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Building Machine Learning Pipelines

DevFest Melbourne 2019

A machine learning project usually includes many moving parts, such as data processing, model training, model inference and model deployment. Because of the iterative and exploratory nature of developing a machine learning project, using a pipeline can make development faster and more effective.

Similar to the ETL process for data, machine learning projects can also have a development pipeline that pre-defines high-level building blocks. These building blocks work to establish a clear structure for the machine learning workflows. Through using pipelines, a large amount of the machine learning workflow can be automated, improvements to the models performance can be tracked and collaboration between engineers is simplified.

This talk will discuss some high-level building blocks in pipelines for ML projects: the concepts of DAG (Directed Acyclic Graph) and how it relates to machine learning pipelines; experience of building ML pipelines for our current project, with light-weight framework like `consecution` in Python and comprehensive framework like `Tensorflow Extended` with `Airflow`.

Xin Liang

Machine Learning Engineer

Eliiza

Australia

Xin is a machine learning engineer at Eliiza, who has considerable experience in both software engineering and machine learning. She has worked with technologies such as deep learning, computer vision, natural language processing, signal processing, web and product development. With her great passion for Artificial Intelligence, she is focusing on using her engineering skills to develop, build, productionise and scale machine learning solutions.