Embarking on the journey to become an AWS Machine Learning Hero is a significant aspiration for many in the tech community. It’s a recognition of deep expertise, community contributions, and a passion for sharing knowledge within the AWS ecosystem, particularly in the rapidly evolving field of machine learning. My own path to achieving this prestigious title involved dedication, community engagement, and a focus on content creation and public speaking. In this article, I’ll detail the steps and strategies that paved my way to becoming an Aws Machine Learning Hero, offering insights and guidance for those who aspire to follow a similar path.
The AWS Heroes program is designed to celebrate and acknowledge individuals who are not only experts in AWS technologies but also actively contribute to the community. These are developers who go above and beyond in sharing their knowledge, helping others, and making a significant impact. AWS Heroes are readily identifiable through their presentations at industry events and their valuable online contributions. This diverse group of professionals from around the globe brings a wealth of skills and experiences, making the AWS Heroes program a truly remarkable collective of talent. It’s an honor to be counted among them.
Screenshot of Brooke
Within the AWS Heroes program, various categories exist, including Community, Container, Data, DevTools, IoT, Machine Learning, and Serverless. These categories represent the diverse expertise within the AWS ecosystem. As of this writing, I am one of a select group of AWS Machine Learning Heroes worldwide, a truly humbling distinction. My aim here is to shed light on my journey and offer actionable advice to those aiming to achieve similar recognition in the field of AWS and machine learning.
Key Steps to Becoming an AWS Machine Learning Hero
The Concise Answer: Diligent and persistent effort is paramount.
The Extended Explanation: My journey was significantly shaped by three core components: active participation in the AWS Community Builders program, consistent content creation focused on AWS machine learning, and gaining experience as a global conference speaker.
It’s important to note that the following is based on my personal experience and observations. While I don’t have insider knowledge of the selection process, the official AWS Heroes page outlines key attributes of an AWS Hero, which include:
- Enthusiasm: Regular and active engagement within the AWS community.
- Expertise: Deep knowledge of AWS services and a commitment to staying updated with the latest trends, especially in machine learning.
- Leadership: Building and nurturing relationships, strengthening community bonds, and guiding others in their AWS journeys.
While my approach may not be universally applicable, it provides a solid starting point for aspiring AWS Machine Learning Heroes.
Leveraging the AWS Community Builders Program for Machine Learning Growth
The AWS Community Builders program is an invaluable resource for anyone looking to deepen their AWS expertise and community involvement. This program was instrumental in my professional growth, particularly in the machine learning domain. While applications may be periodically closed, joining the waitlist is highly recommended. For a detailed perspective on joining, Stephen Sennett’s article ‘How to Become an AWS Community Builder’ offers excellent guidance.
My year as an AWS Community Builder reinforced a crucial understanding: the program emphasizes community contribution just as much as technical proficiency. It’s about actively sharing knowledge and engaging with the community, both online and offline. Don’t be discouraged by perceived technical gaps; focus on your willingness to learn and share. Conversely, exceptional technical skills alone may not suffice if you’re not actively contributing to the community.
In essence, mastering AWS machine learning is only part of the equation. Effectively communicating and sharing that expertise within the AWS community is equally vital to becoming an AWS Community Builder and, subsequently, an AWS Machine Learning Hero.
Content Creation: Sharing Your AWS Machine Learning Expertise
In the tech world, continuous learning is essential, and content creation is a powerful tool for both learning and sharing. While many developers may delay starting, creating even “Minimum Viable Blog Posts” is incredibly beneficial. Prior to joining the AWS Community Builders program, I was already involved in content creation, including personal projects and ghostwriting. However, the program provided further encouragement to amplify my output, especially in the area of AWS machine learning.
My content platforms include dev.to, LinkedIn, Twitter, and TikTok. It’s important to reiterate that content creation isn’t a formal prerequisite to becoming an AWS Hero, but it was a significant part of my journey.
My motivation for creating content stems from genuine enjoyment and a desire to connect with the community. My TikTok content, including AWS-themed augmented reality filters like the ‘Which AWS Service Are you?’ filter and the AWS Summit Crown, exemplifies this. These weren’t commissioned or directly related to becoming an AWS Hero, but they were fun to create and served as engaging community touchpoints. Focus your content on areas you are passionate about within AWS machine learning. Consider tutorials on using Amazon SageMaker, guides to deploying machine learning models, or insights into the latest AWS ML service updates.
Conference Speaking: Amplifying Your Voice in the AWS Machine Learning Community
It’s crucial to emphasize that neurodiversity or anxiety should not be barriers to conference speaking. Despite not being naturally extroverted, I’ve delivered over 40 conference talks. Initially, even attending events was challenging, but gradually, I built confidence to ask questions and eventually present. It was a gradual but rewarding process. For those interested in starting conference speaking, my NDC Sydney talk, ‘How to Become a Tech Conference Speaker,’ available on YouTube, provides a comprehensive guide.
Speaking at conferences is not only a fantastic way to hone communication skills but also to deeply engage with the community. Speaker passes often grant access to entire events, allowing for extensive learning and networking opportunities. I’ve gained immense knowledge from attending other sessions and built invaluable relationships within the community. Conference speaking has also opened doors to international travel, an unexpected and enriching aspect of this journey. Starting may seem daunting, but the rewards are substantial. Consider focusing your talks on AWS machine learning topics, sharing practical insights, case studies, or tutorials. This not only demonstrates your expertise but also provides valuable learning opportunities for the audience.
Bringing It All Together for AWS Machine Learning Hero Status
The overarching theme in becoming an AWS Machine Learning Hero is demonstrating leadership within the AWS community, not just excelling privately. It’s about creating high-quality, impactful content and positively contributing to the global AWS community, specifically in the realm of machine learning. Actively participate in the AWS Community Builders program and engage with the Community & Developer Advocate teams in your region or area of expertise. Attend and present at local AWS User Groups. Most importantly, enjoy the process of learning, sharing, and connecting with the vibrant AWS machine learning community.
There’s no guaranteed formula, but this approach worked for me. If you have questions, feel free to ask in the comments below.
About the Author: Brooke Jamieson is an AWS Machine Learning Hero based in Brisbane, Australia. To delve deeper into their journey as an AWS Developer, watch this video or explore their profiles on LinkedIn, Twitter, Dev.to and TikTok.
