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Journals / Nevşehir Hacı Bektaş Veli Üniversitesi SBE Dergisi / 2024 / Cilt: 14 Sayı: 4

Overview of machine learning service strategy for business

İşletmeler İçin makine öğrenimi hizmet stratejisine genel bakış

Hasan Tahsin Aytekin(UFUK ÜNİVERSİTESİ, SOSYAL BİLİMLER ENSTİTÜSÜ)0000-0002-5632-7825
Pages
1901–1923
Publication Date
2024-12-29
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results show that MLaaS platforms offer enterprises cost-effective, scalable and flexible solutions.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Due to the increasing demand for machine learning and the need to analyse data, many companies are looking to reduce the cost of developing their own infrastructure by incorporating high-tech solutions into their processes and are turning to cloud-based solutions.
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Key Findings & Data:As a result, machine learning as a service (MLaaS) platforms have become important in terms of both cost benefits and technical requirements.
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Conclusion & Impact:The results show that MLaaS platforms offer enterprises cost-effective, scalable and flexible solutions.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Makine Öğrenimi · Hizmet Olarak Makine Öğrenimi · Hizmet Olarak Makine Öğrenimi Platformları

Abstract

Due to the increasing demand for machine learning and the need to analyse data, many companies are looking to reduce the cost of developing their own infrastructure by incorporating high-tech solutions into their processes and are turning to cloud-based solutions. As a result, machine learning as a service (MLaaS) platforms have become important in terms of both cost benefits and technical requirements. In this context, the study adopts a comparative analysis approach and examines the Amazon SageMaker, Google AI Platform, Microsoft Azure Machine Learning and IBM Watson Studio platforms. These platforms were selected based on their user base and the breadth of services they offer. Data was obtained from open data sources provided by the platform vendors. The findings show that each platform has its own advantages, but cost and technical requirements play an important role in organisations' choice of platform. Cloud-based MLaaS solutions were also found to offer cost benefits by enabling small teams to work on large datasets. The results show that MLaaS platforms offer enterprises cost-effective, scalable and flexible solutions. However, issues such as privacy and security play an important role in platform selection. Choosing the right platform and ensuring integration with existing systems is critical to the success of enterprise machine learning projects.

Özet

Bu araştırmanın amacı, Hizmet Olarak Makine Öğrenimi (MLaaS) platformlarının kurumsal makine öğrenimi projelerinin tasarım ve geliştirme ortamlarındaki etkinliğini ve kullanılabilirliğini değerlendirmektedir. Bu amaçla dört büyük MLaaS sağlayıcısına odaklanan karşılaştırmalı bir analiz yaklaşımı benimsenmiştir. Odaklanılan MLaaS platform sağlayıcıları Amazon SageMaker, Google AI Platform, Microsoft Azure Machine Learning ve IBM Watson Studio'dur. Araştırmada analiz amacıyla kullanılan veriler, ilgili platform sağlayıcıları tarafından sağlanan kamuya açık bilgilerden elde edilmiştir. Araştırma metodolojisi, toplanan verilerin tematik analizini içermekte ve makine öğrenimi ile ilgili temel özellikleri karşılaştırmaktadır. Çalışmada MLaaS'ın farklı uzmanlık ve kaynak seviyelerine sahip işletmeler için erişilebilir araçlar sağlayarak makine öğrenimi çözümlerinin uygulanmasını basitleştirmedeki rolü vurgulamakta ve MLaaS'ı benimsemenin potansiyel faydalarını ve zorluklarını tartışarak, makine öğrenimi projelerinin geliştirilmesi aşamasında bu platformlardan yararlanmayı düşünen işletmeler için içgörüler ortaya koymaktadır. Sonuç olarak MLaaS platformlarının makine öğrenimi modellerinin dağıtımıyla ilgili karmaşıklığı ve maliyeti önemli ölçüde azalttığı ve REST API'leri aracılığıyla mevcut BT altyapılarına sorunsuz bir şekilde entegre olan özel çözümler sunduğu ortaya konmuştur.

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Cite This Article

Aytekin, H. T. (2024). Overview of machine learning service strategy for business. Nevşehir Hacı Bektaş Veli Üniversitesi SBE Dergisi, Cilt: 14 Sayı: 4, 1901–1923. https://doi.org/10.30783/nevsosbilen.1521972

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