{"id":1237,"date":"2022-01-25T19:49:38","date_gmt":"2022-01-25T19:49:38","guid":{"rendered":"https:\/\/www.grid.ai\/?p=1237"},"modified":"2022-01-25T19:49:38","modified_gmt":"2022-01-25T19:49:38","slug":"avoid-building-and-maintaining-an-mlops-training-environment","status":"publish","type":"post","link":"https:\/\/lightning.ai\/pages\/community\/case-studies\/avoid-building-and-maintaining-an-mlops-training-environment\/","title":{"rendered":"Avoid Building and Maintaining an MLOps Training Environment"},"content":{"rendered":"

The Company:<\/h2>\n

AutoDevTech<\/a> helps teams write better code by understanding code coverage, churn, and engagement. Using sophisticated machine learning techniques, the AutoDevTech platform accelerates a team\u2019s efficiency by providing valuable insights into their development process in the context of industry norms, systematically exposing teams to best practices from some of the most well-crafted software.<\/p>\n

Nick Gerner<\/a>, AutoDevTech\u2019s CEO\/Founder and builder of many engineering teams, and <\/span>Bora Banjanin<\/a>, AutoDevTech\u2019s lead Applied Scientist are tasked with the complex task of<\/span> preserving the knowledge from the past and providing that knowledge to new engineers.<\/span><\/p>\n

\"\"<\/p>\n

[AutoDevTech’s Review Assistant]<\/em><\/p>\n

The Challenge:<\/b><\/h2>\n

AutoDevTech\u2019s objective is to help write and validate code, making engineers more efficient. When it came time to scale their machine learning training, they quickly realized they themselves needed to be more efficient. They saw using Grid as an opportunity to avoid the complexity of building and maintaining their own MLOps training environment.<\/span><\/p>\n

Prior to using Grid, the AutoDevTech team focused on traditional classic statistical regression models. They wanted to leverage more sophisticated models and evaluated infrastructure solutions such as Horovod and Sagemaker which they realized would require a greater engineering effort to achieve the desired state-of-the-art performance.<\/span><\/p>\n

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“Looking into a service like Grid, we wanted to be using more sophisticated methods and the only way to do that was with large scale distributed training.\u201d – Bora Banjanin, Applied Scientist, AutoDevTech<\/i><\/strong><\/p>\n<\/blockquote>\n

 <\/p>\n

The team benefited from:<\/span><\/p>\n