AI and ML Integration in Cloud Computing Questions and Answers for Viva

Answer-1: Interactive environments like Jupyter notebooks enable data exploration and model building.

Answer-2: Through data encryption, anonymization, and compliance certifications.

Answer-3: Automated workflows that build, test, and deploy ML models regularly.

Answer-4: Cloud provides scalable NLP models for text analysis, translation, and chatbots.

Answer-5: Reinforcement learning trains agents via rewards; it benefits from cloud compute for simulations.

Answer-6: They provide tools to track model accuracy, data drift, and performance over time.

Answer-7: A sequence of steps for data ingestion, cleaning, transformation, and feeding into models.

Answer-8: Improved automation and monitoring can enhance SLA compliance.

Answer-9: A distributed ML approach where models train locally on devices and aggregate updates via the cloud.

Answer-10: By offering edge AI services and IoT integrations.

Answer-11: To handle variable workloads and large datasets without performance loss.

Answer-12: Through streaming data processing and low-latency inference APIs.

Answer-13: They store raw and processed data accessible for AI and analytics workloads.

Answer-14: To avoid vendor lock-in and optimize performance by leveraging strengths of different providers.

Answer-15: By analyzing usage patterns and recommending budget adjustments.

Answer-16: Kubernetes orchestrates containerized AI workloads for scalability and reliability.

Answer-17: By maintaining different model versions for rollback and comparison.

Answer-18: AI automates testing, deployment, and monitoring, improving DevOps efficiency.

Answer-19: By providing accessible tools and platforms for users without deep expertise.

Answer-20: Models trained on large datasets that can be fine-tuned, saving time and resources.

Answer-21: It requires policies for ethical AI use, data security, and compliance.

Answer-22: More automated ML, AI-powered cloud management, edge AI growth, and increased regulation.

Answer-23: AI integration in cloud computing refers to embedding artificial intelligence capabilities within cloud services to enhance automation, analysis, and decision-making.

Answer-24: Cloud computing provides scalable resources, storage, and tools to train, deploy, and manage machine learning models efficiently.

Answer-25: Services like AWS SageMaker, Google AI Platform, and Azure Machine Learning provide end-to-end AI/ML capabilities.

Answer-26: Because it offers elastic compute power, large storage, and access to managed AI frameworks without upfront infrastructure costs.

Answer-27: GPUs accelerate complex ML model training and inference by providing parallel processing capabilities.

Answer-28: A cloud offering that abstracts infrastructure and toolchain management for AI/ML, allowing users to focus on model development.

Answer-29: Cloud platforms provide APIs, containers, and orchestration tools to deploy models at scale with low latency.

Answer-30: AutoML automates the process of model selection, training, and tuning, enabling non-experts to build ML models.

Answer-31: Cloud storage offers scalable, secure, and accessible repositories for large datasets needed in AI training.

Answer-32: Serverless enables event-driven AI tasks that scale automatically without managing servers.

Answer-33: By detecting anomalies, threats, and automating responses in real-time through intelligent analytics.

Answer-34: Data privacy, latency, cost management, and model governance.

Answer-35: Edge computing processes data near the source, reducing latency and bandwidth for AI applications.

Answer-36: Model inference is running a trained ML model to make predictions on new data in real-time or batch.

Answer-37: By providing shared workspaces, version control, and scalable compute environments.

Answer-38: APIs allow easy integration of AI models and services into existing applications.

Answer-39: Containers package AI workloads with dependencies, enabling portability and scalability.

Answer-40: Using a combination of on-premise and cloud resources to run AI workloads.

Answer-41: Data encryption, access control, audit trails, and compliance with regulations.

Answer-42: By predicting demand, auto-scaling resources, and optimizing workloads.

Answer-43: Using pre-trained models from the cloud and fine-tuning them for specific tasks.

Answer-44: Cloud provides distributed storage and processing frameworks to manage large datasets.

Answer-45: Training builds the model using data, inference uses the model to generate predictions.

Answer-46: Through hardware acceleration, distributed training, and monitoring tools.

Answer-47: Data labeling is crucial for supervised ML and cloud platforms offer managed labeling services.

Answer-48: By providing pre-configured environments for TensorFlow, PyTorch, scikit-learn, etc.

Answer-49: It helps users understand and trust AI decisions through transparency.

Answer-50: AI can optimize workloads and reduce costs via predictive analytics.