Tag Archives: GDE

ML Olympiad 2023: Globally Distributed ML Competitions by Google ML Community

Posted by Hee Jung, DevRel Community Manager

What is the ML Olympiad?

The ML Olympiad is an associated Kaggle Community Competitions hosted by ML GDE, TFUG, 3rd-party ML communities, supported by Google Developers. The ML Developer Programs team and the communities successfully ran the first round of the campaign in 2022 and are now launching the second round. The goal of this campaign is to provide ML training opportunities for developers by leveraging Kaggle’s features.

ML Olympiad Community Competitions

17 ML Olympiad community competitions are currently open. Visit the ML Olympiad page to participate.

Into the Space

Predicting which spaceship passengers were transported by the anomaly using records recovered from the spaceship’s damaged computer system.
Host: MD Shahriyar Al Mustakim Mitul / TFUG Dhaka

Water Quality Prediction

Estimating the quality of water.
Hosts: Usha Rengaraju, Vijayabharathi Karuppasamy (TFUG Chennai), Samuel T (TFUG Mysuru)

Breast Cancer Diagnosis

Predicting medical diagnosis [breast cancer].
Host: Ankit Kumar Verma / TFUG Prayagraj

Book Recommendations

To provide personalized recommendations to users based on their reading history and preferences using various machine learning algorithms.
Hosts: Anushka Raj, Yugandhar Surya / TFUG Hajipur

Argania Tree Deforestation Detection

Use Sentinel-2 satellite imagery to detect and map areas of deforestation in the Argania region.
Hosts: Taha Bouhsine / TFUG Agadir

Multilingual Spell Correction

Reconstruct noisy sentences in European languages: English, French, German, Bulgarian and Turkish.
Host: Radostin Cholakov (ML GDE)

CO2 Emissions Forecasting

Forecasting CO2 emissions based on deforestation in Côte d'Ivoire.
Hosts: Armel Yara, Kimana Misago, Jordan Erifried / TFUG Abidjan

Ensure Healthy Lives (in local language)

Use ML techniques to help achieve common good health and well-being.
Hosts: Vinicius Fernandes Caridá (ML GDE), Pedro Gengo, Alex Fernandes Mansano / TFUG São Paulo

Predictive Maintenance

Predict future engine’s failures.
Host: Daniel Pereda / TFUG Santiago

Firetrucks Are Red And Cars Are Blue

To create a model that can accurately predict the correct class for each image, without overfitting.
Host: Prasoon Kottarathil / TFUG Thrissur

Dialect Recognition (in Arabic)

Dialect recognition in order to improve user experience in AI applications.
Hosts: Ruqiya Bin Safi (ML GDE), Eyad Sibai, Hussain Alfayez / Saudi TFUG & Applied ML/AI group

Sentiment Analysis Of JUMIA Tunisia (in local language)

Use JUMIA customer reviews to determine the sentiment of content from text data.
Host: Boulbaba BEN AMMAR / TFUG Sfax

Kolkata Housing Prediction

Kolkata housing prediction results can be used to address related social and economic issues.
Host: Rishiraj Acharya / TFUG Kolkata

Can You Guess The Beer Style?

This is a machine learning competition focused on classifying beer into 17 distinct styles based on key descriptors.
Host: Marvik

Detect ChatGpt answers

The goal of this competition is to classify ChatGpt answers vs real human answers for a variety of questions.
Host: Elyes Manai (ML GDE) / IEEE ESSTHS + GDSC ISETSO + PyData Tunisia

MLAct Pose Detection

Raising awareness about some basic yoga poses, and encouraging our community members to practice the basic parts of computer vision.
Host: Imen Masmoudi / MLAct ML Community

Hausa Sentiment Analysis 2.0 (in local language)

Classify the sentiment of sentences of Hausa Language.
Hosts: Nuruddeen Sambo, Dattijo Murtala Makama / TFUG Bauchi

Navigating ML Olympiad

You can search “ML Olympiad” on Kaggle Community Competitions page to see them all. And for further info, look for #MLOlympiad on social media.

Google Developers supports the hosts of each competition. Browse through the available competitions and participate in those that interest you!

Source: Google Developers Blog

Introducing the Earth Engine Google Developer Experts (GDEs)

Posted by Tyler Erickson, Developer Advocate, Google Earth Engine

One of the greatest things about Earth Engine is the vibrant community of developers who openly share their knowledge about the platform and how it can be used to address real-world sustainability issues. To recognize some of these exceptional community members, in 2022 we launched the initial cohort of Earth Engine Google Developer Experts (GDEs). You can view the current list of Earth Engine GDEs on the GDE Directory page.

Moving 3D image of earth rotating showing locations of members belonging to the initial cohort of Earth Engine GDEs

The initial cohort of Earth Engine Google Developer Experts.

What makes an Earth Engine expert? Earth Engine GDEs are selected based on their expertise in the Earth Engine product (of course), but also for their knowledge sharing. They share their knowledge in many ways, including answering questions from other developers, writing tutorials and blogs, teaching in settings spanning from workshops to university classes, organizing meetups and conference sessions that allow others to share their work, building extensions to the platform, and so much more!

To learn more about the Google Developer Experts program and the Earth Engine GDEs, go to https://developers.google.com/community/experts.

