The Role of Developer Relations in AI Product Success. Lagos

Co-founder of Data Community Africa. AI/ML Developer Evangelist, Data Scientist, & Community Builder. Advisory Board Member at DevNetwork , MLOps Community Lagos Lead. Twitter: GiftOjeabulu_ Linkedin: Gift Ojeabulu Who am I? click here to learn more about me

Table of contents 01 What is Devrel, Types and roles DevRel Intro 02 Why DevRel is Essential for AI Product Success DevRel importance.. 03 Three practical case study on an API, Open source and Computer Vision Company Case Studies 04 Key Metrics for assessing devrel product success in AI DevRel KPIs 05 Tools, techniques and aligning devrel metrics with business objectives DevRel ROI & Business objectives 06 ????????? Q & A + Conclusion

What is Developer Relations?

DevRel is a group of developer advocates, technical community managers, documentation writers, and more who all exist to empower developers to do their best work. - Mary Thengvall

Developer Relations Variation Developer Advocate Developer Community Manager Technical Writer DevRel Manager Developer Programs Manager Developer Experience Engineer Developer Relations

Day-to-Day in the Life of a DevRel professional

Day-to-Day Blog Post Workshop & User feedback Crafting ML Memes & Organizing Events Coding & Contributing to open source Monday Tuesday Wednesday Thursday

DevRel Writing sample code, building demo applications, creating SDKs, or contributing to open-source projects. Acting as the voice of developers within the company by relaying feedback to product teams and advocating for features or fixes. Presenting at conferences, webinars, meetups, or hosting workshops. Organizing or participating in hackathons, developer days, or product launches. Developing blogs, tutorials, technical guides, videos, or social media content to educate developers. Working with ecosystem partners, cloud providers, or other tech companies to create integrations or joint developer programs. Setting goals, tracking metrics like developer engagement, adoption rates, or community growth, and adjusting outreach strategies. Interacting with users via forums, Discord, Slack, Twitter, or other platforms. Attending meetups or hosting virtual Q&A sessions.

DevRel Team and Functions Developer Advocate Technical Writer Community Manager DX Engineer Product Engineer Content Community Product Write & Speak Organize Event User Feedback Integrations/Build live Demos

Some Amazing AI DevRel Friends & what they do # Staff Developer Advocate Developer Advocate Senior Developer Advocate Community Manager Maria Khalusova, Unstructured.io Harpreet Sahota, Voxel51 Tony Kipemboi, CrewAI Jeny De Figuerido, Ex - DVC.ai

Some Amazing AI DevRel Friends & what they do # Staff Developer Advocate Senior Developer Advocate Senior Developer Advocate Developer Advocate Jay Miller, Aiven Marlene Maghami, Microsoft Patrick Loeber, Assembly AI Marisa Smith, Tobiko Data

Why DevRel is Essential for AI/ML Product Success

Simplifying Complexity Building Trust and Credibility with developers Creating a feedback loop Building a Thriving developer Ecosystem Showcasing Responsible AI practices Why DevRel is Essential for AI Product Success

Case Studies

Case Study 1: Driving Adoption through Targeted AI/ML Education and Content.

Case Study 0 An AI company, let’s call it AI Co., struggled with onboarding new users who were intimidated by the complexity of their API and its documentation. The API provided robust machine learning capabilities but required some knowledge of model configuration and data handling, which posed a barrier to entry for beginners or developers new to AI.

How do we overcome this as AI Co’s Devrel team?

Step-by-Step Tutorial Walkthrough: Introduce Users to API Realistic Use Cases Level-Based Content (Beginner, Intermediate, & Advanced) Video & Written Content AICo’s beginner-friendly tutorial for API Integration covers

Impact & Results The educational content made AI Co.’s complex AI tools more accessible, and adoption metrics showed a significant increase in API usage within just a few months. Developers felt empowered to start with the API without needing extensive AI/ML knowledge upfront, which lowered the barriers to entry and increased the product’s appeal to a broader audience.

“Tailored educational content can demystify complex AI/ML products and make them accessible to a wider audience. By creating content that guides users from basic to advanced levels, DevRel can drive adoption and engagement, turning hesitant users into confident, active ones.” Gift Ojeabulu

Case Study 2: Building an Engaged Open Source AI/ML Community.

Case Study 1 An open-source machine learning platform, ML Hub, relied heavily on community contributions and feedback for product improvements. However, without a structured community approach, users faced challenges, and their feedback often went unheard. The ML Hub team recognized the need for a more organized community structure to capture valuable feedback and encourage collaboration.

