{"course":{"productid":34633,"modality":1,"active":true,"language":"de","title":"AI+ Cloud","productcode":"AICL","vendorcode":"AH","vendorname":"AI Certs","fullproductcode":"AH-AICL","courseware":{"has_ekit":false,"has_printkit":true,"language":""},"url":"https:\/\/portal.flane.ch\/course\/aicerts-aicl","objective":"<ul>\n<li><strong>AI Model Development<\/strong><ul>\n<li>Students learn to construct, train, and optimize machine learning models utilizing cloud-based tools and services. This involves learning to choose methods, preprocess data, and optimize models.<\/li><\/ul><\/li><li><strong>Mastering cloud AI model deployment<\/strong><ul>\n<li>Learners will master cloud AI model deployment and integration into existing systems and workflows. Learn deployment pipelines, version control, and CI\/CD procedures to seamlessly integrate AI solutions into production environments.<\/li><\/ul><\/li><li><strong>Problem-Solving in AI and Cloud<\/strong><ul>\n<li>Participants will learn to apply AI and cloud computing concepts to real-world problems will improve problem-solving skills.<\/li><\/ul><\/li><li><strong>Optimization Techniques<\/strong><ul>\n<li>Emphasizing AI model development and cloud deployment, learners will learn to optimize AI models and processes for performance, scalability, and cost.<\/li><\/ul><\/li><\/ul>","essentials":"<ul>\n<li>A foundational understanding of key concepts in both artificial intelligence and cloud computing<\/li><li>Fundamental understanding of computer science concepts like programming, data structures, and algorithms<\/li><li>Familiarity with cloud computing platforms like AWS, Azure, or GCP<\/li><li>Basic knowledge of mathematics as it important for machine learning, which is a core component of AI+ Cloud program<\/li><\/ul>","outline":"<h4>Module 1: Fundamentals of Artificial Intelligence (AI) in Cloud<\/h4><ul>\n<li>1.1 Introduction to AI and Its Application<\/li><li>1.2 Overview of Cloud Computing and Its Benefits<\/li><li>1.3 Benefits and Challenges of AI-Cloud Integration<\/li><\/ul><h4>Module 2: Introduction to Artificial Intelligence<\/h4><ul>\n<li>2.1 Basic Concepts and Principles of AI<\/li><li>2.2 Machine Learning and Its Applications<\/li><li>2.3 Overview of Common AI Algorithms<\/li><li>2.4 Introduction to Python Programming for AI<\/li><\/ul><h4>Module 3: Fundamentals of Cloud Computing<\/h4><ul>\n<li>3.1 Cloud Service Models<\/li><li>3.2 Cloud Deployment Models<\/li><li>3.3 Key Cloud Providers and Offerings (AWS, Azure, Google Cloud)<\/li><\/ul><h4>Module 4: AI Services in the Cloud<\/h4><ul>\n<li>4.1 Integration of AI Services in Cloud Platform<\/li><li>4.2 Working with Pre-built Machine Learning Models<\/li><li>4.3 Introduction to Cloud-based AI tools<\/li><\/ul><h4>Module 5: AI Model Development in the Cloud<\/h4><ul>\n<li>5.1 Building and Training Machine Learning Models<\/li><li>5.2 Model Optimization and Evaluation<\/li><li>5.3 Collaborative AI Development in a Cloud Environment<\/li><\/ul><h4>Module 6: Cloud Infrastructure for AI<\/h4><ul>\n<li>6.1 Setting Up and Configuring Cloud Resources<\/li><li>6.2 Scalability and Performance Considerations<\/li><li>6.3 Data Storage and Management in the Cloud<\/li><\/ul><h4>Module 7: Deployment and Integration<\/h4><ul>\n<li>7.1 Strategies for Deploying AI Models in the Cloud<\/li><li>7.2 Integration of AI Solutions with Existing Cloud-Based Applications<\/li><li>7.3 API Usage and Considerations<\/li><\/ul><h4>Module 8: Future Trends in AI+ Cloud Integration<\/h4><ul>\n<li>8.1 Introduction to Future Trends<\/li><li>8.2 AI Trends Impacting Cloud Integration<\/li><\/ul><h4>Module 9: Capstone Project<\/h4><ul>\n<li>9.1 Exercise 1: Diabetes Prediction Using Machine Learning<\/li><li>9.2 Exercise 2: Building &amp; Deploying an Image Classification Web App with GCP AutoML Vision Edge, Tensorflow.js &amp; GCP App Engine<\/li><li>9.3 Exercise 3: How to deploy your own ML model to GCP in 5 simple steps.<\/li><li>9.4 Exercise 4: Google Cloud Platform Custom Model Upload , REST API Inference and Model Version Monitoring<\/li><li>9.5 Exercise 5: Deploy Machine Learning Model in Google Cloud Platform Using Flask<\/li><\/ul>","summary":"<p>The AI+ Cloud&trade; certification program targets developers and IT professionals aspiring to excel in cloud computing integrated with artificial intelligence. The curriculum offers an in-depth exploration of AI and cloud computing, encompassing advanced cloud infrastructure and AI model deployment. Participants gain practical insights into cloud-based AI applications, culminating in an interactive capstone project. With these skills, graduates are primed to navigate the dynamic AI+ Cloud&trade; integration landscape, equipped to design and implement AI solutions seamlessly within cloud environments for sustained success.<\/p>\n<p>An exam voucher is included in the course price.