Now that it is 2023, we are re-opening the application process for additional Earth Engine GDEs. If you’re interested in being considered, you can find information about the process in the GDE Program Application guide.

Source: Google Developers Blog

Google Earth Engine GDE Liza Goldberg uses tech to fight climate change

Posted by Janelle Kuhlman, Developer Relations Program Manager

Liza Goldberg, Google Earth GDE

Google Earth Engine GDE Liza Goldberg uses tech to fight climate change

Liza Goldberg learned to code through Google Earth Engine at age fourteen, when her mentors at NASA, where she was an intern, introduced the tool as a way to model global trends in environmental change. Liza, who had arrived at NASA with no coding or remote sensing experience, gradually gained expertise in the platform, thanks to strong mentorship, Google training, and guidance from the Google Earth Engine developer community. The fact that Google Earth Engine is built for scientists and has a clear world impact aligned with Liza’s commitment to using technology to combat climate change. “Earth Engine enabled me to write each line of code knowing that my algorithms could eventually make true change in climate monitoring,” she says. “The visualization-focused interface of Earth Engine showed me that coding could be simple, data focused, and broadly influential across all fields of climate science.”

Liza Goldberg on stage speaking at the Geo for Good Summit

Liza Goldberg speaking at the Geo for Good Summit

Becoming a GDE

Liza used Earth Engine for years at her NASA internship, which grew into a part-time research position. In 2022, her longtime collaborator on the Google Earth Engine team, Tyler Erickson, nominated Liza for the GDE Program, and she became a GDE in April 2022.

“When I found out about my nomination, I felt admittedly nostalgic,” she says. “I remembered my 14-year-old excitement when I first opened Earth Engine – how the whole world suddenly seemed open to me. Becoming a GDE felt like coming full-circle; in many ways, I grew up with Earth Engine.”

Liza hopes her GDE role encourages other young students to explore new technologies.

“I hope that my position as a GDE can show other young students - particularly women - that starting with no knowledge of a field doesn’t need to be a barrier towards accomplishing your ultimate goals,” she says. “As the youngest female GDE in North America, I hope to break the barriers that prevent other young women from chasing down their passions in male-dominated arenas.”

In her GDE role, Liza is collaborating with Google India and the Indian Institutes of Technology (IIT) to launch a series of Google Earth Engine trainings across the country, building technical capacity among the next generation of climate scientists.

“We’ll be guiding students in basic geospatial skills, preparing them for fellowships with partner conservation organizations in the coming year,” she says. “I’m optimistic that this program can distribute the advanced computing power of Earth Engine to students who can leverage its tools for local-to-national scale change.”

Working at NASA

Liza has continued her longtime work on global mangrove ecosystem vulnerability at NASA, analyzing the impact of various mangrove protection and governance models on the degree of forest disturbance. Liza is spending the summer in West Africa with her NASA colleagues, completing mangrove-based fieldwork and delivering Google Earth Engine trainings to academic and conservation institutions in the area.

Liza is also currently leading The Atlantis Project, a global initiative to enable the Earth’s most climate vulnerable populations to develop community disaster response capacity, at NASA.

“We’re using Google Earth Engine to map the key barriers toward a community’s recovery from impending climatic disasters, enabling aid organizations to more effectively target the right stressors in the right communities,” she says. “We’re currently training highly flood vulnerable communities in early warning system deployment and household disaster preparation and response.”

Her team is also collaborating with NGOs in India to educate communities on their post-disaster aid rights.

Studying at Stanford

Liza is also a college student, studying Earth Systems and international development policy at Stanford University.

“I seek to better understand how climate change can further trap the extreme poor in cycles of lagging economic growth,” she says. “I will then combine my remote sensing knowledge with this policy and climate change background to develop new solutions for climate adaptation across the developing world.”

Ultimately, Liza seeks to use technology to help the planet’s most climate-vulnerable populations respond most effectively to climate impacts.

“I’ve found that satellite analysis is among the most effective ways to tackle many of these challenges, but I’ve fallen in love with the problem, not any particular solution to it,” she says. “In my professional future, I seek to continue applying satellite tech towards building these critical bridges between technical capacity and on-the-ground need.”

Learn more about Liza on LinkedIn.

The Google Developer Experts (GDE) program is a global network of highly experienced technology experts, influencers, and thought leaders who actively support developers, companies, and tech communities by speaking at events and publishing content.

Source: Google Developers Blog

Machine Learning Communities: Q2 ‘22 highlights and achievements

Posted by Nari Yoon, Hee Jung, DevRel Community Manager / Soonson Kwon, DevRel Program Manager

Let’s explore highlights and accomplishments of vast Google Machine Learning communities over the second quarter of the year! We are enthusiastic and grateful about all the activities by the global network of ML communities. Here are the highlights!

TensorFlow/Keras

TFUG Agadir hosted #MLReady phase as a part of #30DaysOfML. #MLReady aimed to prepare the attendees with the knowledge required to understand the different types of problems which deep learning can solve, and helped attendees be prepared for the TensorFlow Certificate.

TFUG Taipei hosted the basic Python and TensorFlow courses named From Python to TensorFlow. The aim of these events is to help everyone learn about the basics of Python and TensorFlow, including TensorFlow Hub, TensorFlow API. The event videos are shared every week via Youtube playlist.

