Essays about: "MLOps"

Showing result 1 - 5 of 6 essays containing the word MLOps.

  1. 1. Investigate the challenges and opportunities of MLOps

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Ting Chun Yau; [2023]
    Keywords : Machine Learning Operations MLOps ; Continuous Integration; Continuous Delivery; Pipeline Automation; MLOps; Kontinuerlig integration; Kontinuerlig leverans; Pipeline Automation;

    Abstract : MLOps is becoming a widespread practice in modern machine learning and data science. The word ”MLOps” combines machine learning technology and business operation process. Many business companies are applying machine learning techniques to improve their business targets and increase their profits. READ MORE

  2. 2. An Empirical Study on AI Workflow Automation for Positioning

    University essay from Linköpings universitet/Programvara och system

    Author : Hannes Jämtner; Stefan Brynielsson; [2022]
    Keywords : MLOps; ML; AI; Machine Learning; Artificial Intelligence; Radio Antenna Positioning; MLOps Maturity Levels; Radio Access Network; RAN; Kubeflow; KServe; DVC; Data Version Control; MinIO; Kubernetes; Kind; Docker;

    Abstract : The maturing capabilities of Artificial Intelligence (AI) and Machine Learning (ML) have resulted in increased attention in research and development on adopting AI and ML in 5G and future networks. With the increased maturity, the usage of AI/ML models in production is becoming more widespread, and maintaining these systems is more complex and likely to incur technical debt when compared to standard software. READ MORE

  3. 3. An Open-Source Framework for Large-Scale ML Model Serving

    University essay from Uppsala universitet/Avdelningen för beräkningsvetenskap

    Author : Petter Sigfridsson; [2022]
    Keywords : Model Serving; Machine Learning; MLOps; DevOps; Distributed Computing Infrastructure; System Scalability and Performance; Cloud Infrastructure; Open-Source Software; Horizontal Scalability; Data Engineering; Cloud Computing; AI; Data Science;

    Abstract : The machine learning (ML) industry has taken great strides forward and is today facing new challenges. Many more models are developed, used and served within the industry. Datasets that models are trained on, are constantly changing. READ MORE

  4. 4. Evaluation of MLOps Tools for Kubernetes : A Rudimentary Comparison Between Open Source Kubeflow, Pachyderm and Polyaxon

    University essay from Uppsala universitet/Institutionen för informationsteknologi

    Author : Anders Köhler; [2022]
    Keywords : MLOps; machine learning; Kubernetes; cloud computing; cloud native; Kubernetes native; MLOps; maskininlärning; Kubernetes; molnberäkning; cloud native; Kubernetes native;

    Abstract : MLOps and Kubernetes are two major components of the modern-day information technology landscape, and their impact on the field is likely to grow even stronger in the near future. As a multitude of tools have been developed for the purpose of facilitating effortless creation of cloud native MLOps solutions, many of them have been designed, to varying degrees, to integrate with the Kubernetes system. READ MORE

  5. 5. Increasing Reproducibility Through Provenance, Transparency and Reusability in a Cloud-Native Application for Collaborative Machine Learning

    University essay from Uppsala universitet/Avdelningen för datorteknik; Uppsala universitet/Avdelningen för datorteknik

    Author : Adam Ekström Hagevall; Carl Wikström; [2021]
    Keywords : Machine Learning; MLOps; STACKn; Reproducibility; Replicability; Provenance; Transparency; Reusability; Kubernetes; Cloud-Native; Open Source; Software Engineering;

    Abstract : The purpose of this thesis paper was to develop new features in the cloud-native and open-source machine learning platform STACKn, aiming to strengthen the platform's support for conducting reproducible machine learning experiments through provenance, transparency and reusability. Adhering to the definition of reproducibility as the ability of independent researchers to exactly duplicate scientific results with the same material as in the original experiment, two concepts were explored as alternatives for this specific goal: 1) Increased support for standardized textual documentation of machine learning models and their corresponding datasets; and 2) Increased support for provenance to track the lineage of machine learning models by making code, data and metadata readily available and stored for future reference. READ MORE