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What do you do if your relationships with key stakeholders in the Machine Learning field are strained?

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What do you do if your relationships with key stakeholders in the Machine Learning field are strained? This is a common challenge for many Machine Learning professionals who need to collaborate with various teams, clients, and managers to deliver successful projects. In this article, you will learn some practical tips on how to improve your communication, alignment, and trust with your key stakeholders and boost your career prospects in the Machine Learning field.

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Deepak Gaur

Lead Scientist | AI | CV | NLP | DS | Aerospace & Astronautics | Dynamical Systems | Astrodynamics | LinkedIn Top Voice…
Sidharth Ramachandran

Data Scientist at Air India | Artificial Intelligence | Machine Learning | Deep Learning | NLP | Computer Vision |…

1 Identify your key stakeholders

The first step is to identify who are your key stakeholders in the Machine Learning field and what are their roles, expectations, and needs. A stakeholder is anyone who has an interest or influence in your Machine Learning project, such as a business owner, a domain expert, a data engineer, a product manager, or a user. You can use a stakeholder analysis matrix to map out their level of power, interest, and engagement in your project and prioritize your communication and collaboration efforts accordingly.

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2 Understand their perspectives

The next step is to understand the perspectives of your key stakeholders and how they view your Machine Learning project. Different stakeholders may have different goals, assumptions, preferences, and concerns about your project, such as its scope, quality, feasibility, impact, or risks. You can use various techniques to empathize with your stakeholders, such as interviews, surveys, feedback sessions, or user stories. By understanding their perspectives, you can tailor your Machine Learning solutions to meet their needs and expectations and avoid potential conflicts or misunderstandings.

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Deepak Gaur

Lead Scientist | AI | CV | NLP | DS | Aerospace & Astronautics | Dynamical Systems | Astrodynamics | LinkedIn Top Voice 2024 - Aerospace Engineering, AI, Machine Learning, Data Science, Computer Vision, NLP
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When relationships with key stakeholders in the Machine Learning field are strained, understanding their perspectives and how they view your project becomes crucial for fostering reconciliation and collaboration. By empathetically considering their viewpoints, concerns, and expectations, you can identify areas of misalignment and work towards finding common ground. This understanding allows you to tailor your communication, adjust project goals, and implement changes that address their specific needs and priorities. Building trust and demonstrating a genuine commitment to addressing their concerns can help rebuild relationships, enhance cooperation, and ultimately lead to more successful outcomes for the Machine Learning project.

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3 Communicate effectively

The third step is to communicate effectively with your key stakeholders and keep them informed and involved in your Machine Learning project. Communication is essential for building trust, alignment, and support among your stakeholders and ensuring that your project is on track and meets the desired outcomes. You can use various tools and methods to communicate with your stakeholders, such as emails, reports, presentations, dashboards, or demos. You should also consider the frequency, format, and content of your communication and adapt it to the needs and preferences of your stakeholders.

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4 Manage expectations

The fourth step is to manage the expectations of your key stakeholders and set realistic and clear goals and boundaries for your Machine Learning project. Expectations are the beliefs or assumptions that your stakeholders have about your project, such as its scope, timeline, budget, quality, or impact. You should align your expectations with your stakeholders and make sure that they are aware of the limitations, challenges, and trade-offs of your Machine Learning project. You should also monitor and update your expectations as your project progresses and deal with any changes or issues promptly and transparently.

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Sidharth Ramachandran

Data Scientist at Air India | Artificial Intelligence | Machine Learning | Deep Learning | NLP | Computer Vision | Data Engineering | Fascinated by the intersection of AI and neuroscience
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In a machine learning project, managing expectations involves educating stakeholders about potential challenges in production . For example, stakeholders should be taught to recognize the phenomenon of data drift, where changes in input data characteristics can affect the model's performance over time. Clear communication aids in setting realistic expectations and empowering stakeholders to address obstacles as they arise.

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5 Seek feedback

The fifth step is to seek feedback from your key stakeholders and use it to improve your Machine Learning project and your relationship with them. Feedback is the information or opinions that your stakeholders provide about your project, such as its strengths, weaknesses, opportunities, or threats. You should solicit feedback from your stakeholders regularly and proactively and listen to their suggestions, concerns, or compliments. You should also acknowledge and appreciate their feedback and act on it accordingly and show them the results and benefits of your Machine Learning project.

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6 Build rapport

The sixth step is to build rapport with your key stakeholders and establish a positive and respectful relationship with them. Rapport is the connection or bond that you have with your stakeholders, based on mutual trust, understanding, and respect. You can build rapport with your stakeholders by showing genuine interest in them and their needs, expressing appreciation and recognition for their contributions, being honest and reliable, and finding common ground and shared values. By building rapport, you can enhance your credibility, influence, and collaboration with your key stakeholders and advance your career in the Machine Learning field.

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7 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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Jigar Joshi

Microsoft Certified Azure AI Professional | IBM Certified Enterprise Data Science Professional | IEEE Senior Member | Linked in Top Voice in Data Science | Machine Learning and Data Science Enthusiast
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To improve strained relationships with key stakeholders in the Machine Learning field, initiate a transparent dialogue to address concerns, provide regular updates on project progress, and actively seek feedback to ensure alignment with their interests and objectives; for instance, a data science team could host monthly briefing sessions with stakeholders to discuss the development and implementation of new algorithms, thus fostering a collaborative atmosphere and rebuilding trust.

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What do you do if your relationships with key stakeholders in the Machine Learning field are strained?

1

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3

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1 Identify your key stakeholders

2 Understand their perspectives

3 Communicate effectively

4 Manage expectations

5 Seek feedback

6 Build rapport

7 Here’s what else to consider

Machine Learning

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What do you do if your relationships with key stakeholders in the Machine Learning field are strained?

1

2

3

4

5

6

7

1 Identify your key stakeholders

2 Understand their perspectives

3 Communicate effectively

4 Manage expectations

5 Seek feedback

6 Build rapport

7 Here’s what else to consider

Machine Learning

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