Here's how you can effectively advocate for your ideas and contributions in the Machine Learning workplace.

Establish yourself as a reliable source of knowledge. Take on challenging projects, deliver strong results, and stay up-to-date on the latest advancements in ML. This builds trust in your expertise and makes your ideas more persuasive.

Let me share my personal experience on how I advocated for my idea: 1- we faced a complex PDF analysis problem. I wrote down and visualized the issue deeply, coining the term "Oppenheimer way" to describe my approach. 2 - I dissected the problem layer by layer and discussed it with the team lead, addressing each sub-issue to understand the overall flow. 3 - I proposed a carefully thought-out solution, which my team lead appreciated and encouraged me to try. The solution worked well, and my creative approach was accepted. To achieve this, I stay informed on AI news, read articles, watch interviews, review popular GitHub issues, and discuss problems with GPT & Claude AI. Continuous learning and analysis are key to solving complex problems.

To build credibility it needs deeper understanding of ML concepts keeping up with latest trends and tech updates. Understanding the use cases to suggest or evaluation of any solution or proposal is very important keeping security concerns in consideration. This will help in demonstrating the work properly and positively.

A good learner is a good listener also, inputting data and patience. ML machines to run effectively should be able to listen first. Also the machine should have the ability to filter out and interpret data.

If you do not understand a word in ML, just shut down the business. you have to know the minimum in ML. People will never trust you if you show uncredibility in your field. Building credibility happens when you work on multiple projects, study a lot about ML, and contribute your knowledge to Research papers. imagine someone who knows only the basics and another one who is experienced in his field. People will follow the second one certainly

Present your ideas in a concise, well-organized manner, using visuals and data to back up your claims. Tailor your communication style to your audience, ensuring everyone understands the value you bring. You have to make people understand what data says.

Don't just have a great idea, explain it well! Present your ideas concisely, focusing on the problem it solves and the benefits it brings. Use clear visuals and data to support your arguments. Tailor your communication style to your audience, be it technical discussions with peers or managerial presentations.

When communicating in ML, tailor your message to your audience's expertise. Start with a clear, impactful statement that highlights the importance of your idea. Use analogies to simplify complex concepts and interactive tools for complex data visualization. Encourage feedback to ensure understanding and engage your audience, making your presentations not just informative but also interactive and responsive to their needs.

You don't have to give your audience the entire history in one sitting of how your idea does or doesn't reinvent something from "conventional" analytics as an ML-based solution. Ultimately what matters is how you tackled the problem you set out to solve.

In Machine Learning, clear communication is crucial to convey complex ideas accurately. Ambiguity leads to misunderstandings and misapplications. Use plain language, visual aids, and transparency about methodology to foster trust and growth in the ML community.

Showcase the positive impact of your past contributions. Did your model improve efficiency? Did your approach reduce errors? Quantify these successes with metrics and highlight them in discussions. This demonstrates the value you bring and strengthens your arguments for future ideas.

I would also add that you should try to leverage failures. Failures can often be cultivated internally as reference case. Past failures can also be used as a source of motivation for all those tasks that are important, but can be boring for some people (like some types of testing, or security protocols etc.). A good past failure can give the boost to do that extra mile.

Leverage success is important, for me learning from failed projects gives a great perspective on what not to do ! Bottlenecks act as a source of experience and comes handy while designing solutions around ML

To leverage success, systematically document and present your project outcomes, emphasizing the methodologies and ML techniques that drove success. Share case studies or white papers with stakeholders to illustrate the tangible benefits and scalability of your approaches. Celebrate team achievements publicly to foster a culture of innovation and collaboration, thereby enhancing support for future projects and positioning your contributions as valuable assets.

Leveraging your successful ML projects to advocate for future initiatives can be a good strategy. Connect your previous achievements with your future ideas to demonstrate a clear path to further success. Showcase the positive outcomes and highlight the specific ML techniques that drove success. This showcases your expertise, the tangible benefits of your work, and a proven strategy for delivering results, making it easier to gain support for your proposals.

Internal networking within your ML workplace is crucial for amplifying your advocacy efforts. Engage with colleagues across departments, aiming to understand their challenges and perspectives from a business value standpoint. By empathizing with their goals and tailoring your ML solutions accordingly, you not only make your ideas more appealing but also foster genuine connections that can turn colleagues into allies. These allies can champion your projects and increase their visibility throughout the organization.

Build relationships with colleagues across different teams. Discuss your ideas with them, get their feedback, and understand their challenges. This not only builds a support network but also helps you identify potential applications for your ideas within the organization.

ML solutions involve many skills, spend time with your team to understand their perspective and work to make yours clearer. This is the only path to get an holistic view and build effective solutions.

A support network is needed to exchange ideas, contribute to newer technologies and keep yourself updated with the latest changing trends. Network on linkedin, or on whatsapp group

Effective internal networking involves actively participating in cross-departmental meetings and company events. Establish genuine relationships by offering ML insights that benefit different teams. Act as a resource, helping others understand how ML can solve specific problems. This not only broadens your influence but also ensures your projects receive wider recognition and support, creating a network of advocates for your work across the organization.

Don't be afraid to ask for feedback on your ideas, both before and after proposing them. This shows openness to improvement and allows you to refine your approach based on valuable insights from colleagues. Openness to feedback also fosters a collaborative environment where everyone feels comfortable sharing ideas.

Proactively ask for feedback from mentors or senior colleagues. This demonstrates your willingness to learn and helps you refine your ideas for better reception.

Actively seek feedback to refine your ML ideas and contributions. Approach peers or mentors for input on your projects or proposals to uncover blind spots and gain valuable suggestions. Involving others in the development process creates a sense of shared ownership, making them more likely to support you when you advocate for your work.

Choose the right moment to present your ideas. Consider upcoming deadlines, project stages, and leadership availability. Well-timed proposals have a higher chance of getting the attention and resources they deserve.

Align your proposals with the organization's priorities, such as presenting ideas during strategic planning or innovation-focused periods. Be mindful of your team's workload to ensure your proposals are well-received.

There is no single action you can take which will guarantee that your ideas will be accepted, rather it is an iterative process which enables your proposals to reach the top. Without credibility no one will take your ideas seriously, know your field and confidently defend your proposals. If you can't communicate your ideas effectively, no one will find out about them, work on your communication skills and iterate with colleagues. Without trust of your colleagues no one will support your ideas when it comes time to decide which projects to proceed with. Finally, understand the language of business, you need to sell your proposal not only technically, but in terms of business impact and current market conditions for your company.

Apart from all the above-mentioned points Be Passionate and Enthusiastic: Your passion for your ideas is contagious. When you demonstrate genuine enthusiasm for your contributions, it motivates others to listen and take your ideas seriously. Always try to look after how you can integrate your passion with your skillset to get the best of both worlds. Good Luck with your learning journey ??