The GenAI – Software Development Lifecycle is a 1-day (8-hour), hands-on training program designed to give professionals a practical and business-aligned introduction to Generative AI. Covering both technical and strategic perspectives, this program explores how GenAI is reshaping the modern software development lifecycle — from ideation and design to coding, testing, documentation, and deployment. Participants will gain real-world exposure to GenAI-powered tools and learn implementation best practices for applying AI within the development process. This course bridges the gap between understanding GenAI conceptually and knowing how to actually use it across real workflows in software teams.
The GenAI Architect Track is a 32-hour advanced training program designed for professionals aiming to lead enterprise-grade Generative AI implementations. This hands-on course covers key concepts in building scalable AI workflows using LLMs, including model context protocols (MCP), multi-agent systems, prompt engineering, and secure integrations with APIs and legacy systems. Participants will work with industry-standard tools such as LangChain, HuggingFace, and OpenAI, while exploring advanced topics like RAG, compliance, observability, and AI cost optimization. Labs and real-world scenarios reinforce concepts and practical application. Ideal for architects, ML engineers, and technical leads, this course empowers learners to design secure, efficient, and production-ready GenAI solutions within cloud, hybrid, or on-prem environments.
The GenAI Developer Track is a 40+ hour advanced training program tailored for developers and ML engineers ready to build and deploy powerful LLM-based applications. The course offers in-depth, hands-on experience with modern GenAI tools such as LangChain, HuggingFace, and OpenAI APIs, while covering core concepts like prompt engineering, agentic AI, and multi-agent systems. Learners will explore real-world use cases like RAG-based bots, dynamic chat agents, and LLM-integrated workflows using tools like Slack, Streamlit, and Gradio. With a focus on practical labs, debugging techniques, and deployment strategies, this track equips participants to confidently design, build, and optimize GenAI applications for real production environments.
The Graphical Models Training course offers a deep dive into the world of probabilistic graphical models, including Bayesian networks and Markov models, essential for modern AI and machine learning solutions. Over 24-32 hours of instruction, you’ll master the foundations of graphical models, learning how to represent complex dependencies among variables using graphical structures. This course emphasizes practical application, guiding you through key inference techniques and real-world use cases in areas like AI, data science, and decision-making. By the end of the course, you’ll have hands-on experience building and using graphical models to solve real-world problems, from automated reasoning to probabilistic predictions. Whether you’re building AI models for data-driven decision making or refining machine learning algorithms, this course will give you the tools to build more intelligent systems.
The IBM AI Engineering Professional program is a comprehensive 40–60 hour training designed to help learners master the full spectrum of modern AI engineering—from foundational machine learning to deep learning, NLP, MLOps, and production deployment. Built around IBM’s enterprise-grade tools and cloud ecosystem, this course equips participants with the technical depth and hands-on experience needed to build, train, optimize, and deploy AI models at scale. Using IBM Watson Studio, IBM Cloud Pak for Data, and other IBM AI engineering frameworks, participants will work through real-world workflows involving model development, pipeline automation, monitoring, and enterprise deployment practices. By the end of the course, learners will have the practical skills and tooling knowledge necessary to function as AI Engineers capable of supporting enterprise AI initiatives.
The iOS course is designed to help individuals learn how to develop mobile applications for Apple’s iOS platform. iOS is the operating system that powers iPhones, iPads, and iPod Touch devices. The course covers a range of topics related to iOS app development, including programming languages, development tools, and best practices. An iOS course can provide students with the skills and knowledge needed to develop high-quality mobile applications for Apple’s iOS platform. With the increasing popularity of iPhones, iPads, and iPod Touch devices, the demand for skilled iOS developers is likely to continue to grow in the years ahead.
The IoT Deployments course is designed to provide participants with an in-depth understanding of how to design, implement, and manage IoT deployments. The course covers the core concepts of IoT, including its architecture, technologies, and protocols, as well as the challenges and opportunities associated with IoT deployments. The course covers best practices for managing and monitoring IoT deployments, including tips for optimizing cost, managing resource utilization, and monitoring performance. Upon completion of the IoT Deployments course, participants will have a solid understanding of the key features and capabilities of IoT and will be equipped with the knowledge and skills needed to design, implement, and manage enterprise IoT deployments. The course also provides participants with the foundation they need to pursue further certifications in IoT or related fields.
The ITIL V4 (Information Technology Infrastructure Library Version 4) certification course is designed to provide a comprehensive understanding of IT service management (ITSM) principles, practices, and concepts. ITIL V4 is the latest version of the ITIL framework, and it focuses on delivering value to customers through services. The course also covers important ITSM topics such as incident management, problem management, change management, service level management, and service catalog management. These topics are essential for IT professionals who want to improve their knowledge of ITSM processes and best practices. The ITIL V4 certification course is designed for IT professionals who are responsible for ITSM processes within their organization, including service desk managers, IT operations managers, and IT service management consultants. It is also suitable for those who are interested in pursuing a career in ITSM.
Microservices with Azure Service Fabric Training is a 3-day intensive course designed for developers and cloud professionals aiming to build and manage scalable, resilient microservices on Microsoft Azure. This hands-on program dives into the architecture and lifecycle of microservices using Azure Service Fabric—a distributed systems platform designed for cloud-native applications. Participants will learn how to design, deploy, and monitor services, implement service discovery, scale effectively, and integrate with DevOps pipelines. Ideal for teams building high-availability applications, this course equips you with both theoretical foundations and practical implementation strategies for working in cloud-scale environments.
The MLOps: End-to-End Practices for Model Deployment & Governance course is a 40-hour comprehensive training designed to equip professionals with the knowledge and skills to effectively manage machine learning (ML) models throughout their lifecycle. This course covers end-to-end MLOps practices, from model deployment and monitoring to governance. You will learn how to automate ML pipelines, integrate continuous integration and continuous deployment (CI/CD) practices for ML, and implement drift detection mechanisms to ensure model reliability. Additionally, the course delves into AI governance frameworks to ensure models remain compliant, transparent, and ethically sound. By the end of the course, participants will be proficient in managing and scaling ML models in production environments, ensuring models are deployed efficiently, monitored for performance degradation, and governed within industry standards. This training is ideal for data scientists, ML engineers, and DevOps professionals looking to integrate MLOps practices into their workflows.
