You’re a Machine Learning professional. How can you become a better leader?

Something that's underemphasized when it comes to communication is being able to prioritize what things are important to the audience and what are not. I'd argue that this is even more critical than being able to explain a technical topic to a non-technical audience.

As a Machine Learning professional, becoming a better leader involves continuous learning, open knowledge sharing, and leading by example with collaborative coding. Bridge the communication gap between tech intricacies and clear language. Encourage curiosity, embrace experimentation, and view failures as growth opportunities. Celebrate victories, big and small, boosting team morale. Trust your team's adaptability, foster autonomy, and remember, leadership is about guiding collective potential. Stay curious, share generously, and evolve your leadership style in our dynamic field.

Becoming a Trusted Advisor should be one of the key goals to every machine learning professional. Your audience are not always technical, simplifying Sentences into simple understandable figures/flow charts has worked wonders for me. For non-technical audiences It doesn't matter which algorithms you used, which one succeeded matters the most. Once you build a trust it's easier to connect and communicate your technical findings as well.

Effective communication is vital for machine learning professionals. It involves the ability to convey complex ideas and results clearly and concisely, ensuring understanding and appreciation among the audience. Active listening, asking pertinent questions, and offering constructive feedback are also essential components of effective communication. Beyond information sharing, communication fosters trust, rapport, and collaboration in the field of machine learning.

Communication could be: 1. Presenting at pyData 2. Hosting webinars or participating in paper reading sessions 3. Writing blogs and sharing your learnings on social platforms. Medium, Substack, Linkedin, can be a good starting point

Staying up to date with latest trends in AI and ML is a must. To keep up with the pace, I make sure to regularly get hands on with online courses or specializations. In addition, I personally prefer to balance technical and non-technical programs. Some examples that helped me include courses on Deep Learning, Generative AI as well as Strategy and Leadership. I am always trying to find the right balance between the two based on project needs so that the learning can be practically implemented and tested. Moreover, sharing this knowledge on common forums could help benefit other team members. We usually conduct Data Science "Chapters" with demos and Q&As and extend the invite to cross functional team members.

In machine learning leadership, strategic thinking is essential. It involves more than just model building; it's about problem-solving and value creation. To excel, align your machine learning efforts with your organization's or client's overall vision and mission. Understand the business context, stakeholder needs, and expectations, and translate them into clear, measurable objectives and metrics. Efficiently plan and prioritize machine learning projects and resources, and assess and communicate the impact and outcomes of your solutions.

I cannot emphasize more on this point. In my 8 years of experience in ML, I have realized that ML projects are successful when you have a strong relationship with teams not doing ML. They are your Data Engineers, DevOps Engineers, IT Security & Network Engineers, Platform & Solution Architects and finally Product Managers. Only then you can achieve a well architectured and reliable solution deployed at scale. I focus on building strong relationships with them and make sure they are a part of the journey since day one, when you are in your experimentation/proof of concept phase. Developing a well designed framework requires the right balance of art and science which is possible due to this cross functional collaboration.