Advanced Apache Airflow: Custom Operators and Plugins Training Course

This instructor-led, live training program, available in US (via online or onsite delivery), is designed for advanced-level practitioners seeking to deploy Apache Airflow within cloud infrastructures, establish CI/CD pipelines, and implement comprehensive monitoring and logging protocols. The curriculum provides actionable guidance for government agencies looking to modernize data orchestration processes for government operations.

Upon completion of this training, participants will be able to:

• Deploy Apache Airflow across cloud and containerized environments.
• Establish CI/CD pipelines to automate DAG testing and deployment.
• Integrate monitoring and logging tools to enhance system reliability.
• Optimize Airflow configurations for performance and scalability.
• Implement security best practices for managing workflows and environments.

This guided, instructor-led program, available in US (remote or on-location), is designed for intermediate practitioners seeking to streamline and control machine learning processes. The curriculum covers the end-to-end management of model training, validation, and deployment through the utilization of Apache Airflow, tailored specifically for government operations.

Upon completion of this coursework, attendees will be equipped to:

• Configure Apache Airflow to orchestrate machine learning workflows in accordance with federal standards.
• Automate essential tasks such as data preprocessing, model training, and validation procedures.
• Establish integration between Airflow and various machine learning frameworks and utilities.
• Execute the deployment of machine learning models via automated pipeline mechanisms.
• Monitor performance and optimize operational efficiency for machine learning workflows in production environments.

Apache Airflow serves as an open-source framework designed for the creation, scheduling, and oversight of computational workflows. Utilizing version 2.x of this platform enables federal agencies to orchestrate complex data pipelines at scale, facilitate integration with cloud-based infrastructure, and maintain rigorous observability and security standards within production environments for government operations.

This instructor-led, live training session—available in online or onsite formats—is designed for novice to intermediate-level data analysts and DevOps practitioners seeking to leverage Apache Airflow to construct, automate, and expand end-to-end data pipelines.

Upon completion of this curriculum, participants will be capable of:

• Installing and configuring Apache Airflow across diverse operational environments.
• Developing, scheduling, and monitoring Directed Acyclic Graphs (DAGs) via the TaskFlow API.
• Integrating Airflow with various data repositories, cloud services, and machine learning workflows.
• Managing Extract, Transform, Load (ETL) processes to guarantee data integrity and reliability.
• Securing and scaling Airflow infrastructure through the use of executors, queues, and Kubernetes.
• Implementing observability measures and adhering to best practices for production-grade deployments.

Course Delivery Format

• Interactive lectures and facilitated discussions.
• Extensive practical exercises and skill-building activities.
• Applied implementation within a live-lab environment.

Course Customization Options

• To request customized training materials tailored to specific agency requirements, please contact the administrative staff to arrange details.

This guided, synchronous instructional session in US (delivered online or at a facility) targets intermediate-level professionals seeking to enhance workflow efficiency, manage intricate dependencies, and expand Apache Airflow infrastructures for enterprise-scale data operations. This curriculum is designed for government

Upon completion of this training, participants will be equipped to:

• Enhance the performance and resilience of Apache Airflow workflows.
• Administer and resolve issues related to complex workflow dependencies.
• Utilize advanced Airflow capabilities, such as custom operators and sensors.
• Scale Apache Airflow solutions to accommodate extensive datasets and distributed computing environments.
• Apply industry standards for monitoring, logging, and security within Airflow systems.