Accelerating the Machine Learning Lifecycle with MLflow

By Matei A. Zaharia, Andrew Chen, A. Davidson, A. Ghodsi, S. Hong, Andy Konwinski, Siddharth Murching, Tomas Nykodym, Paul Ogilvie, Mani Parkhe and 2 more. IEEE Data Engineering Bulletin, 2018

559 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/b2e0b79e6f180af2e0e559f2b1faba66b2bd578a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Bo Li
2. Percy Liang
3. Mohit Bansal
4. Yoshua Bengio
5. Matei A. Zaharia
6. Weixin Liang
7. Li Fei-Fei
8. Kyunghyun Cho

Citations in the corpus

• Advances, challenges and opportunities in creating data for trustworthy AI (2022, methodology): “Example tools include open-source packages such as TFX and MLflow 88,89 .”
• Challenges in Deploying Machine Learning: A Survey of Case Studies (2020, background): “Examples include AWS SageMaker [144], Microsoft ML [7], Uber Michelangelo [57], TensorFlow TFX [18], MLlow [154] and more.”
• Trustworthy AI: From Principles to Practices (2021, background): “Based on this methodology, an increasing number of ML platforms are being developed that assist researchers and developers better track the lifecycle in a reproducible manner [169, 374].”
• Caliban: Docker-based job manager for reproducible workflows (2020, background): “Projects like MLFlow (Zaharia et al., 2018) attempt to streamline the container creation process, but still force the researcher to absorb much of Docker’s mental model.”
• Lakehouse: A New Generation of Open Platforms that Unify Data Warehousing and Advanced Analytics (2021, methodology): “For example, at Databricks, we have integrated Delta Lake with the ML experiment tracking service in MLflow [52] to let data scientists easily track the table versions used in an experiment and reproduce that version of the data later.”