matter of creatively and quickly testing evaluation ideas, agilely exploring the data and developing prototypes. However, the later productive use should not be ignored. In this workshop we will show you how this works. The workshop is divided into the following blocks:
- With Self-Service Analytics, you can quickly explore data and recognize correlations. Uncomplicated building and refining machine learning models, playing through what-if scenarios for the consequences of model applications.
- Using methods systematically for machine learning. Supervised, Unsupervised, Semi-Supervised Learning, Modeling Pipelines, Feature Generation, Model Tuning/ Hyperparameter Tuning, Model Comparison and Interpretation.
- A look into the analytical ecosystem. Openness, scalability, elasticity.
- View on model management and operationalization. How do I bring my model assets into production and secure their value contribution over time? Model Management and Governance, Scoring Tests, Performance Monitoring and Re-Training