AI Development Tools and Frameworks
Description
Hands-on experience with industry-standard AI development tools, frameworks, and platforms for building, deploying, and managing AI applications.
Learning Objectives
Learners will master popular AI frameworks including TensorFlow, PyTorch, and scikit-learn, utilize cloud-based AI services from major providers, implement MLOps practices for model lifecycle management, work with AI development environments and tools, integrate AI APIs and services, and deploy AI models in production environments with monitoring and maintenance capabilities.
Topics (8)
MLOps tools and practices including MLflow, Kubeflow, DVC, model versioning, experiment tracking, automated testing, continuous integration/deployment for ML models, and model monitoring in production.
AI API integration including REST APIs for AI services, GraphQL, microservices architecture for AI applications, containerization with Docker, and service mesh architectures for AI systems.
Comprehensive coverage of TensorFlow ecosystem including TensorFlow Core, Keras high-level API, TensorFlow Serving, TensorFlow Lite, and TensorFlow.js for various deployment scenarios.
PyTorch ecosystem including PyTorch Core, torchvision, torchaudio, PyTorch Lightning, and deployment tools for research and production deep learning applications with dynamic graph computation.
Traditional machine learning libraries including scikit-learn, pandas, NumPy, SciPy, and specialized libraries for various ML tasks including preprocessing, feature selection, and model evaluation.
Cloud AI platforms including AWS SageMaker, Google Cloud AI Platform, Azure Machine Learning, pre-trained AI services, and serverless AI deployment options for scalable AI applications.
AI development environments including Jupyter notebooks, Google Colab, VS Code, PyCharm, Git for ML projects, and collaborative platforms for AI development and experimentation.
Production deployment strategies including model serving, A/B testing for AI models, performance monitoring, drift detection, scaling strategies, and maintenance procedures for production AI systems.