TFUG New York hosted Introduction to Neural Radiance Fields for TensorFlow users. The talk included Volume Rendering, 3D view synthesis, and links to a minimal implementation of NeRF using Keras and TensorFlow. In the event, ML GDE Aritra Roy Gosthipaty (India) had a talk focusing on breaking the concepts of the academic paper, NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis into simpler and more ingestible snippets.

TFUG Turkey, GDG Edirne and GDG Mersin organized a TensorFlow Bootcamp 22 and ML GDE M. Yusuf Sarıgöz (Turkey) participated as a speaker, TensorFlow Ecosystem: Get most out of auxiliary packages. Yusuf demonstrated the inner workings of TensorFlow, how variables, tensors and operations interact with each other, and how auxiliary packages are built upon this skeleton.

TFUG Mumbai hosted the June Meetup and 110 folks gathered. ML GDE Sayak Paul (India) and TFUG mentor Darshan Despande shared knowledge through sessions. And ML workshops for beginners went on and participants built up machine learning models without writing a single line of code.

ML GDE Hugo Zanini (Brazil) wrote Realtime SKU detection in the browser using TensorFlow.js. He shared a solution for a well-known problem in the consumer packaged goods (CPG) industry: real-time and offline SKU detection using TensorFlow.js.

ML GDE Gad Benram (Portugal) wrote Can a couple TensorFlow lines reduce overfitting? He explained how just a few lines of code can generate data augmentations and boost a model’s performance on the validation set.

ML GDE Victor Dibia (USA) wrote How to Build An Android App and Integrate Tensorflow ML Models sharing how to run machine learning models locally on Android mobile devices, How to Implement Gradient Explanations for a HuggingFace Text Classification Model (Tensorflow 2.0) explaining in 5 steps about how to verify the model is focusing on the right tokens to classify text. He also wrote how to finetune a HuggingFace model for text classification, using Tensorflow 2.0.

ML GDE Karthic Rao (India) released a new series ML for JS developers with TFJS. This series is a combination of short portrait and long landscape videos. You can learn how to build a toxic word detector using TensorFlow.js.

ML GDE Sayak Paul (India) implemented the DeiT family of ViT models, ported the pre-trained params into the implementation, and provided code for off-the-shelf inference, fine-tuning, visualizing attention rollout plots, distilling ViT models through attention. (code | pretrained model | tutorial)

ML GDE Sayak Paul (India) and ML GDE Aritra Roy Gosthipaty (India) inspected various phenomena of a Vision Transformer, shared insights from various relevant works done in the area, and provided concise implementations that are compatible with Keras models. They provide tools to probe into the representations learned by different families of Vision Transformers. (tutorial | code)

JAX/Flax

ML GDE Aakash Nain (India) had a special talk, Introduction to JAX for ML GDEs, TFUG organizers and ML community network organizers. He covered the fundamentals of JAX/Flax so that more and more people try out JAX in the near future.

ML GDE Seunghyun Lee (Korea) started a project, Training and Lightweighting Cookbook in JAX/FLAX. This project attempts to build a neural network training and lightweighting cookbook including three kinds of lightweighting solutions, i.e., knowledge distillation, filter pruning, and quantization.

ML GDE Yucheng Wang (China) wrote History and features of JAX and explained the difference between JAX and Tensorflow.

ML GDE Martin Andrews (Singapore) shared a video, Practical JAX : Using Hugging Face BERT on TPUs. He reviewed the Hugging Face BERT code, written in JAX/Flax, being fine-tuned on Google’s Colab using Google TPUs. (Notebook for the video)

ML GDE Soumik Rakshit (India) wrote Implementing NeRF in JAX. He attempts to create a minimal implementation of 3D volumetric rendering of scenes represented by Neural Radiance Fields.

Kaggle

ML GDEs’ Kaggle notebooks were announced as the winner of Google OSS Expert Prize on Kaggle: Sayak Paul and Aritra Roy Gosthipaty’s Masked Image Modeling with Autoencoders in March; Sayak Paul’s Distilling Vision Transformers in April; Sayak Paul & Aritra Roy Gosthipaty’s Investigating Vision Transformer Representations; Soumik Rakshit’s Tensorflow Implementation of Zero-Reference Deep Curve Estimation in May and Aakash Nain’s The Definitive Guide to Augmentation in TensorFlow and JAX in June.

ML GDE Luca Massaron (Italy) published The Kaggle Book with Konrad Banachewicz. This book details competition analysis, sample code, end-to-end pipelines, best practices, and tips & tricks. And in the online event, Luca and the co-author talked about how to compete on Kaggle.

ML GDE Ertuğrul Demir (Turkey) wrote Kaggle Handbook: Fundamentals to Survive a Kaggle Shake-up covering bias-variance tradeoff, validation set, and cross validation approach. In the second post of the series, he showed more techniques using analogies and case studies.

TFUG Chennai hosted ML Study Jam with Kaggle and created study groups for the interested participants. More than 60% of members were active during the whole program and many of them shared their completion certificates.

