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How can feedback improve Machine Learning professionals' understanding of business needs?

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Machine learning (ML) is a powerful and versatile skill that can be applied to various business problems. However, to create effective and valuable ML solutions, you need to understand the needs and expectations of your clients, stakeholders, and users. Feedback is an essential tool that can help you achieve this goal. In this article, we will explore how feedback can improve your understanding of business needs, and how you can collect and use feedback throughout the ML lifecycle.

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Sanjay Kumar MBA,MS,PhD

1 Feedback and business needs

Feedback is the information you receive from others about your ML work, such as its performance, usability, relevance, and impact. Feedback can help you understand the business needs of your project, which are the specific problems, goals, and requirements that your ML solution should address. By understanding the business needs, you can align your ML work with the value proposition and the desired outcomes of your project. Feedback can also help you identify and prioritize the most important and urgent business needs, and avoid wasting time and resources on irrelevant or low-value tasks.

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Sanjay Kumar MBA,MS,PhD
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Feedback in machine learning involves information about your work's performance, usability, relevance, and impact. It aids in comprehending the project's business needs, which are the precise problems, goals, and requirements your ML solution should tackle. This understanding aligns your ML efforts with the project's value proposition and desired outcomes. Feedback also helps identify and prioritize critical business needs, ensuring resources are not wasted on irrelevant or low-value tasks.

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Marco Antonio S.

Data Science Lead LATAM | MSc. Data Science King's College London UK
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I would say business feedback and need is the "model's business optimization function". Every develop as Data Scientist/ Machine Learning Engineers should be aligned to the business need otherwise it will not have any value or a reason to exist. Unless you're a research team worried more in creating the next big thing without worrying too much in the short term usability and business impact. Business feedback will tell you if you are tackling the correct problem and measuring the impact with he correct metrics.

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2 Feedback sources and methods

Gaining an understanding of the business needs of your ML project can be done through various sources and methods of feedback. Interviews, surveys, observations, and experiments are all common options. Interviews can provide in-depth insights and help build rapport with feedback providers. Surveys can measure and compare the satisfaction, usefulness, and impact of the ML solution across different segments. Observations can reveal the actual behavior, challenges, and opportunities of feedback providers. Lastly, experiments can be used to test different versions or features of the ML solution and measure their effects on key metrics. Collectively, these strategies can help you optimize and improve your ML solution based on empirical evidence and feedback.

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Sanjay Kumar MBA,MS,PhD
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Understanding the business needs of your ML project involves various feedback sources and methods. These include interviews for in-depth insights and rapport building, surveys to measure satisfaction and impact across segments, observations to identify behavior and opportunities, and experiments to test different ML solution versions and their effects on key metrics. These strategies enable data-driven improvements and optimization based on empirical evidence and feedback.

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3 Feedback stages and cycles

Collecting and using feedback at different stages and cycles of your ML project can be beneficial. In the discovery phase, feedback can help define the scope, objectives, and criteria of your ML project, as well as generate hypotheses and ideas for your ML solution. During development, feedback can validate or invalidate hypotheses and ideas, allowing you to iterate and refine your ML solution. After deployment, feedback can assess the impact, value, and sustainability of your ML solution. It can also help measure and communicate the results and benefits of your ML solution, while identifying any issues or gaps that may arise.

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4 Feedback challenges and best practices

Collecting and using feedback to understand the business needs of your ML project is not always easy or straightforward, and you may face challenges such as feedback quality, quantity, and timing. To overcome these issues, you can follow some best practices, such as feedback planning, communication, and analysis. Feedback planning involves deciding who, what, when, where, why, and how you will collect and use feedback. Feedback communication helps explain the purpose and expectations of your feedback. Feedback analysis allows you to identify key themes, insights, and patterns from the feedback. By collecting and using feedback throughout the ML lifecycle, you can create ML solutions that are more relevant, useful, and impactful for clients, stakeholders, and users.

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5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How can feedback improve Machine Learning professionals' understanding of business needs?

1

2

3

4

5

1 Feedback and business needs

2 Feedback sources and methods

3 Feedback stages and cycles

4 Feedback challenges and best practices

5 Here’s what else to consider

Machine Learning

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How can feedback improve Machine Learning professionals' understanding of business needs?

1

2

3

4

5

1 Feedback and business needs

2 Feedback sources and methods

3 Feedback stages and cycles

4 Feedback challenges and best practices

5 Here’s what else to consider

Machine Learning

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