Pengembangan Pipeline Berbasis MLOps untuk Experiment Tracking pada Infrastruktur Data Science

Bayuputra, Rayhan Arvianta (2025) Pengembangan Pipeline Berbasis MLOps untuk Experiment Tracking pada Infrastruktur Data Science. Project Report. [s.n.], [s.l.]. (Unpublished)

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Abstract

Kerja praktik ini dilakukan di PT United Tractors Tbk untuk meningkatkan efisiensi siklus pembelajaran mesin melalui penerapan MLOps (Machine Learning Operations). Proyek ini difokuskan pada pengembangan pipeline machine learning modular yang terdiri dari enam lapisan, dengan implementasi MLOps yang terpusat pada experiment tracking menggunakan Databricks. Penerapan ini berhasil meningkatkan keterlacakan eksperimen, efisiensi, dan reproduktibilitas pengembangan model, sekaligus mendukung kolaborasi tim data science. Pipeline yang terstandarisasi ini diharapkan menjadi pedoman dalam pengembangan dan pengoperasian model machine learning di masa depan.
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This internship was conducted at PT United Tractors Tbk to improve the efficiency of the machine learning lifecycle through the implementation of MLOps (Machine Learning Operations). The project focused on developing a modular machine learning pipeline consisting of six layers, with MLOps implementation centered on experiment tracking using Databricks. This implementation successfully enhanced experiment traceability, efficiency, and model development reproducibility while also supporting collaboration within the data science team. The standardized pipeline is expected to serve as a guideline for the future development and operation of machine learning models.

Item Type:	Monograph (Project Report)
Uncontrolled Keywords:	MLOps, machine learning, experiment tracking, Databricks, pipeline machine learning
Subjects:	Q Science > QA Mathematics > QA336 Artificial Intelligence Q Science > QA Mathematics > QA76.758 Software engineering Q Science > QA Mathematics > QA76.9.D343 Data mining. Querying (Computer science) Q Science > QA Mathematics > QA76.9.D37 Data warehousing. Q Science > QA Mathematics > QA76.9.I52 Information visualization
Divisions:	Faculty of Information Technology > Informatics Engineering > 55201-(S1) Undergraduate Thesis
Depositing User:	Rayhan Arvianta Bayuputra
Date Deposited:	05 Feb 2025 10:25
Last Modified:	05 Feb 2025 10:25
URI:	http://repository.its.ac.id/id/eprint/118370

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