TFUG Mysuru organizer Usha Rengaraju shared a Kaggle notebook which contains the implementation of the research paper: UNETR - Transformers for 3D Biomedical Image Segmentation. The model automatically segments the stomach and intestines on MRI scans.

TFX

ML GDE Sayak Paul (India) and ML GDE Chansung Park (Korea) shared how to deploy a deep learning model with Docker, Kubernetes, and Github actions, with two promising ways - FastAPI (for REST) and TF Serving (for gRPC).

ML GDE Ukjae Jeong (Korea) and ML Engineers at Karrot Market, a mobile commerce unicorn with 23M users, wrote Why Karrot Uses TFX, and How to Improve Productivity on ML Pipeline Development.

ML GDE Jun Jiang (China) had a talk introducing the concept of MLOps, the production-level end-to-end solutions of Google & TensorFlow, and how to use TFX to build the search and recommendation system & scientific research platform for large-scale machine learning training.

ML GDE Piero Esposito (Brazil) wrote Building Deep Learning Pipelines with Tensorflow Extended. He showed how to get started with TFX locally and how to move a TFX pipeline from local environment to Vertex AI; and provided code samples to adapt and get started with TFX.

TFUG São Paulo (Brazil) had a series of online webinars on TensorFlow and TFX. In the TFX session, they focused on how to put the models into production. They talked about the data structures in TFX and implementation of the first pipeline in TFX: ingesting and validating data.

TFUG Stockholm hosted MLOps, TensorFlow in Production, and TFX covering why, what and how you can effectively leverage MLOps best practices to scale ML efforts and had a look at how TFX can be used for designing and deploying ML pipelines.

Cloud AI

ML GDE Chansung Park (Korea) wrote MLOps System with AutoML and Pipeline in Vertex AI on GCP official blog. He showed how Google Cloud Storage and Google Cloud Functions can help manage data and handle events in the MLOps system.

He also shared the Github repository, Continuous Adaptation with VertexAI's AutoML and Pipeline. This contains two notebooks to demonstrate how to automate to produce a new AutoML model when the new dataset comes in.

TFUG Northwest (Portland) hosted The State and Future of AI + ML/MLOps/VertexAI lab walkthrough. In this event, ML GDE Al Kari (USA) outlined the technology landscape of AI, ML, MLOps and frameworks. Googler Andrew Ferlitsch had a talk about Google Cloud AI’s definition of the 8 stages of MLOps for enterprise scale production and how Vertex AI fits into each stage. And MLOps engineer Chris Thompson covered how easy it is to deploy a model using the Vertex AI tools.

Research

ML GDE Qinghua Duan (China) released a video which introduces Google’s latest 540 billion parameter model. He introduced the paper PaLM, and described the basic training process and innovations.

ML GDE Rumei LI (China) wrote blog postings reviewing papers, DeepMind's Flamingo and Google's PaLM.

Source: Google Developers Blog

ML in Action: Campaign to Collect and Share Machine Learning Use Cases

Posted by Hee Jung, Developer Relations Community Manager / Soonson Kwon, Developer Relations Program Manager

ML in Action is a virtual event to collect and share cool and useful machine learning (ML) use cases that leverage multiple Google ML products. This is the first run of an ML use case campaign by the ML Developer Programs team.

Let us announce the winners right now, right here. They have showcased practical uses of ML, and how ML was adapted to real life situations. We hope these projects can spark new applied ML project ideas and provide opportunities for ML community leaders to discuss ML use cases.

4 Winners of "ML in Action" are:

Detecting Food Quality with Raspberry Pi and TensorFlow

By George Soloupis, ML Google Developer Expert (Greece)

This project helps people with smell impairment by identifying food degradation. The idea came suddenly when a friend revealed that he has no sense of smell due to a bike crash. Even with experiences attending a lot of IT meetings, this issue was unaddressed and the power of machine learning is something we could rely on. Hence the goal. It is to create a prototype that is affordable, accurate and usable by people with minimum knowledge of computers.

The basic setting of the food quality detection is this. Raspberry Pi collects data from air sensors over time during the food degradation process. This single board computer was very useful! With the GUI, it’s easy to execute Python scripts and see the results on screen. Eight sensors collected data of the chemical elements such as NH3, H2s, O3, CO, and CH4. After operating the prototype for one day, categories were set following the results. The first hours of the food out of the refrigerator as “good” and the rest as “bad”. Then the dataset was evaluated with the help of TensorFlow and the inference was done with TensorFlow Lite.

Since there were no open source prototypes out there with similar goals, it was a complete adventure. Sensors on PCBs and standalone sensors were used to get the best mixture of accuracy, stability and sensitivity. A logic level converter has been used to minimize the use of resistors, and capacitors have been placed for stability. And the result, a compact prototype! The Raspberry Pi could attach directly on with slots for eight sensors. It is developed in such a way that sensors can be replaced at any time. Users can experiment with different sensors. And the inference time values are sent through the bluetooth to a mobile device. As an end result a user with no advanced technical knowledge will be able to see food quality on an app built on Android (Kotlin).

Reference: Github, more to read

* This project is supported by Google Impact Fund.

