This course is designed to equip students with the knowledge and skills required to effectively leverage cloud computing and artificial intelligence in various applications, including but not limited to finance and accounting. It explores the integration of cloud technologies and AI to solve complex problems and optimize business processes.
Course duration : 30 Hours | Required study hours : 25 to 30 hours
Online Interactive Training Program
Frequency : Flexi pass to attend for the next 120 days in any of the schedule.
Our Stats: 150 + Batches Completed | 1500 + Certified professional
A bachelor's degree in a relevant field like computer science, engineering, or data science is typically. Proficiency in cloud platforms like AWS, Azure, or Google Cloud is essential. Certifications in these platforms demonstrate expertise. Knowledge of data engineering, data pipelines, and database management is valuable. Integration of AI: Cloud Engineering in the context of AI involves the seamless integration of AI technologies into cloud-based systems. This includes deploying machine learning models, natural language processing, and computer vision algorithms on cloud platforms.
Scalability: Cloud platforms offer the ability to scale AI applications easily, allowing businesses to adapt to changing workloads and data requirements.
Data Management: Effective data storage, processing, and management are crucial components of AI in the cloud. Cloud Engineering professionals work on optimizing data pipelines and storage solutions.
Automation: Cloud Engineering often involves automating various aspects of AI, from model training and deployment to monitoring and optimization.
Security: Ensuring the security of AI models and data is paramount. Cloud Engineers implement robust security measures to protect sensitive AI applications.
Module 1 : Prompt Engineering
1.1 Productivity Tool
1.2 Forcasting and data analytics
1.3 Personal Profile and Grooming
1.4 Business Documentation
Module 2: Introduction to Cloud Computing
2.1 Understanding Cloud Models (IaaS, PaaS, SaaS)
2.2 Cloud Service Providers (AWS, Azure, GCP)
2.3 Cloud Deployment Models (Public, Private, Hybrid)
Module 3: Cloud Infrastructure
3.1 Virtualization and Containers
3.2 Networking in the Cloud
3.3 Storage Solutions in Cloud
Module 4: Cloud Security
4.1 Identity and Access Management (IAM)
4.2 Data Encryption and Security Best Practices
4.3 Compliance and Governance in Cloud
Module 5: AI Fundamentals
5.1 Introduction to Artificial Intelligence
5.2 Machine Learning vs. Deep Learning
5.3 Data Preprocessing and Feature Engineering
Module 6: AI in the Cloud
6.1 Cloud-Based AI Services
6.2 Building AI Models in the Cloud
6.3 Deploying AI Models on Cloud Platforms
Module 7: Big Data and Cloud
7.1 Introduction to Big Data
7.2 Big Data Technologies (Hadoop, Spark)
7.3 Data Warehousing in the Cloud
Module 8: DevOps and Automation
8.1 Continuous Integration and Continuous Deployment (CI/CD)
8.2 Infrastructure as Code (IaC)
8.3 DevOps Tools in Cloud Environments
Module 9: Cloud Cost Management
9.1 Cost Monitoring and Optimization
9.2 Budgeting and Cost Allocation
9.3 AWS Cost Explorer and Similar Tools
Trainer Profile :
Dinesh R M , Program Coordinator & Program Instructor
Chief Enterprise Architect & Portfolio Management at Team solutions LLC ,
Experienced Portfolio , Program & Project Manager with a demonstrated history of working in the IT , airlines/aviation industry. Skilled in Enterprise Architecture, Digital Transformation , Digital Governance , IT Project Life cycle, Requirements Analysis, IT Governance, and Managed IT Services ITIL.
Q : . What is AI and BI?
A: AI refers to the development of computer systems capable of performing tasks that typically require human intelligence, such as learning from data, recognizing patterns, and making decisions.
BI involves the use of technology and techniques to gather, process, and analyze business data to support decision-making.
Q : What are the key benefits of learning AI and BI?
A: AI can automate processes, provide data-driven insights, and enhance efficiency.
BI helps businesses make informed decisions by providing access to actionable data.
Q : Who can benefit from AI and BI courses?
A: Anyone interested in AI or BI can benefit, including students, professionals, entrepreneurs, and data enthusiasts..
Q : What career opportunities are available in Cloud Engineering for AI?
A: Cloud Engineering for AI opens up various career opportunities, including roles such as Cloud AI Engineer, AI Solutions Architect, Data Engineer, DevOps Engineer, AI Consultant, and AI Researcher. Industries such as technology, finance, healthcare, and e-commerce offer job prospects in this field.
Q : IWhat are the prerequisites for AI and BI courses?
A: Prerequisites vary, but typically involve a basic understanding of mathematics, statistics, and programming for AI. BI courses may require basic data analysis skills.
Q : What programming languages are commonly used in AI courses?
A: Python is the most popular language in AI courses, known for its libraries like TensorFlow and PyTorch.
Q : What are the career prospects after completing AI and BI courses?
A: AI professionals can pursue careers in machine learning, data science, and AI research.
BI specialists can work as data analysts, business analysts, or BI developers.
Q : Can AI and BI be learned online?
A: Yes, Team Academy provides AI and BI courses that can be taken remotely.
Q : What are some AI and BI tools and technologies to learn?
A: - AI: TensorFlow, PyTorch, scikit-learn.
- BI: Tableau, Power BI, QlikView.
Q : Are there job opportunities in AI and BI globally?
A : Yes, AI and BI professionals are in demand worldwide, with opportunities in various industries.
Q : Can I take AI and BI courses if I don't have a technical background?
A : Yes, many introductory courses are designed for individuals with non-technical backgrounds.
Q : Are there any ethical considerations in AI and BI courses?
A : Yes, ethical considerations, including data privacy and bias, are important aspects of AI and BI training.
