International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 04 | Apr 2024
p-ISSN: 2395-0072
www.irjet.net
Cloud Deployed Machine Learning Model Selector Sanjivani Adsul 1, Piyush Ghante2, Avdhoot Fulsundar3, Satwik Divate3, Vaishnavi Dalvi4, Janhavi Gangurde6 123456Vishwakarma Institute of Technology, Pune, India
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Abstract - Selecting the most suitable machine learning
precision, and recall provide valuable insights into model performance, they may not always capture the nuances of real-world applications. Furthermore, existing model selection systems often offer limited support for exploring different model types, lack flexibility in hyperparameter tuning, and provide inadequate guidance for users in navigating the model selection process effectively. In light of these challenges, there is a growing demand for robust and user-friendly tools that can assist practitioners in selecting the most suitable machine learning models for their specific tasks. In this paper, we introduce a novel cloud-based machine learning model selector designed to address the limitations of existing systems. Our system aims to democratize access to machine learning algorithms by providing users with a comprehensive suite of tools for exploring, evaluating, and selecting models tailored to their needs. By leveraging the scalability and flexibility of cloud computing, we seek to empower users with the resources and insights necessary to navigate the complexities of model selection and accelerate the adoption of machine learning techniques across diverse applications.
model for a given task is a critical yet challenging aspect of data analysis. Existing model selection systems often present significant limitations, including a lack of flexibility, inadequate evaluation metrics, and poor usability. These shortcomings hinder users from making informed decisions, leading to suboptimal model performance and wasted computational resources. In this paper, we address these challenges by introducing a novel cloud-based machine learning model selector. Our system aims to empower users with a comprehensive suite of tools, allowing them to seamlessly explore various model types, upload datasets, and access robust evaluation metrics and visualization capabilities. By providing a user-friendly interface and flexible customization options, our solution promises to revolutionize the model selection process. This paper highlights the shortcomings of existing systems and introduces our proposed solution, which offers a streamlined and efficient approach to model selection in machine learning. Key Words: Cloud computing, Machine learning, Model selection, Scalability, , Optimization.
2.LITERATURE REVIEW
1.INTRODUCTION
The literature on machine learning model selection encompasses a diverse range of topics, methodologies, and tools aimed at addressing the challenges associated with choosing the most suitable algorithm and hyperparameters for a given task. Caruana and Niculescu-Mizil (2006) conducted an empirical comparison of supervised learning algorithms, shedding light on the performance characteristics of different models. Subsequent works by Bischl et al. (2012) and Feurer et al. (2015) focused on developing efficient and robust automated machine learning frameworks, while Bergstra and Bengio (2012) and Thornton et al. (2013) proposed algorithms for hyperparameter optimization. Additionally, studies by Raschka and Mirjalili (2019) and Pedregosa et al. (2011) introduced popular machine learning libraries, such as scikit-learn, facilitating model selection and evaluation in Python. Interpretability and visualization of model predictions were addressed by Lundberg and Lee (2017), while Demšar (2006) provided statistical comparisons of classifiers across multiple datasets. Other notable contributions include the development of distributed computing frameworks like Hadoop (Shvachko et al., 2010) and hyperparameter optimization libraries such as Hyperopt (Bergstra et al., 2013). The WEKA software suite (Hall et al.,
Machine learning (ML) has become an indispensable tool across various domains, empowering businesses and researchers to extract insights, make predictions, and automate decision-making processes. At the heart of any successful machine learning endeavor lies the critical task of selecting an appropriate model that can effectively capture the underlying patterns in the data. However, this process is riddled with challenges, stemming from the sheer diversity of available algorithms, the complexity of datasets, and the need to optimize model performance for specific tasks. The landscape of machine learning algorithms is vast and continually expanding, encompassing a wide array of techniques ranging from classical methods like linear regression to sophisticated deep learning architectures. Each algorithm comes with its own set of assumptions, hyperparameters, and computational requirements, making the task of selecting the right model a daunting one for practitioners and researchers alike. Moreover, the efficacy of a model often hinges on its ability to generalize well to unseen data, posing additional challenges in model evaluation and selection. Compounding these challenges is the lack of standardized practices and tools for model evaluation and comparison. While metrics such as accuracy,
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