Election Watch: Applying ML in Analyzing Elections Discourse and Citizen Participation in Nigeria

By Victor Dibia, ML Google Developer Expert (USA)

This project explores the use of GCP tools in ingesting, storing and analyzing data on citizen participation and election discourse in Nigeria. It began on the premise that the proliferation of social media interactions provides an interesting lens to study human behavior, and ask important questions about election discourse in Nigeria as well as interrogate social/demographic questions.

It is based on data collected from twitter between September 2018 to March 2019 (tweets geotagged to Nigeria and tweets containing election related keywords). Overall, the data set contains 25.2 million tweets and retweets, 12.6 million original tweets, 8.6 million geotagged tweets and 3.6 million tweets labeled (using an ML model) as political.

By analyzing election discourse, we can learn a few important things including - issues that drive election discourse, how social media was utilized by candidates, and how participation was distributed across geographic regions in the country. Finally, in a country like Nigeria where updated demographics data is lacking (e.g., on community structures, wealth distribution etc), this project shows how social media can be used as a surrogate to infer relative statistics (e.g., existence of diaspora communities based on election discussion and wealth distribution based on device type usage across the country).

Data for the project was collected using python scripts that wrote tweets from the Twitter streaming api (matching certain criteria) to BigQuery. BigQuery queries were then used to generate aggregate datasets used for visualizations/analysis and training machine learning models (political text classification models to label political text and multi class classification models to label general discourse). The models were built using Tensorflow 2.0 and trained on Colab notebooks powered by GCP GPU compute VMs.

References: Election Watch website, ML models descriptions one, two

Bioacoustic Sound Detector (To identify bird calls in soundscapes)

By Usha Rengaraju, TFUG Organizer (India)

(Bird image is taken by Krisztian Toth @unsplash)

“Visionary Perspective Plan (2020-2030) for the conservation of avian diversity, their ecosystems, habitats and landscapes in the country” proposed by the Indian government to help in the conservation of birds and their habitats inspired me to take up this project.

Extinction of bird species is an increasing global concern as it has a huge impact on food chains. Bioacoustic monitoring can provide a passive, low labor, and cost-effective strategy for studying endangered bird populations. Recent advances in machine learning have made it possible to automatically identify bird songs for common species with ample training data. This innovation makes it easier for researchers and conservation practitioners to accurately survey population trends and they’ll be able to regularly and more effectively evaluate threats and adjust their conservation actions.

This project is an implementation of a Bioacoustic monitor using Masked Autoencoders in TensorFlow and Cloud TPUs. The project will be presented as a browser based application using Flask. The deep learning prototype can process continuous audio data and then acoustically recognize the species.

The goal of the project when I started was to build a basic prototype for monitoring of rare bird species in India. In future I would like to expand the project to monitor other endangered species as well.

References: Kaggle Notebook, Colab Notebook, Github, the dataset and more to read

Persona Labs' Digital Personas

By Martin Andrews and Sam Witteveen, ML Google Developer Experts (Singapore)

Over the last 3 years, Red Dragon AI (a company co-founded by Martin and Sam) has been developing real-time digital “Personas”. The key idea is to enable users to interact with life-like Personas in a format similar to a Zoom call : Speaking to them and seeing them respond in real time, just as a human would. Naturally, each Persona can be tailored to tasks required (by adjusting the appearance, voice, and ‘motivation’ of the dialog system behind the scenes and their corresponding backend APIs).

The components required to make the Personas work effectively include dynamic face models, expression generation models, Text-to-Speech (TTS), dialog backend(s) and Speech Recognition (ASR). Much of this was built on GCP, with GPU VMs running the (many) Deep Learning models and combining the outputs into dynamic WebRTC video that streams to users via a browser front-end.

Much of the previous years’ work focussed on making the Personas’ faces behave in a life-like way, while making sure that the overall latency (i.e. the time between the Persona hearing the user asking a question, to their lips starting the response) is kept low, and the rendering of individual images matches the 25 frames-per-second video rate required. As you might imagine, there were many Deep Learning modeling challenges, coupled with hard engineering issues to overcome.

In terms of backend technologies, Google Cloud GPUs were used to train the Deep Learning models (built using TensorFlow/TFLite, PyTorch/ONNX & more recently JAX/Flax), and the real-time serving is done by Nvidia T4 GPU-enabled VMs, launched as required. Google ASR is currently used as a streaming backend for speech recognition, and Google’s WaveNet TTS is used when multilingual TTS is needed. The system also makes use of Google’s serverless stack with CloudRun and Cloud Functions being used in some of the dialog backends.

Visit the Persona’s website (linked below) and you can see videos that demonstrate several aspects : What the Personas look like; their Multilingual capability; potential applications; etc. However, the videos can’t really demonstrate what the interactivity ‘feels like’. For that, it’s best to get a live demo from Sam and Martin - and see what real-time Deep Learning model generation looks like!

Reference: The Persona Labs website

Source: Google Developers Blog

Angular GDE Todd Motto encourages developers to care for their bodies and minds

Posted by Janelle Kuhlman, Developer Relations Program Manager

Photo of a man in a wetsuit swimming in the water. He is mid stroke and is taking a breath of air

The second of two interviews with GDEs about mental health, during Mental Health Awareness Month

Angular GDE Todd Motto would love to see people talk about mental health more freely–in tech and in other areas of life.

