Celebrating Google Dev Library’s Ladies Contributors in AI/ML — Google for Builders Weblog

Posted by Swathi Dharshna Subbaraj, Google Dev Library

Ladies have made outstanding progress in advancing AI/ML expertise by way of their contributions to open supply tasks. They’ve developed and maintained instruments, algorithms, and frameworks that allow researchers, builders, and companies to create and implement innovative AI/ML options.

To rejoice these achievements, Google Dev Library has featured excellent contributions from builders worldwide. It has additionally supplied a chance to showcase contributions from ladies builders who’re engaged on AI/ML tasks. Learn on to study their tasks and insights.

Contributors in Highlight

Suzen Fylke

Suzen is a machine studying engineer with a ardour for serving to mission-driven and socially-minded firms leverage AI and information to drive impactful outcomes. With 3 years of expertise at Twitter, Suzen developed platform instruments that streamlined mannequin improvement and deployment processes, permitting for sooner iteration and improved effectivity. Sue just lately shared her weblog put up titled “Find out how to Visualize Customized TFX Artifacts With InteractiveContext” with Dev Library. Let’s converse with Sue and study extra about her expertise.

Headshot of Suzen Fylke, smiling

1.    Inform us extra about your current Dev Library submission on inspecting TFX artifactswith InteractiveContext and why you think about it invaluable for debugging TFX pipelines?
    Considered one of my favourite issues about TFX is having the ability to run pipeline steps individually and interactively examine their outcomes with InteractiveContext. I used to assume you may solely show normal artifacts with built-in visualizations, however, because it seems, you can even use InteractiveContext with customized artifacts. Since I hadn’t discovered any examples or documentation explaining the right way to show customized artifacts, I wrote a tutorial.

    2.    Are you able to stroll me by way of your course of for creating technical documentation in your tasks to assist different builders?   

    After I create technical documentation for work or open supply tasks, I do my finest to observe the group’s finest practices and elegance guides and to heart the reader. I feel quite a bit about what readers can hope to study or be capable to do after studying the docs. I adopted an analogous method when writing the tutorial I submitted.

    Most of my private tasks are energetic studying workout routines. After I write about such tasks, I focus rather more on the method of constructing them than on the result. So, along with exhibiting how they work, I describe what impressed me to create them, the challenges I encountered, and what’s subsequent for the venture. I additionally embrace numerous hyperlinks to sources I discovered useful for understanding the instruments and ideas I discovered about.

    3.    What recommendation do you have got for different ladies keen on growing open supply AL/ML tasks, and the way can they get began? 

    I like to recommend contributing to communities you care about and tasks you employ and need to assist enhance. Create issues utilizing the venture. Ask questions when documentation must be clarified. Report bugs once you encounter them. In case you construct one thing cool, demo it or write about it. In case you discover an issue you may repair, volunteer to take action. And if you happen to get caught or do not perceive one thing, ask for assist. I additionally suggest studying GitHub’s “Find out how to Contribute to Open Supply” information (https://opensource.information/how-to-contribute/). My favourite takeaway is that open supply tasks are greater than code and that there are lots of alternative ways to contribute based mostly in your pursuits.

    4.    Your Dev Library creator profile bio states that you simply’re exploring the right way to “make studying languages enjoyable and approachable.” Are you able to stroll me by way of that course of? 


    That is aspirational and primarily a pastime proper now. I really like studying languages and studying the right way to study languages. Languages are my “factor I can discuss for hours with out becoming bored.” I do not even have a course of for this. As an alternative, I do a number of exploring and experimenting and let my curiosity information me. Generally this includes studying linguistics textbooks, attempting completely different language-learning apps, contributing to tasks like Frequent Voice, or studying the right way to use libraries like spaCy.

    5.    How do you see the sector of open supply AI/ML improvement evolving within the coming years, and the way are you making ready for these adjustments?

    I see the continued improvement of instruments and platforms aimed toward democratizing machine studying. I hope this may allow folks to meaningfully have interaction with the fashions and AI-powered merchandise they use and higher perceive how they work. I additionally hope this may result in extra grassroots participatory analysis communities like Masakhane and encourage folks with out ML or software program engineering backgrounds to create and contribute to open supply tasks.

    Aqsa is a passionate machine studying engineer with a powerful curiosity for expertise and a want to share concepts with others. She has sensible expertise in various tasks, together with footfall forecasting, cataract detection, augmented actuality, object detection, and recommender techniques. Aqsa shared her weblog put up titled “Callbacks in TensorFlow — Customise the Conduct of your coaching” with Dev Library. Let’s converse with Aqsa and study extra about her expertise.

    Photo of Aqsa Kausar holding a microphone

    1.    Being Pakistan’s first Google Developer Professional (GDE), how do you method constructing inclusive and various communities round you?

