Introduction to Cloud Computing

Introduction to Cloud Computing

• Cloud computing offers on-demand IT resources via the internet, using pay-as-you-go pricing.
• Access to computing power, storage, and databases comes from a cloud provider, negating the need for physical data centers/servers.
• Cloud resources can be scaled and provisioned as needed

Legacy IT vs. Cloud Computing

• Legacy IT requires a significant upfront investment in data centers, physical hardware, software licensing, maintenance, power, connectivity, and IT staff.
• Legacy IT has drawbacks including high costs, security & backup risks, scalability issues, limited flexibility, and compatibility concerns

Key Characteristics of Cloud Computing

• On-Demand Self-Service: Users can automatically provision computing resources without human intervention.
• Broad Network Access: Cloud services can be accessed from multiple devices (laptops, phones, tablets) via the internet.
• Resource Pooling: Cloud providers employ a multi-tenant model to serve many customers using shared resources.
• Rapid Elasticity: Resources can be scaled up or down automatically based on demand.
• Measured Service: Cloud usage is tracked, and customers pay only for what they use.
• Agility: Cloud computing enables rapid deployment, testing, and scaling of applications.
• Elasticity: Scales resources automatically, based on demand.
• Cost Efficiency: Reduces capital expenses (CapEx) and uses a pay-as-you-go payment model.
• Performance & Reliability: Cloud providers ensure high availability and redundancy with multiple data centers.
• Security & Compliance: Cloud providers include built-in security measures, encryption, and regulatory compliance.

Cloud Computing Service Models

• Software as a Service (SaaS): Cloud-hosted applications are accessible via the internet without installation.
• Platform as a Service (PaaS): Provides a development environment with pre-built tools.
• Infrastructure as a Service (IaaS): Includes virtualized computing resources like servers, storage, and networking on demand.

Cloud Deployment Models

• Public Cloud: Services are offered over the internet and shared by several customers.
• Private Cloud: A cloud infrastructure is dedicated to a single organization.
• Hybrid Cloud: Combines public and private clouds for flexibility and security.
• Community Cloud: A shared infrastructure caters to organizations with similar needs.

Why Learn AWS?

• AWS is the most widely used public cloud platform.
• There is high demand among employers for IT professionals certified in AWS .
• Cloud skills are essential in business today.
• Major companies like Netflix, NASA, Airbnb, and Pfizer use AWS.

Cloud Computing Market & Adoption

• AWS leads the cloud computing market, followed by Microsoft Azure and Google Cloud.
• Healthcare, finance, retail, and education industries are adapting cloud computing.
• Current trends in cloud computing include AI and ML integration, IoT and edge computing, and serverless computing.

Introduction to AI in Business

• AI allows computer systems to handle tasks requiring human intelligence like problem-solving, learning, and decision-making.
• AI improves efficiency across industries through process automation, enhanced customer experiences, and analysis of large datasets.

Evolution of AI

• 1950s–1970s: Development of early AI concepts, rule-based systems, and symbolic reasoning.
• 1980s–2000s: Machine Learning (ML) was integrated into AI for business applications.
• 2010s–Present: Deep Learning drives rapid AI adoption, which enables more complex decision-making.

Types of AI Used in Business

• Machine Learning (ML): AI performance improves by learning from data.
• Natural Language Processing (NLP): AI processes and understands human language.
• Computer Vision: AI can interpret images and videos.
• Robotics: AI drives automation in manufacturing and logistics.

AI vs. Traditional Automation

• Traditional automation follows fixed rules, whereas AI-driven automation learns and adapts.
• Traditional automation is limited to predefined tasks
• AI-driven automation can handle complex, evolving operations.
• Traditional automation uses pre-programmed instructions, whereas AI-driven automation uses data insights for decision-making.

Current Trends in AI for Business

• AI-Powered Analytics: Businesses use AI to gain actionable insights.
• AI Chatbots & Virtual Assistants: Enhances customer support and user engagement.
• Autonomous Systems: AI is used to create self-driving cars, drones, and smart factories.

AI-Powered Business Applications

• Marketing & Customer Insights: Personalization, sentiment analysis, and ad targeting are AI applications.
• Customer Service: AI applications include AI Chatbots, sentiment analysis, and voice assistants.
• Finance & Banking: Fraud detection, automated financial analysis, and stock market predictions utilize AI.
• Human Resources: AI is used in hiring and employee engagement analysis.
• Supply Chain & Logistics: AI is used for demand forecasting, route optimization, and warehouse automation.
• Business Process Automation: Uses AI for document processing, CRM automation, and decision support.
• Healthcare & Pharmaceuticals: AI-assisted diagnostics, drug discovery, and personalized treatments are AI applications.
• Manufacturing: Predictive maintenance, quality control, and process optimization are AI applications.
• Cybersecurity: AI applications include threat detection, behavioral analytics, and security automation.