“Everyone struggles inside,” he says. “I see talking about it a good thing. Our brains are highly complex and need maintenance and good fuel.”

Todd says he silently struggled through most of his life with depression and anxiety, so it has become increasingly important to him to be forthright about it. He says ignoring feelings often makes things worse.

“The thing is, you can go through life just thinking it’s normal to feel this way, and you assume everyone else has bad days like that, as well, but things can slowly progress to become worse, without you realizing,” he says. “It took me a very long time to realize I had mental health issues–some issues were from my past, and I had adapted unhealthy lifestyle patterns to deal with those. I was pouring fuel on my own fire and not realizing it. That’s why it is important to me to raise awareness.”

He sees mental health as a balancing act and believes it’s important to take care of your body and mind every day. He recommends choosing your work projects and responsibilities carefully, if possible, to avoid taking on too much, and to pay attention to your internal thoughts.

“It’s important to be in tune with your body and also how your mind feels,” he says. “We all feel stress, but sometimes we just sit on autopilot and ignore it. This is when it’s time to protect your mental health. Keep an eye on your stress levels, as, at least for me, this played a huge role in the rest of my mental health.”

Todd copes with stress by carefully managing his workload, learning new things away from the keyboard, taking breaks from work throughout the day, and taking down time.

“To cope, I don’t overwhelm myself, and I take regular breaks, even if it’s just 1-2 minutes to walk into the kitchen and grab water,” he says. “Maybe I’ll walk into the garden and research a personal topic I’m interested in for a few moments.”

He also incorporates daily exercise, like running, swimming, and weight training, which he says helps his concentration, sleep, and mood.

“I have been running and swimming for years now, and swimming gives you time out from reality,” he says. “When you get physically stronger, you will unlock new levels of mental strength. That is my guarantee.”

Todd’s version of physical and mental challenges might be running up mountains and swimming in lakes, but your version might be going on a walk around the block, picking up a new instrument, or learning how to cook a new meal. Whatever it is, Todd feels it’s important to make time for these challenges, in order to achieve that balancing act he mentioned. He reminds other developers to keep work, life, body, and mind in balance as much as possible.

“I aim to have regular breaks and not overwhelm myself,” he says. “It’s easy to get stressed and have a bad work/life balance. Take breaks, and keep your stress levels low by doing so. You are more than worth it!”

Learn more about Todd on Twitter @toddmotto

Source: Google Developers Blog

Google Workplace GDE Alice Keeler on balancing responsibilities and using coding as self-care

Posted by Janelle Kuhlman, Developer Relations Program Manager

The first of two interviews with GDEs about mental health, during Mental Health Awareness Month

“I don’t think I have work-life balance,” says Google Workplace GDE Alice Keeler. “I could use some. I’m not very good at self-care, either…my idea of a good time is coding.”

Alice may be humble, but she juggles numerous responsibilities successfully. In addition to her freelance programming work and the books she has published, she has five children, all of whom have various mental health challenges. An educator known for publishing add-ons, schedulers, and Google Classroom tips, Alice teaches math to high school seniors. She says they also struggle with mental health, often due to poverty and family issues.

“I see firsthand as an employer, mom, and teacher how mental health challenges affect people, yet we expect everyone to suck it up and go to work, attend school, and respond to family events,” she says. “I’ve really been thinking about this a lot, as I see the challenges my family and students are going through. I try to offer lots of grace and flexibility to others.”

She points out that mental health is very personal. “Of the 20 people I feel closest to in my life, no one solution would work for all of them,” she says.

Coding as self-care

In Alice’s experience, tech has provided a means of self-care, professional opportunity, and academic support. “I think one of the benefits of coding is that it doesn’t necessarily have to be done at a certain time and can offer some flexible creative options for people,” she says. “I can code at 3am, and no one cares. It’s not very social, which is helpful for people who struggle with social expectations.”

And during those coding sessions, Keeler builds creative solutions.

“You can make really cool things,” she says. “When I solve a problem with ten lines of code, it’s a nice way for me to feel valued.”

Alice has found the GDE community to be tremendously supportive, even though at first, she worried no one would want to hear from her.

“I post in the GDE chat, and people respond with, ‘Alice!’,” she says. “I teach math; I’m not a full-time coder. I’m self taught; everything I do, I figure out myself. I don’t feel like an imposter anymore. I’ve gotten 14 add-ons approved.”

She has realized over time that even “experts” are still learning.

“You think everyone knows everything, but they don’t, and people may be considered experts, but you can put something out there they hadn’t even thought of,” she says. “You realize quickly that it’s not like a tower, and you’ve reached the top, it’s more like scattered LEGOs: I know some of this, and some of that, and you know this, and it’s scattered.”

Alice’s coding expertise grew out of her desire to create technological solutions for herself and other teachers that simplified their processes and reduced stress. She’s enthusiastic about the educational technology tools that help both teachers and students decrease stress and improve well-being.

Educational technology for improved well-being

Alice appreciates classroom technology that makes life easier for teachers and students. For example, she cites the tablet as “one of the best things that ever happened to special education,” because it provides students with learning challenges an alternative way to share their thoughts and demonstrate their understanding of academic material. Alice explains that tablets and Chromebooks make it easy to give students extra time on assignments and assessments when needed.

