Implementing Artificial Intelligence and Machine Learning: Strategies, Tools, and Best Practices
Author: Dr. Anna Neya Kazanskaia
Publisher: NEYA Global Publishing
Article | NEYA Global Journal of Non-Profit Studies
Year: 2025
ORCID: https://orcid.org/0009-0009-5669-1676
DOI: https://doi.org/10.64357/neya-gjnps-ai-mch-lr-06
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About the Article
Effective implementation of artificial intelligence (AI) and machine learning (ML) requires more than technical expertise: it demands a structured lifecycle that integrates data preparation, model development, deployment, and continuous monitoring. This article outlines strategies, tools, and best practices for operationalizing AI systems responsibly and sustainably. It highlights frameworks such as TensorFlow, PyTorch, and Scikit-learn, as well as cloud platforms that support scalable deployment. Real-world applications in agriculture, healthcare, finance, and education demonstrate AI’s versatility and impact. By addressing challenges such as overfitting, infrastructural demands, and privacy risks, and by embedding monitoring, optimization, and ethical safeguards, the article offers a roadmap for academics and practitioners seeking to implement AI with scalability, efficiency, and responsibility.
Key Topics
- AI and ML implementation lifecycle
- Data preparation and model training
- Deployment and cloud-based scalability
- Continuous monitoring and optimization
- Tools and frameworks (TensorFlow, PyTorch, Scikit-learn)
- Ethical and responsible AI adoption
Suggested Citation
Kazanskaia, A. N. (2025). Implementing Artificial Intelligence and Machine Learning: Strategies, Tools, and Best Practices. NEYA Global Journal of Non-Profit Studies. Neya Global Publishing. https://doi.org/10.64357/neya-gjnps-ai-mch-lr-06
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