Challenges of AI Adoption

• High Costs: AI implementation is expensive.
• Data Privacy: AI raises security and regulatory concerns.
• Ethical Issues: AI bias and transparency issues need addressing.
• Workforce Adaptation: AI adoption requires employee training.

The Future of AI in Business

• AI will integrate with IoT and Blockchain, which will enhance security and data management.
• AI tools will provide businesses with assistence with strategic planning.
• Autonomous Systems Growth will revolutionize transportation and other industries.

Regulatory and Compliance Issues

• Governments are enforcing AI standards, leading to emerging AI regulations.
• GDPR Compliance: Companies must ensure data privacy and security.

AI for Competitive Advantage

• Business Intelligence: AI extracts valuable insights for decision-making.
• Innovation & Product Development: AI enhances research and development.
• Customer Experience: AI-driven personalization improves user satisfaction.

Building an AI-Ready Strategy

• Assess Needs: Determine how AI can improve operations.
• Invest: Allocate resources for AI infrastructure and talent.
• Implement: Integrate AI into workflows for efficiency.

Evolution of Big Data

• The way data is generated and consumed has changed

  Big Data has evolved significantly, shifting from a model where few companies generated data to one where everyone generates and consumes it due to digital transformation, the internet, and connected devices. The rise of IoT, social media, and cloud computing has led to exponential data growth, with sources including human-generated, machine-generated, web, and sensor data.

  over time.

• Old Model: Few companies generated data, while most consumed it.

• New Model: Everyone generates and consumes data because of digital transformation, the internet, and connected devices.

• Technological Evolution: The rise of IoT, social media, and cloud computing has contributed to exponential data growth.

Big Data Sources

• Data is categorized as human-generated, machine-generated, web data, and sensor data.
• Human-Generated Data: Includes emails, documents, images, videos, and social media posts.
• Machine-Generated Data: Logs from sensors, IoT devices, and automated systems, which are significantly larger in volume than human-generated data.
• Web Data: Includes social media interactions, clickstream data (website user activity), and logs from online transactions.
• Sensor Data: Devices embedded in roads, vehicles, or smart meters generate real-time monitoring data.

Understanding Big Data

• Big Data refers to datasets too vast and complex for traditional database management tools.
• Facebook stores 40 PB of data and captures 100 TB daily.
• Twitter generates 8 TB of data per day.
• Google processes over 24 PB daily.
• E-commerce platforms store 50 TB of new data daily.

Characteristics of Big Data (5 Vs)

• Big Data is defined by five key characteristics
• Volume: The enormous size of data.
• Velocity: The speed at which data is generated and processed in real-time.
• Veracity: Addresses the reliability and accuracy of data plus inconsistencies and uncertainties.
• Variety: The diversity of data formats, including text, images, videos, and structured/unstructured data.
• Value: Deriving meaningful insights to guide decision-making from raw data.

Traditional Databases vs. Big Data

• Data Size: Traditional databases work in terabytes, whereas Big Data systems use petabytes to zettabytes.
• Data Type: Traditional databases manage structured data, while Big Data systems handle structured and unstructured data.
• Hardware: Traditional databases use centralized, expensive hardware. Big Data systems use distributed, cost-effective hardware.
• Software: Traditional databases use SQL-based software (MySQL, Oracle), whereas Big Data systems use NoSQL, Hadoop.
• Scalability: Traditional databases have limited scalability, while Big Data systems are highly scalable.

Big Data Analytics

• Big Data Analytics is the process of analyzing large datasets to uncover patterns, trends, and correlations for better decision-making.
• Data Collection: The first stage includes gathering structured and unstructured data from multiple sources.
• Data Storage: Storing data in scalable and distributed storage systems.
• Data Processing: Using tools like Hadoop and Spark to process massive datasets.
• Analysis & Visualization: Extracting insights using machine learning, AI, and BI tools.

Goals of Big Data Analytics

• Improve efficiency and intelligence in organizations.
• Predict trends and optimize operations.
• Enhance customer experiences through data-driven decisions.

Applications & Use Cases of Big Data Analytics

• Big Data is transforming industries with practical applications in healthcare, finance, retail, smart cities, manufacturing, and IoT.

Challenges of Big Data

• Effectively managing massive datasets is an issue.
• Protecting sensitive data is necessary for data security and privacy.
• Accurately handling inconsistencies and inaccuracies is key to data quality.
• Combining data from multiple sources into a single system is necessary for appropriate integration.

Hadoop as a Solution to Big Data Problems

• Hadoop is an open-source framework for storing and processing Big Data with distributed computing.
• Hadoop addresses key challenges through these methods:
• HDFS (Hadoop Distributed File System): Stores large datasets across multiple machines.
• MapReduce: Uses a programming model that enables parallel data processing.
• Scalability & Cost-Effectiveness: Commodity hardware is used instead of expensive servers.