“It brought in an enormous amount of inclusivity that had been impossible,” she says. “It literally gives some kids a voice; they can submit questions and responses digitally, without raising their hands.”

Alice’s focus, as an educator, developer, and parent, is on using technology to streamline tasks and balance responsibility, which reduces stress, improves well-being, and benefits her mental health. During the pandemic, she appreciated how technology allowed her to teach online, write code, and also be present for her family. She had more time to go to her kids’ events and was able to dial down her stress. Like all of us, she’s still figuring out what comes next, but she’s committed to supporting her loved ones and students.

Learn more about Alice on her website or on Twitter @alicekeeler

Source: Google Developers Blog

Google Workplace GDE Alice Keeler on balancing responsibilities and using coding as self-care

Posted by Janelle Kuhlman, Developer Relations Program Manager

The first of two interviews with GDEs about mental health, during Mental Health Awareness Month

“I don’t think I have work-life balance,” says Google Workplace GDE Alice Keeler. “I could use some. I’m not very good at self-care, either…my idea of a good time is coding.”

She points out that mental health is very personal. “Of the 20 people I feel closest to in my life, no one solution would work for all of them,” she says.

Coding as self-care

And during those coding sessions, Keeler builds creative solutions.

“You can make really cool things,” she says. “When I solve a problem with ten lines of code, it’s a nice way for me to feel valued.”

Alice has found the GDE community to be tremendously supportive, even though at first, she worried no one would want to hear from her.

She has realized over time that even “experts” are still learning.

Educational technology for improved well-being

Learn more about Alice on her website or on Twitter @alicekeeler

Source: Google Developers Blog

Finding courage and inspiration in the developer community

Posted by Monika Janota

How do we empower women in tech and equip them with the skills to help them become true leaders? One way is learning from others' successes and failures. Web GDEs—Debbie O'Brien, Julia Miocene, and Glafira Zhur—discuss the value of one to one mentoring and the impact it has made on their own professional and personal development.

A 2019 study showed that only 25% of keynote speakers at tech events are women, meanwhile 70% of female speakers mentioned being the only woman on a conference panel. One way of changing that is by running programs and workshops with the aim of empowering women and providing them with the relevant soft skills training, including public speaking, content creation, and leadership. Among such programs are the Women Developer Academy (WDA) and the Road to GDE, both run by Google's developer communities.

With more than 1000 graduates around the world, WDA is a program run by Women Techmakers for professional IT practitioners. To equip women in tech with speaking and presentation skills, along with confidence and courage, training sessions, workshops, and mentoring meetings are organized. Road to GDE, on the other hand, is a three-month mentoring program created to support people from historically underrepresented groups in tech on their path to becoming experts. What makes both programs special is the fact that they're based on a unique connection between mentor and mentee, direct knowledge sharing, and an individualized approach.

Julia Miocene

Some Web GDE community members have had a chance to be part of the mentoring programs for women as both mentors and mentees. Frontend developers Julia Miocene and Glafira Zhur are relatively new to the GDE program. They became Google Developers Experts in October 2021 and January 2022 respectively, after graduating from the first edition of both the Women Developer Academy and the Road to GDE; whilst Debbie O'Brien has been a member of the community and an active mentor for both programs for several years. They have all shared their experiences with the programs in order to encourage other women in tech to believe in themselves, take a chance, and to become true leaders.

Different paths, one goal

Although all three share an interest in frontend development, each has followed a very different path. Glafira Zhur, now a team leader with 12 years of professional experience, originally planned to become a musician, but decided to follow her other passion instead. A technology fan thanks to her father, she was able to reinstall Windows at the age of 11. Julia Miocene, after more than ten years in product design, was really passionate about CSS. She became a GDE because she wanted to work with Chrome and DevTools. Debbie is a Developer Advocate working in the frontend area, with a strong passion for user experience and performance. For her, mentoring is a way of giving back to the community, helping other people achieve their dreams, and become the programmers they want to be. At one point while learning JavaScript, she was so discouraged she wanted to give it up, but her mentor convinced her she could be successful. Now she's returning the favor.

Debbie O'Brien

As GDEs, Debbie, Glafira, and Julia all mention that the most valuable part of becoming experts is the chance to meet people with similar interests in technology, to network, and to provide early feedback for the web team. Mentoring, on the other hand, enables them to create, it boosts their confidence and empowers them to share their skills and knowledge—regardless of whether they're a mentor or a mentee.

Sharing knowledge

A huge part of being a mentee in Google's programs is learning how to share knowledge with other developers and help them in the most effective way. Many WDA and Road to GDE participants become mentors themselves. According to Julia, it's important to remember that a mentor is not a teacher—they are much more. The aim of mentoring, she says, is to create something together, whether it's an idea, a lasting connection, a piece of knowledge, or a plan for the future.

Glafira mentioned that she learned to perceive social media in a new way—as a hub for sharing knowledge, no matter how small the piece of advice might seem. It's because, she says, even the shortest Tweet may help someone who's stuck on a technical issue that they might not be able to resolve without such content being available online. Every piece of knowledge is valuable. Glafira adds that, "Social media is now my tool, I can use it to inspire people, invite them to join the activities I organize. It's not only about sharing rough knowledge, but also my energy."