How do we overcome this as the ML Hub DevRel team?

Open Forums and GitHub Discussions Monthly Feedback Sessions: Users Propose New features, report bugs, Collaborative Documentation Recognitions and Awards: Contribution program for Bugs, feedback, etc. ML Hub Open Source Community-Driven Model

Impact & Results The DevRel team’s community-driven approach created a space for developers to engage deeply with ML Hub and its product evolution. By engaging the community in a structured way, ML Hub saw improvements in product usability, quicker bug resolutions, and an increase in contributors who were committed to the platform’s growth. This feedback loop also led to faster iterations and a product better aligned with user needs.

“An engaged community can be a powerful resource for AI/ML products, contributing ideas, catching bugs, and accelerating the product’s evolution. By fostering a feedback-rich environment, DevRel teams empower users to shape the product, improving it faster than internal teams alone could manage.” Gift Ojeabulu

Case Study 3: Evangelism through Real-World Use Cases in Computer Vision

Case Study 2 An AI company, let's call it Vision AI, specialized in computer vision solutions but faced challenges in demonstrating the real-world value of their products to developers and potential customers. While there was interest in Vision AI’s offerings, many developers struggled to understand how to apply the technology to specific industry problems, such as quality control in manufacturing or patient monitoring in healthcare.

How do we overcome this as Vision AI DevRel Lead?

Developing Industry-Specific Case Studies Publishing Papers and Presenting at Top Conferences CVPR, ICCV, ECCV, NEURIPS Conducting hands-on workshops such as defect detection in manufacturing. Content Marketing on Practical Impact: Success Stories across Blogs and Newsletters. Vision AI Technology Solution-Driven Model

Impact & Results From this model, Vision AI effectively built trust and established credibility within the community. These activities allowed Vision AI to connect with advanced developers, researchers, and industry stakeholders, demonstrating the tangible benefits of their product in real-world scenarios. The combination of case studies, research papers, and hands-on workshops opened new business opportunities and led to increased adoption by helping potential users and customers clearly see how Vision AI’s solutions could directly address their industry-specific needs.

“Showcasing industry-specific use cases through research publications, conference presentations, and hands-on workshops builds credibility and excitement, helping developers and stakeholders see how computer vision can address real-world challenges. Active participation in the AI/ML research community allows DevRel teams to connect complex technology with practical applications, making products more accessible and relevant to industry professionals and advanced users.” Gift Ojeabulu

Summary of Key Learnings from Case Study

Lowering Barriers to Entry Empowering a Collaborative Community Building Trust through Real-World Impact Key Learnings from Case Study

Key KPIs/Metrics for assessing DevRel Success in AI

DevRel Metrics Engagement and Interaction Metrics Adoption and Retention Metrics Feedback Quality and Quantity: Educational Content & Training Metrics Community Contributions Open-Source Metrics

Tools and techniques for tracking DevRel ROI.

Analytics Platforms e.g Google Analytics, Mix Panel, Looker, Tableau Customer Relationship Management (CRM) Systems Community Platforms e.g Discord, GitHub Discussion, Discourse Specialized DevRel Tools, e.g Orbit, Common Room, Mentions.com Surveys and Feedback Forms, e.g Google forms Tools and Techniques

Aligning DevRel Metrics with Overall Business Objectives

Map DevRel Goals to Product Growth Tie Community Engagement to Customer Retention Link Educational Efforts to Product Quality Highlight Brand Advocacy and Thought Leadership DevRel Metric - > Business Objectives

Final Takeaways By focusing on relevant metrics and aligning them with business objectives, DevRel teams in AI/ML can demonstrate a clear, measurable impact on product success. This approach not only highlights DevRel’s contribution to community health and user satisfaction but also shows how it directly supports the organization’s goals for growth, retention, and market positioning.

“In the AI space, the best product doesn't always win— the one with the strongest developer community does. DevRel is how you build that community.” Gift Ojeabulu

Conclusion

“Just as data serves as the lifeblood of businesses in the 21st century, quality feedback is the lifeblood of every product, particularly in this era of rapid AI advancement. Developer Relations (DevRel) acts as the skilled surgeon, ensuring this vital lifeblood flows effectively, enabling products to thrive and evolve.” Gift Ojeabulu

Q & A? DevRel DevRel AI AI AI AI AI DevRel

GiftOjeabulu_ THANK YOU Scan this QR Code for the link to my slide