
Explore building a CML pipeline using Apache Spark and Kafka. Learn functional programming techniques for efficient data processing and machine learning workflows.

Explore quality assurance in machine learning, its importance, and strategies for enhancing AI trustworthiness through lessons from other disciplines and best practices in MLOps and data science.

Explore strategies for scaling LLM applications from beta to production, addressing challenges and building adaptable AI infrastructure for evolving models and workflows.

Explore production-quality Generative AI pipelines using Apache Airflow. Learn to orchestrate workflows, integrate data engineering, and implement MLOps best practices for robust AI applications.

Discover how to fine-tune LLMs on specific codebases using Flyte, an open-source orchestration platform. Explore multi-node, multi-gpu distributed training for efficient model adaptation with limited resources.

Expand observability into business domain with Dynatrace Business Events. Learn to capture, ingest, explore, and analyze critical business data using OneAgent, API, and DQL for enhanced business analytics.

Leverage Dask's parallelism capabilities on Kubeflow for advanced ETL processing. Learn to use the Dask Operator to optimize resource utilization in interactive Jupyter sessions and pipeline workflows.

Explore real-time ML pipeline construction using feature stores. Learn to optimize compute efficiency and enhance ML workflows for production environments.

Discover 5 key best practices for treating your data platform as a product, ensuring reliability and scalability for maximum business value.

Implement SecMLOps at every stage of the ML pipeline to address security challenges, comply with data privacy laws, and ensure robust ML system architecture.

Explore DAG-based workflow orchestration challenges and learn about Prefect Orion's DAG-less system for enhanced runtime flexibility and developer experience in data pipelines.

Explore UnionML, a microframework simplifying ML application development. Learn to streamline the process from research to production, addressing common challenges in the ML lifecycle.

Explore challenges in deploying ML pipelines and learn how feature stores and platforms solve data problems for production ML, enabling real-time processing and scaling.

Discover key components for transforming your company into an ML-driven organization. Learn strategies for overcoming challenges in the machine learning development lifecycle and deploying models effectively.

Discover best practices for building effective ML platforms. Learn key components, justification strategies, and how ML infrastructure differs from software infrastructure.

Explore Apache Airflow's role in operational machine learning with a real-world demo and reference implementation of GenAI in production.

Explore GraphRAG: AI-powered content interpretation using LLMs to create knowledge graphs from unstructured text, enabling advanced search and question-answering capabilities.

Explore ByteDance's journey in building a video data processing pipeline using Ray's ecosystem for creating realistic video generation models from text instructions.

Learn Apache Airflow fundamentals, set up environments, write pipelines, and integrate with Google Cloud services. Gain practical skills for efficient workflow automation and data pipeline management.

Learn to programmatically author, schedule, and monitor workflows using Apache Airflow. Covers installation, DAGs, operators, and hands-on examples with MySQL and email integration.

Aprende a instalar y usar Apache Airflow con operadores Spark, Hive y Sqoop en Windows, con tutoriales en inglés y tamil.