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Welcome to an exploration of machine learning (ML) deployment! In this article, we'll demystify the strategic approach to successfully deploying ML projects, unlocking the potential to improve operational performance. Whether you're a business professional or a technical practitioner, join me as we delve into the gold-standard, six-step practice of bizML and gain a new perspective on ML deployment. Let's harness the power of ML to drive real-world impact!

Why Machine Learning Needs a Specialized Business Practice

Discover the essentiality of a specialized business practice for successful ML deployment.

Machine learning is a powerful technology that can only drive improvements at scale by changing operational processes. To achieve tangible business impact, ML projects need to be viewed and approached as business initiatives rather than purely technical endeavors. In this section, we'll explore why ML needs a specialized business practice to ensure successful deployment and improved operational performance.

By reframing ML projects as strategic business initiatives, we can establish a disciplined approach known as bizML. This comprehensive, six-step practice serves as the gold-standard framework for executing ML projects from conception to deployment. Let's dive deeper into the reasons behind the need for a specialized business practice and discover how it lays the foundation for successful ML deployment.

Who Can Benefit from a Business-Oriented Approach to ML Deployment

Explore the diverse set of professionals who can successfully leverage a business-oriented ML deployment approach.

With ML deployment no longer confined to a narrow technical focus, a business-oriented approach opens up new possibilities across various professional roles. In this section, we'll discuss the wide range of professionals who can benefit from adopting the bizML approach.

1. Business Professionals

If you're an executive, manager, consultant, or decision-maker responsible for running ML projects, embracing the bizML practice will empower you to drive operational transformations. By deploying ML successfully and maximizing its value, you can revolutionize how your organization operates and stays ahead of the competition.

2. Technical Practitioners

For data scientists, ML engineers, and other technical practitioners, the bizML approach offers a holistic perspective beyond the technical aspects of ML implementation. By understanding the fundamental business-oriented framework, you can align your technical contributions with the organizational goals, resulting in greater effectiveness and project success.


Machine learning has tremendous potential for improving operational performance, but it requires a specialized business practice to fully unlock its benefits. By adopting the bizML approach, organizations can strategically deploy ML projects, turning them into impactful business initiatives. The six-step discipline of bizML guides professionals in successfully developing and deploying ML models, ensuring that they align with business objectives and deliver real value.

Business professionals and technical practitioners alike can benefit from understanding the strategic framework of bizML. It empowers business leaders to drive transformational change and provides technical experts with the necessary business-oriented perspective to contribute effectively. With a business practice tailored for ML deployment, we can overcome the common challenges that lead to project failures and foster innovation in the ever-evolving world of machine learning.


What is the purpose of a specialized business practice for ML deployment?

The purpose of a specialized business practice for ML deployment is to view ML projects as strategic business initiatives rather than purely technical endeavors. It ensures that ML is not just a technology project but a means to improve operational performance, with ML as a necessary but not sufficient component.

Who can benefit from a business-oriented approach to ML deployment?

A business-oriented approach to ML deployment benefits a wide range of professionals, including executives, managers, consultants, decision-makers, data scientists, and ML engineers. It empowers business professionals to drive transformations and helps technical practitioners align their contributions with organizational objectives.