<\/p>","objective_plain":"- AI Model Development\n- Students learn to construct, train, and optimize machine learning models utilizing cloud-based tools and services. This involves learning to choose methods, preprocess data, and optimize models.\n- Mastering cloud AI model deployment\n- Learners will master cloud AI model deployment and integration into existing systems and workflows. Learn deployment pipelines, version control, and CI\/CD procedures to seamlessly integrate AI solutions into production environments.\n- Problem-Solving in AI and Cloud\n- Participants will learn to apply AI and cloud computing concepts to real-world problems will improve problem-solving skills.\n- Optimization Techniques\n- Emphasizing AI model development and cloud deployment, learners will learn to optimize AI models and processes for performance, scalability, and cost.","essentials_plain":"- A foundational understanding of key concepts in both artificial intelligence and cloud computing\n- Fundamental understanding of computer science concepts like programming, data structures, and algorithms\n- Familiarity with cloud computing platforms like AWS, Azure, or GCP\n- Basic knowledge of mathematics as it important for machine learning, which is a core component of AI+ Cloud program","outline_plain":"Module 1: Fundamentals of Artificial Intelligence (AI) in Cloud\n\n\n- 1.1 Introduction to AI and Its Application\n- 1.2 Overview of Cloud Computing and Its Benefits\n- 1.3 Benefits and Challenges of AI-Cloud Integration\nModule 2: Introduction to Artificial Intelligence\n\n\n- 2.1 Basic Concepts and Principles of AI\n- 2.2 Machine Learning and Its Applications\n- 2.3 Overview of Common AI Algorithms\n- 2.4 Introduction to Python Programming for AI\nModule 3: Fundamentals of Cloud Computing\n\n\n- 3.1 Cloud Service Models\n- 3.2 Cloud Deployment Models\n- 3.3 Key Cloud Providers and Offerings (AWS, Azure, Google Cloud)\nModule 4: AI Services in the Cloud\n\n\n- 4.1 Integration of AI Services in Cloud Platform\n- 4.2 Working with Pre-built Machine Learning Models\n- 4.3 Introduction to Cloud-based AI tools\nModule 5: AI Model Development in the Cloud\n\n\n- 5.1 Building and Training Machine Learning Models\n- 5.2 Model Optimization and Evaluation\n- 5.3 Collaborative AI Development in a Cloud Environment\nModule 6: Cloud Infrastructure for AI\n\n\n- 6.1 Setting Up and Configuring Cloud Resources\n- 6.2 Scalability and Performance Considerations\n- 6.3 Data Storage and Management in the Cloud\nModule 7: Deployment and Integration\n\n\n- 7.1 Strategies for Deploying AI Models in the Cloud\n- 7.2 Integration of AI Solutions with Existing Cloud-Based Applications\n- 7.3 API Usage and Considerations\nModule 8: Future Trends in AI+ Cloud Integration\n\n\n- 8.1 Introduction to Future Trends\n- 8.2 AI Trends Impacting Cloud Integration\nModule 9: Capstone Project\n\n\n- 9.1 Exercise 1: Diabetes Prediction Using Machine Learning\n- 9.2 Exercise 2: Building & Deploying an Image Classification Web App with GCP AutoML Vision Edge, Tensorflow.js & GCP App Engine\n- 9.3 Exercise 3: How to deploy your own ML model to GCP in 5 simple steps.\n- 9.4 Exercise 4: Google Cloud Platform Custom Model Upload , REST API Inference and Model Version Monitoring\n- 9.5 Exercise 5: Deploy Machine Learning Model in Google Cloud Platform Using Flask","summary_plain":"The AI+ Cloud\u2122 certification program targets developers and IT professionals aspiring to excel in cloud computing integrated with artificial intelligence. The curriculum offers an in-depth exploration of AI and cloud computing, encompassing advanced cloud infrastructure and AI model deployment. Participants gain practical insights into cloud-based AI applications, culminating in an interactive capstone project. With these skills, graduates are primed to navigate the dynamic AI+ Cloud\u2122 integration landscape, equipped to design and implement AI solutions seamlessly within cloud environments for sustained success.\n\nAn exam voucher is included in the course price.","skill_level":"Beginner","version":"1.0","duration":{"unit":"d","value":5,"formatted":"5 Tage"},"pricelist":{"List Price":{"SI":{"country":"SI","currency":"EUR","taxrate":20,"price":3450},"DE":{"country":"DE","currency":"EUR","taxrate":19,"price":3450},"AT":{"country":"AT","currency":"EUR","taxrate":20,"price":3450},"US":{"country":"US","currency":"USD","taxrate":null,"price":3995},"SE":{"country":"SE","currency":"EUR","taxrate":25,"price":3450},"CA":{"country":"CA","currency":"CAD","taxrate":null,"price":5515},"CH":{"country":"CH","currency":"CHF","taxrate":8.1,"price":3450}}},"lastchanged":"2026-04-01T15:53:11+02:00","parenturl":"https:\/\/portal.flane.ch\/swisscom\/json-courses","nexturl_course_schedule":"https:\/\/portal.flane.ch\/swisscom\/json-course-schedule\/34633","source_lang":"de","source":"https:\/\/portal.flane.ch\/swisscom\/json-course\/aicerts-aicl"}}