📚 Syllabus & Course Curriculum
Data Science & Analytics
Module-by-module breakdown of Continuous Integration and Delivery for AI, from foundations to a certified capstone project.
Outline
Design scalable AI systems using containerization and orchestration tools like Docker and Kubernetes • Implement continuous integration pipelines using Jenkins and GitLab CI/CD for automated testing and deployment • Analyze AI project requirements and develop a comprehensive CI/CD strategy for improved collaboration and efficiency
Outline
Develop data preprocessing pipelines using Apache Beam and Apache Spark for efficient data processing and transformation • Configure data storage solutions like Amazon S3 and Google Cloud Storage for scalable data management • Evaluate data quality and implement data validation techniques using Great Expectations and Deequ
Outline
Design and implement deep learning models using TensorFlow and PyTorch for computer vision and natural language processing tasks • Develop and evaluate machine learning algorithms using scikit-learn and XGBoost for regression, classification, and clustering tasks • Optimize model performance using hyperparameter tuning techniques like Grid Search and Random Search
Outline
Train and deploy machine learning models using Amazon SageMaker and Google Cloud AI Platform for scalable model deployment • Implement hyperparameter optimization techniques like Bayesian Optimization and Gradient-Based Optimization for improved model performance • Evaluate model performance using metrics like accuracy, precision, and recall, and develop strategies for model improvement
Outline
Deploy machine learning models using TensorFlow Serving and AWS SageMaker for scalable model deployment • Develop and implement MLOps workflows using Apache Airflow and Zapier for automated model deployment and monitoring • Configure model monitoring and logging solutions like Prometheus and Grafana for real-time model performance tracking
Outline
Analyze and mitigate bias in machine learning models using techniques like data preprocessing and feature engineering • Develop and implement fairness metrics like disparity impact and equal opportunity difference for fair model evaluation • Evaluate and implement explainability techniques like SHAP and LIME for transparent model interpretation
Outline
Develop and implement AI solutions for business applications like customer segmentation and predictive maintenance • Evaluate and implement AI-powered chatbots using Dialogflow and Microsoft Bot Framework for improved customer service • Analyze and develop strategies for AI adoption in various industries like healthcare and finance
Earn government-registered certification in Continuous Integration and Delivery for AI
e-Certificate and e-Marksheet issued on successful completion.
