40 Hours
Automate deployment pipelines and manage cloud infrastructure at scale using advanced AWS DevOps tools.
35 Hours
Secure cloud data and workloads with advanced security controls, compliance frameworks, and identity management on AWS.
30 Hours
Master architectural principles and services on AWS to design secure, robust, and cost-effective cloud infrastructures.
38 Hours
Gain hands-on operational expertise in deploying, managing, and operating scalable systems on AWS.
35 Hours
Manage cloud services, storage, networking, and security compute environments in Microsoft Azure.
40 Hours
Build practical skills in Azure DevOps Training with guided training and hands-on practice.
25 Hours
Master core cloud concepts, security, compliance, privacy, and foundational Microsoft Azure architecture to prepare for the AZ-900 certification.
28 Hours
Design and implement robust compute, storage, networking, and governance solutions within Microsoft Azure.
40 Hours
Master container orchestration, cluster management, networking, and deployment security using Kubernetes.
30 Hours
Design, develop, and manage robust enterprise solutions utilizing Google Cloud Platform (GCP) technologies.
38 Hours
Automate cloud infrastructure provisioning and management using HashiCorp Terraform.
35 Hours
Gain industry-recognized cybersecurity skills to protect networks, systems, and data while preparing for the CompTIA Security+ certification.
42 Hours
Build practical skills in Cybersecurity with AI with guided training and hands-on practice.
30 Hours
Build practical skills in Certified Ethical Hacker ( CEHV13 ) with guided training and hands-on practice.
45 Hours
Build practical skills in Cloud Security with guided training and hands-on practice.
30 Hours
Build practical skills in Beginner Cybersecurity Training with guided training and hands-on practice.
50 Hours
45 Hours
Build practical skills in Digital Forensics Beginner with guided training and hands-on practice.
40 Hours
Build practical skills in GRC Beginner with guided training and hands-on practice.
45 Hours
Build practical skills in Network Security with guided training and hands-on practice.
38 Hours
Build practical skills in SOC analyst with guided training and hands-on practice.
40 Hours
Build practical skills in AI Machine learning with guided training and hands-on practice.
42 Hours
Build practical skills in Azure Data scientist with guided training and hands-on practice.
40 Hours
40 Hours
Build practical skills in Python with AI with guided training and hands-on practice.
35 Hours
Build practical skills in Power BI with guided training and hands-on practice.
35 Hours
Build practical skills in Data Analytics with guided training and hands-on practice.
38 Hours
Build practical skills in Salesforce Administrator with guided training and hands-on practice.
42 Hours
Build practical skills in Salesforce Developer with guided training and hands-on practice.
40 Hours
45 Hours
30 Hours
Master Jira to plan, track, and manage Agile projects with Scrum and Kanban workflows.
30 Hours
Build practical skills in PRINCE 2 Foundational with guided training and hands-on practice.
30 Hours
Build practical skills in CAPM Training with guided training and hands-on practice.
35 Hours
Build practical skills in PMP Training with guided training and hands-on practice.
40 Hours
Build practical skills in Scrum Master Training with guided training and hands-on practice.
42 Hours
Automate web application testing using Selenium WebDriver with Java and TestNG.
38 Hours
Learn software testing fundamentals, defect reporting, and quality assurance using industry-standard practices.
38 Hours
38 Hours
Process large datasets efficiently using Apache Spark for analytics and machine learning.
42 Hours
Master modern Big Data engineering by designing scalable data pipelines, processing massive datasets, and building cloud-ready data architectures.
36 Hours
Learn distributed storage and large-scale data processing using the Hadoop ecosystem.
35 Hours
Understand the core principles of Artificial Intelligence, Machine Learning basics, and real-world AI applications.
40 Hours
Master Large Language Models, prompt engineering, diffusion models, and building end-to-end GenAI applications.
40 Hours
Master supervised and unsupervised learning, model evaluation, deep neural networks, and algorithmic problem solving.
45 Hours
Bridge the gap between ML models and production by mastering CI/CD pipelines, model tracking, automation, and monitoring.