Applied Artificial Intelligence and Machine Learning with Generative & Agentic AI

• Explain the relevance of statistics in big data and machine learning.

• Relate statistical and data science terminology.

• Generate descriptive statistics and explore data with graphs.

• Detect associations among variables.

• Perform linear regression for explanatory modeling.

• Compare explanatory modeling with predictive modeling.

• Describe the trade-off between bias and variance.

• Fit a logistic regression model for predictive modeling.

• Score new data.

• Explain the statistical foundations of machine learning.

• Run traditional SAS code on the SAS Viya platform.

• Connect to SAS Cloud Analytic Services (CAS).

• Access and use caslibs on the CAS server.

• Load SAS data sets, Parquet files, CSV files, Microsoft Excel files, and DBMS tables into CAS.

• Save distributed CAS tables back to permanent storage in various formats like SASHDAT, Parquet, and CSV.

• Modify DATA step code to execute in CAS.

• Modify SQL procedure code to execute in CAS using FedSQL.

• Use CAS-enabled procedures.

• Write CASL code to execute actions on the CAS server.

• Use the Python API in SAS Viya.

• Submit CAS actions from Python.

• Manage, alter, and prepare data on the CAS server.

• Implement and compare machine learning models on the CAS server.

• Move data between the client and server.

• Use Python syntax to wrap up CAS actions with functions and loops.

• Promote data to persist in memory.

• Apply the analytical life cycle to a business need.

• Incorporate a business-problem-solving approach in daily activities.

• Prepare and explore data for analytical model development.

• Create and select features for predictive modeling.

• Develop a series of supervised learning models (decision trees, ensembles of trees, neural networks, support vector machines).

• Evaluate and select the best model based on business needs.

• Deploy and manage analytical models under production.

• Get started building reports using SAS Visual Analytics.

• View reports using SAS Visual Analytics and other supported applications.

• Access data loaded into CAS and navigate the SAS Visual Analytics interface.

• Manage and create data items, work with multiple data sources, and sort, filter, and rank data.

• Explore data using objects.

• Create reports using objects.

• Analyze geographic data using geo maps.

• Enhance reports with display rules.

• Design interactive reports by modifying viewer capabilities and by adding prompts, actions, and links.

• Discover best practice recommendations for report design using SAS Visual Analytics.

• Define and understand deep learning.

• Build traditional, convolutional, and recurrent neural networks using deep learning techniques.

• Apply models to score new data.

• Search the hyperparameter space of a deep learning model.

• Leverage transfer learning using supervised and unsupervised methods.

• Use the point-and-click interface of Model Studio and SAS Visual Text Analytics.

• Explore collections of text documents to discover key topics.

• Interpret term maps.

• Identify key textual topics automatically in your large document collections.

• Create robust models for categorizing content according to your organization’s needs.

• Create, modify, and enable (or disable) custom concepts and test linguistic rule definitions.

• Extract individual instances of concepts from within documents.

• Create custom Boolean rules to categorize documents with respect to a categorical target variable.

• Modify automatically generated Boolean category rules.

• Extract a document-level sentiment score.

• Create modeling-ready data for use by SAS Visual Data Mining and Machine Learning

• Import and convert document files for use in SAS Visual Text Analytics.

• Explain what generative AI is and how it fits into the broader AI landscape.

• Describe several types of GenAI systems.

• Name some of the key challenges and opportunities in making a trustworthy AI system.

• Generate synthetic data with SMOTE and GANs.

• Explain how Large Language Models (LLMs) generate meaningful text.

• Classify text for LLMs using BERT.

• Improve the accuracy and relevance of LLM output using Retrieval Augmented Generation (RAG).

• Manage SAS Model Manager data sources.

• Import models into SAS Model Manager.

• Score SAS Model Manager models.

• Create SAS Model Manager performance reports.

• Schedule Model Manager jobs.

• Distinguish ModelOps from DevOps.

• Use corporate culture as a positive influence on modeling outcomes.

• Control the process of deploying models with the ModelOps approach.

• Recognize the hidden impact of model risk.

• Deploy models in the cloud using SAS Container Runtime.

• Set up administration of the SAS modeling environment.

• Create and manage decisions and their objects in SAS Intelligent Decisioning.

• Understand the foundation needed for advanced courses based on SAS Intelligent Decisioning.

• Register, publish, deploy, and monitor large language models (LLMs) and Agentic AI workflows (intelligent decision flows).

• Combine proprietary and open-source LLMs with deterministic models in decision-making workflows.

• Govern, version, and scale LLM usage in enterprise applications.

• Integrate workflows into Azure AI Assistants for execution and enhanced functionality.

• Demonstrate integration of Agentic AI workflows into enterprise applications.