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Master comprehensive RAG pipeline evaluation using MLflow and open-source LLMs. Learn to implement custom metrics, optimize systems, track experiments, and visualize results to enhance your retrieval-augmented generation implementations.
Explore Google Colaboratory for deep learning in computer vision with Python, from creating interactive notebooks to building and training CNNs for image classification using the CIFAR-10 dataset.
Dive into the fundamentals of containers and Kubernetes with hands-on exercises, learning how to create Docker containers and manage them with Kubernetes for consistent application deployment and automated scaling.
Discover how to build real-time analytics pipelines using Snowpipe Streaming and Dynamic Tables in Snowflake, enabling high-volume data ingestion with minimal latency and automated transformations for immediate reporting.
Discover how to analyze large-scale complex networks using High Performance Computing and the Networkit module, focusing on social network properties and efficient GPU-based analysis techniques.
Discover how GenAI can automate QA tasks by creating page objects, locators, and smoke tests for REST API services, with insights on integrating code generation and self-healing capabilities to enhance test automation workflows.
Learn how to build functional AI agents from scratch with a practical guide covering core components, architecture, and progressive development techniques for both beginners and experienced developers.
Explore how Graph Neural Networks transform molecular structures into graph-based models for predicting molecular activity, with techniques for handling imbalanced datasets and optimizing performance in biological contexts.
Explore strategies for tackling non-standard ML tasks with limited datasets, from technology selection to maximizing small data potential for unique problem domains.
Discover how to orchestrate ETL processes using Apache Airflow, exploring its architecture, DAGs, Tasks, Variables, Params, and XComs through practical examples for effective data engineering pipeline management.
Discover how to create intelligent agents using LlamaIndex, from basic components to custom implementations. Learn to streamline development of agent-driven solutions through practical demonstrations and hands-on techniques.
Explore the evolution of recommendation systems from basic heuristics to advanced deep learning and LLMs with hands-on implementation using real-world datasets and popular libraries for creating personalized recommendation engines.
Discover strategies for building robust data pipelines for AI applications, from data streaming with Debezium to cost optimization with Snowflake, while ensuring data security through RBAC and proper PII handling.
Discover how to model and implement multiagent systems using ontology-driven approaches with the SPADE platform, moving efficiently from theoretical concepts to practical implementation.
Explore Azure AI Studio's capabilities for rapid prototyping through hands-on demonstrations of chatbots, image generation, and practical AI applications for evaluating solution feasibility.
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