SAS Viya AI & Machine Learning Professional: Certification & Deployment

What you will learn:

Navigate and effectively utilize the SAS Viya platform and its suite of functionalities.
Grasp fundamental concepts underpinning Artificial Intelligence and Machine Learning.
Execute robust data preprocessing, including cleaning, transformation, and advanced feature creation.
Construct and implement diverse supervised learning algorithms such as linear regression, decision forests, and gradient boosting machines.
Implement unsupervised learning methods like K-means clustering, principal component analysis (PCA), and association rule mining.
Engineer sophisticated deep learning architectures, including convolutional and recurrent neural networks.
Assess and fine-tune model efficacy through a variety of advanced evaluation metrics and optimization strategies.
Operationalize and deploy predictive models into live production settings within SAS Viya.
Establish continuous monitoring systems for deployed models and proactively manage concept drift.
Address complex industry challenges with AI solutions, exemplified by churn forecasting and financial anomaly detection.

Description

Embark on an immersive journey into the dynamic world of Artificial Intelligence with this advanced SAS Viya program. This robust, career-centric training is meticulously crafted to equip you with unparalleled expertise in AI and Machine Learning within the cutting-edge SAS Viya environment. Whether your goal is to achieve industry certification or to architect impactful, real-world AI applications, this course offers a holistic pathway from foundational principles to sophisticated model operationalization.

Structured logically and progressively, our curriculum initiates with essential topics like the bedrock of AI concepts, fundamental SAS coding, and configuring your development environment. Subsequently, the program delves deep into crucial data science processes, encompassing meticulous data cleansing, inventive feature engineering, insightful exploratory analysis, and compelling data visualization. Participants will acquire practical skills in deploying both supervised and unsupervised learning methodologies, including various regression models, intricate decision trees, powerful clustering algorithms, and effective dimensionality reduction techniques.

Advancing further, the instruction transitions into the realm of deep learning, leveraging specialized SAS utilities. You will explore neural network architectures, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and the powerful concept of transfer learning. Crucially, the course extends beyond model building to cover the deployment of these sophisticated models into live production systems, continuous performance monitoring, and strategic retraining protocols—competencies indispensable for today's AI practitioner.

A distinguishing feature of this educational experience is its integration of authentic, real-world business challenges. Engage with practical case studies such as predicting customer churn in telecommunications and detecting financial irregularities, enabling you to translate theoretical knowledge into tangible solutions. These simulated industry projects offer invaluable insights into the complete lifecycle of developing and deploying AI-driven solutions.

Furthermore, this program is meticulously designed to align with official professional certification benchmarks, ensuring participants are thoroughly prepared and confident for obtaining their SAS AI & Machine Learning credentials.

Proudly presented by Veloxa Labs, we are dedicated to fostering innovative, career-enhancing learning experiences. Our core mission is to empower the next generation of professionals with essential, forward-looking AI and data science proficiencies through practical, premium-quality, and industry-relevant educational initiatives.

Curriculum

Foundation of AI/ML & SAS Viya Essentials

This introductory section lays the groundwork by exploring the core principles of Artificial Intelligence and Machine Learning. Learners will gain a deep understanding of AI concepts, its history, and various applications. Crucially, the section introduces the powerful SAS Viya ecosystem, guiding participants through environment setup, navigation, and fundamental SAS programming syntax essential for data manipulation and analysis within the platform. Key topics include data types, basic procedures, and an overview of Viya's interactive interfaces.

Data Preparation, Feature Engineering & Exploratory Analytics

This module focuses on the critical initial phases of any AI/ML project. Participants will master techniques for data cleaning, handling missing values, and transforming raw data into a usable format. Emphasis is placed on advanced feature engineering strategies to create impactful variables for model building. The section also covers comprehensive exploratory data analysis (EDA) using SAS Viya tools, including statistical summaries, hypothesis testing, and effective data visualization methods to uncover patterns and insights.

Supervised Learning Models & Predictive Analytics

Dive into the world of predictive modeling with supervised learning. This section thoroughly covers a range of algorithms, starting with linear and logistic regression for predicting continuous and categorical outcomes. Advanced tree-based models like decision trees, random forests, and gradient boosting machines (GBMs) are explored in detail, including their implementation and interpretation within SAS Viya. Learners will develop the skills to select appropriate models, tune hyperparameters, and understand model assumptions.

Unsupervised Learning & Advanced Data Mining

Expand your analytical toolkit with unsupervised learning techniques that discover hidden structures in data without labeled outcomes. This module covers various clustering algorithms such as K-means and hierarchical clustering for segmenting data. Principal Component Analysis (PCA) is introduced for dimensionality reduction, helping to simplify complex datasets while retaining critical information. Additionally, participants will explore association rule mining to identify relationships and patterns within large datasets, all implemented using SAS Viya.

Deep Learning with SAS Viya

This advanced section delves into the fascinating domain of deep learning. Learners will be introduced to the architecture and functioning of artificial neural networks (ANNs). The course then progresses to specialized networks, including Convolutional Neural Networks (CNNs) for image and spatial data processing, and Recurrent Neural Networks (RNNs) for sequential data like time series and natural language. Concepts such as transfer learning, regularization, and optimization strategies for deep networks are covered, with practical implementation examples in SAS Viya.

Model Deployment, Monitoring & Real-World Case Studies

The culmination of the course focuses on operationalizing AI/ML solutions. This module teaches how to seamlessly deploy trained models into production environments within SAS Viya, ensuring they are accessible for real-time predictions. Crucial aspects of model monitoring, performance tracking, and strategies for handling concept drift are covered to maintain model accuracy over time. The section is heavily reinforced by comprehensive real-world case studies, including an end-to-end telecom churn prediction project and a financial fraud detection scenario, offering hands-on experience in solving complex business problems.

Certification Preparation & Career Advancement

This concluding section is dedicated to preparing learners for the SAS AI & Machine Learning professional certification exams. It includes review sessions, practice questions, and strategic advice to ensure readiness and confidence for achieving official SAS credentials. Beyond certification, the module provides insights into career pathways in AI and data science, practical tips for job searching, and guidance on how to leverage your new SAS Viya skills for significant professional growth and impact in the industry.

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