Introduction to Cloud Computing

Pragmatic AI Labs via edX Professional Certificate

Overview

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Get started on the fast-growing field of cloud computing with this foundational program. Gain the essential skills and knowledge needed to develop and deploy data science and machine learning solutions on leading cloud platforms like Amazon Web Services (AWS), Azure, and Google Cloud Platform (GCP).

In this program from industry experts and renowned professors from Duke University you will develop a strong foundation through hands-on projects and real-world examples. You will learn cloud concepts like serverless technology, virtual machines, and best practices in addition to frameworks and applications like Kubernetes.

This program is designed to address a growing demand for cloud talent in the industry. By completing this program you will position yourself as a competitive candidate in the job market and potentially accelerate your career.

Syllabus

Courses under this program:
Course 1: Cloud Computing Foundations

Learn the foundations of cloud computing and build websites using serverless, PaaS, and IaaS technologies. Apply DevOps principles and create continuous delivery pipelines for efficient cloud infrastructure management.

Course 2: Cloud Virtualization, Containers and APIs

Master cloud computing fundamentals with virtual machines, containers, microservices, and DevOps best practices for operations.

Course 3: Cloud Data Engineering

Master data engineering for cloud-native applications through distributed systems, big data, and serverless technologies.

Course 4: Cloud Machine Learning Engineering and MLOps

Unleash the power of machine learning with engineering best practices, AutoML, and emerging technologies.

Course 5: Authoritative AWS

Master the core AWS services and architect secure, high-performance cloud solutions through this comprehensive, industry-recognized AWS certification training course.

Course 6: Authoritative GCP

Master Google Cloud Architecture and prepare for the Professional Cloud Architect certification exam through hands-on labs and expert instruction.

Course 7: Azure Fundamentals

Master Azure Fundamentals and confidently pass the AZ-900 certification exam with this comprehensive course, featuring expert guidance, study strategies, and real-world applications.

Course 8: Azure AI Fundamentals

Master Azure AI Fundamentals and confidently pass the AI-900 certification exam with this comprehensive course, featuring hands-on experience with Azure's AI services and practical Machine Learning applications.

Courses

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6 weeks, 3-6 hours a week, 3-6 hours a week
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Gain in-depth knowledge of AWS core services like EC2, S3, VPC, and IAM - Master AWS networking and security best practices to deploy robust, compliant solutions - Learn advanced cloud architecture principles to design scalable, fault-tolerant systems - Explore AWS big data and machine learning services for data analytics workloads - Prepare for AWS Certified Solutions Architect - Professional and other AWS certifications - Get hands-on through integrated labs and projects using real AWS environments
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4 weeks, 3-6 hours a week, 3-6 hours a week
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- Get hands-on experience with cloud computing building blocks like virtual machines and containers
- Learn to develop microservices using technologies like Flask and Kubernetes.
- Discover best practices for effective cloud operations, including monitoring, alerting, load testing, and continuous improvement.
- Apply DevOps principles to build and deploy cloud-native applications.
- Gain practical skills for designing and implementing scalable, reliable cloud solutions.
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4 weeks, 3-6 hours a week, 3-6 hours a week
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- Discover the principles of data engineering and its role in building scalable, cloud-based systems.
- Explore the challenges of the end of Moore's Law and learn to develop distributed systems.
- Gain hands-on experience with big data technologies and best practices for implementing solutions.
- Learn to build serverless data engineering pipelines and apply effective data governance strategies.
- Develop expertise in key data engineering tasks, including ETL, cloud databases, and cloud storage.
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4 weeks, 3-6 hours a week, 3-6 hours a week
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- Discover the principles of machine learning engineering and its role in building scalable, intelligent systems.
- Learn to develop machine learning applications using software engineering best practices and continuous delivery pipelines.
- Explore AutoML technologies for efficient model training with minimal coding effort.
- Gain hands-on experience with open-source and cloud-based AutoML solutions like Ludwig and Cloud AutoML.
- Dive into emerging topics such as MLOps, edge machine learning, and AI APIs for cutting-edge applications.
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4 weeks, 3-6 hours a week, 3-6 hours a week
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- Gain a solid foundation in Azure AI Fundamentals and prepare for the AI-900 certification exam
- Comprehensive coverage of all content relevant to the AI-900 certification
- Step-by-step walkthrough of Azure AI Learning Paths
- Create your own personalized study guide and exam strategy
- Access the author's extensive study notes and reference guides
- Hands-on experience with Azure Machine Learning Studio and Designer
- Explore Computer Vision techniques, including image analysis, classification, and object detection
- Dive into Natural Language Processing (NLP) with Azure Language Service
- Practical examples of Azure OpenAI Service and Azure AI Service
- Learn about speech recognition, synthesis, and translation
- Understand Conversational Language Understanding and Azure Bot Service integration
- Discover anomaly detection with Azure Anomaly Detector
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4 weeks, 3-6 hours a week, 3-6 hours a week
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- Gain a solid foundation in Azure Fundamentals and prepare for the AZ-900 certification exam
- Comprehensive coverage of all content relevant to the AZ-900 certification
- Step-by-step walkthrough of Azure Learning Paths
- Create your own personalized study guide and exam strategy
- Access the author's extensive study notes and reference guides
- Understand the fundamentals of large language models within the Azure ecosystem
- Design scalable architectural patterns and deployment strategies for large-scale applications
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2 weeks, 3-6 hours a week, 3-6 hours a week
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This comprehensive course, designed for cloud architects and developers, covers the essential skills needed to design, plan, and manage robust enterprise solutions on Google Cloud Platform (GCP). Through a series of hands-on labs and expert-led instruction, you will:
- Learn to design and plan cloud solution architectures that meet business and technical requirements
- Configure network topologies, storage systems, and compute resources in GCP
- Implement security best practices and ensure compliance
- Analyze and optimize technical and business processes
- Manage solution implementation, monitoring, and deployment
By the end of this course, you will have the knowledge and practical experience to effectively architect and manage scalable, secure, and reliable solutions on Google Cloud Platform. The course also prepares you for the Google Professional Cloud Architect certification exam.