Building resilient ML pipelines: Scaling data science impact with production-grade engineering
In this session, Manasa Hari will discuss how to transform machine learning prototypes into scalable, production-grade pipelines. Attendees will learn essential engineering practices to automate, test, and deploy ML pipelines that seamlessly integrate with platform workflows, maximizing the impact of data science.
Building resilient machine learning pipelines is crucial for scaling AI solutions. This talk will outline the best practices for structuring ML workflows in production environments, covering Python best practices, DAG-based pipeline design, and effective packaging strategies. Manasa Hari will explore approaches for testing and deploying ML systems, ensuring seamless integration with platform workflows. Designed for platform engineers and data scientists, this session will provide practical insights into streamlining the transition from development to deployment, enabling the creation of robust and scalable AI solutions.
Building resilient ML pipelines: Scaling data science impact with production-grade engineering
In this session, Manasa Hari will discuss how to transform machine learning prototypes into scalable, production-grade pipelines. Attendees will learn essential engineering practices to automate, test, and deploy ML pipelines that seamlessly integrate with platform workflows, maximizing the impact of data science.
Presented by
Panelist
Panelist
Panelist
Moderator
Manasa Hari
Software Development Engineer III, Adobe
Building resilient machine learning pipelines is crucial for scaling AI solutions. This talk will outline the best practices for structuring ML workflows in production environments, covering Python best practices, DAG-based pipeline design, and effective packaging strategies. Manasa Hari will explore approaches for testing and deploying ML systems, ensuring seamless integration with platform workflows. Designed for platform engineers and data scientists, this session will provide practical insights into streamlining the transition from development to deployment, enabling the creation of robust and scalable AI solutions.
Building resilient ML pipelines: Scaling data science impact with production-grade engineering
In this session, Manasa Hari will discuss how to transform machine learning prototypes into scalable, production-grade pipelines. Attendees will learn essential engineering practices to automate, test, and deploy ML pipelines that seamlessly integrate with platform workflows, maximizing the impact of data science.
Presented by
Building resilient machine learning pipelines is crucial for scaling AI solutions. This talk will outline the best practices for structuring ML workflows in production environments, covering Python best practices, DAG-based pipeline design, and effective packaging strategies. Manasa Hari will explore approaches for testing and deploying ML systems, ensuring seamless integration with platform workflows. Designed for platform engineers and data scientists, this session will provide practical insights into streamlining the transition from development to deployment, enabling the creation of robust and scalable AI solutions.
Building resilient ML pipelines: Scaling data science impact with production-grade engineering
In this session, Manasa Hari will discuss how to transform machine learning prototypes into scalable, production-grade pipelines. Attendees will learn essential engineering practices to automate, test, and deploy ML pipelines that seamlessly integrate with platform workflows, maximizing the impact of data science.
Presented by
Host
Manasa Hari
Software Development Engineer III, Adobe
Panelist
Panelist
Panelist
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