      As a Google Developer Professional (GDE), my accountability is to assist enhance the tech group by way of inclusive and various occasions, workshops, and mentorship. With help from Google, fellow GDEs, and Google Developer Teams, we intention to create accessible alternatives for everybody, no matter their background or expertise degree. As a speaker, I share my information in ML with various audiences and provide mentorship to underrepresented people in tech, together with ladies, minorities, and people from completely different backgrounds. I present steering on academic and profession alternatives and join folks with sources, catering to as many as I can by way of varied technique of communication.

      2.     How do you method collaborating with different builders on open supply AI/ML tasks, and what are some finest practices you observe to make sure success?

      In our GDE group, now we have energetic open supply contributors who collaborate in teams for tutorials, analysis papers, and extra. Collaboration is inspired, and Googlers generally lead open supply tasks with GDEs. Whenever you specific curiosity, builders are open to working collectively. To foster a constructive tradition, we emphasize worth and respect, clear objectives, manageable duties, communication channels, open communication, constructive suggestions, and celebrating milestones. Profitable collaboration hinges on valuing one another’s time and expertise.

      3.    How do you steadiness the necessity for technical rigor with the necessity for usability and accessibility in your open supply tasks?

      Understanding your viewers and their wants is essential to strike the suitable steadiness between technical rigor and value. Simplify technical ideas for non-technical audiences and concentrate on sensible functions. In open supply tasks, you have got extra flexibility, however in workshops or coaching, select instruments and applied sciences appropriate in your viewers. For rookies, use easier language and interactive demos. For intermediate or superior audiences, go deeper into technical particulars with coding snippets and sophisticated ideas.

      4.    Why do you assume it is necessary for technical writers to revise your content material or tasks frequently? Do you assume it’s essential that each tech author or open supply maintainer observe this finest observe?

      Expertise is ever-changing, so technical writers have to revise content material frequently to make sure accuracy. Suggestions from the viewers can assist make it accessible and related. Nonetheless, contributors could not all the time have time to replace their work as a consequence of busy schedules. Nonetheless, tech blogs and tasks nonetheless present a priceless kickstart for brand spanking new builders, who can contribute with updates or follow-up blogs.

      5.    Are you able to inform me a few venture you have labored on that you simply’re notably happy with, and what affect it has had on the open supply group?

      I’ve been a part of impactful initiatives resembling Google Ladies Developer Academy, the place I used to be a mentor for his or her pilot. This system helps ladies in tech enhance their communication expertise and prepares them for showcasing their skills, boosting their confidence. I additionally collaborated with fellow Google Developer Consultants (GDEs) in the course of the COVID-19 pandemic to create an open supply course referred to as “ML for Rookies,” which simplifies machine studying ideas. At present, I’m engaged on a Cloud AI venture supported by GCP and have began an open supply “Cloud Playground” repo to make cloud-ai studying extra accessible.

      Margaret, an ML Google Developer Professional (GDE) since 2018, is an ML analysis engineer who applies AI/ML to actual world functions starting from local weather change to artwork and design. With experience in deep studying, laptop imaginative and prescient, TensorFlow, and on-device ML, she typically writes and speaks at conferences. Margaret has shared a number of tasks in matters like TensorFlow Lite with Dev Library. Let’s converse with Margaret and study extra about her expertise.

      Photo of Margaret Maynard-Reid, smiling

      1.    Are you able to share the Google applied sciences you’re employed with?  


      A number of the Google applied sciences I work with are TensorFlow, TensorFlow Lite, Keras, Android, MediaPipe, and ML Package. 

      2.    How do you method collaborating with different builders on open supply tasks, and what are some finest practices you observe to make sure a profitable collaboration? 

      I’ve collaborated with Googlers, ML GDEs, college students and professionals in tech. Constant communication and observing finest practices, resembling code check-in and code evaluations, are useful to make sure a profitable collaboration. 

      3.    What’s your improvement course of like for creating and sustaining open supply AI/ML tasks, and the way do you prioritize which tasks to work on? 

      There may be restricted time so prioritization is tremendous essential. I wish to showcase new applied sciences or areas the place builders together with myself could have challenges with. Except for code and tutorials, I additionally wish to share my information with sketchnotes and visible illustrations. 

      4.    You’ve got been sharing studying sources on TensorFlow Lite. What recommendation do you have got for different ladies keen on growing open supply tasks, and the way can they get began? 


      There are numerous methods to contribute to open supply tasks: present suggestions on documentation or product options; write a tutorial with pattern code; assist repair bugs or contribute to libraries and so on. It’s finest to begin easy and simple first, after which progress to tougher tasks. 

      5.    How do you see the sector of open supply AI/ML improvement evolving within the coming years, and the way are you making ready for these adjustments? 

      Open supply is turning into more and more essential for AI/ML improvement, evident within the current improvement of generative AI and on-device machine studying for instance. There might be much more alternatives for open supply tasks. Preserve contributing as a result of open supply tasks are a good way to study the most recent whereas serving to others.

      Are you actively contributing to the AI/ML group? Develop into a Google Dev Library Contributor!

      Google Dev Library is a platform for showcasing open supply tasks that includes Google applied sciences. Be a part of our international group of builders to showcase your tasks. Submit your content material.

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