Working with mentors who have successfully built an audience for their own channels allows the participants to learn more about the technical aspects of content creation—how to choose topics that might be interesting for readers, set up the lighting in the studio, or prepare an engaging conference speech.

Learning while teaching

From the other side of the mentor—mentee relationship, Debbie O'Brien says the best thing about mentoring is seeing the mentees grow and succeed: "We see in them something they can't see in themselves, we believe in them, and help guide them to achieve their goals. The funny thing is that sometimes the advice we give them is also useful for ourselves, so as mentors we end up learning a lot from the experience too."

Glafira Zhur

Both Glafira and Julia state that they're willing to mentor other women on their way to success. Asked what is the most important learning from a mentorship program, they mention confidence—believing in yourself is something they want for every female developer out there.

Growing as a part of the community

Both Glafira and Julia mentioned that during the programs they met many inspiring people from their local developer communities. Being able to ask others for help, share insights and doubts, and get feedback was a valuable lesson for both women.

Mentors may become role models for the programs' participants. Julia mentioned how important it was for her to see someone else succeed and follow in their footsteps, to map out exactly where you want to be professionally, and how you can get there. This means learning not just from someone else's failures, but also from their victories and achievements.

Networking within the developer community is also a great opportunity to grow your audience by visiting other contributors' podcasts and YouTube channels. Glafira recalls that during the Academy, she received multiple invites and had an opportunity to share her knowledge on different channels.

Overall, what's even more important than growing your audience is finding your own voice. As Debbie states: "We need more women speaking at conferences, sharing knowledge online, and being part of the community. So I encourage you all to be brave and follow your dreams. I believe in you, so now it's time to start believing in yourself."

Source: Google Developers Blog

GDE community highlight: Nishu Goel

Posted by Monika Janota, Community Manager

Nishu Goel is a renowned web engineer from India, Google Developer Expert for Angular and web technologies, Microsoft Most Valuable Professional. She’s the author of Step by Step Angular Routing (BPB, 2019) and A Hands-on Guide to Angular (Educative, 2021) as well as the author of Web Almanac 2021 JavaScript chapter. Nishu currently works at epilot GmbH as a full stack engineer. She told us about her community involvement, career plans and the best ways to learn web development.

Monika: Let’s start with your story. What inspired you to become a developer and take on an active role within dev communities?

Nishu: I got my bachelor’s degree in computer science, we studied data structures, and that’s where the interest in programming started. During the third year of engineering, a connection with the developer community was established through my participation in the Microsoft Imagine Cup Nationals competition where we presented solutions through code. The idea of the application we built was to bring educational opportunities to local students, especially girls. I met some very inspiring people, both contestants and organizers in this journey.

In 2018, my professional career took off, and I started working with Angular. Angular became the primary technology that connected me to the GDE program. Around the same time, I started writing blog posts and creating content around the subject I was working on and learning . Dhananjay Kumar helped me get started on this journey and ensured to keep me on track. My first articles tackled the basics of Angular. Soon after I started speaking at events-the first one being ngNepal, Nepal’s Angular Conference. This led to more speaking invitations about Angular and web technologies.

GDE Nishu Goel stands in the middle of the photo with 4 men on her left and 4 men on her right. They all look into the camera with half smiles

Monika: What’s your professional experience technology-wise?

Nishu: It was all about Angular and web components for the first two years. I was using Angular for building the web, but soon I decided to go beyond that and explore other fields. I didn’t want to limit myself in case I’d have to switch projects. That’s how I started creating web components in Angular to use in other frameworks.

The first thing I did was to create web components using Angular. I published it to npm and used it as a demo in a React project. I’ve discussed this during some of my talks and presentations later. My next job required using React and Typescript. Now, because I was working with React, I wasn’t just using one framework anymore, but the web in general. At that moment I learned a lot about the web, especially web performance. That’s when I had to start thinking about the Largest Contenful Paint (LCP) or First Contentful Paint (FCP), which means how much time it will take your application to load or what’s going to be the maximum time for the page to render. I have been working towards choosing best practices and an improved performance of the applications.

Because of this interest in web performance, I got involved in the Web Almanac and wrote the JavaScript chapter. Web Almanac is an annual report on the state of the web in general — it tells us how people are using different features. Last year 8.6 million websites were screened, the data was analyzed and presented in the report. The report includes statistics like the usage of the async and defer attributes in a <script> element. How many websites are using them correctly, how many are not using that at all, and how many improved those compared to 2020. The last Web Almanac report mentioned that around 35% of websites used two attributes on the same script, which was an anti-pattern, decreasing the performance. This was pointed out last year, and this year we tried to see if the situation improved. I also spoke at ngConf and Reliable Dev Summit, where I focused on the performance of the web.

Close up of the front of a book titled 2021 Web Almanac, HTTP Archive's Annual state of the web report

Monika: You’re also very much involved in giving back to the community. Lately you’ve been volunteering with a Ugandan NGO YIYA — how did it start and what was the main point of that cooperation?

Nishu: It started with the GDE team informing us about the volunteering opportunity with YIYA. The Ugandan NGO was looking for engineers to help them with either the content preparation or technical features. The program aims to empower school-aged children in Uganda and offer them education opportunities using the technologies available locally — not computers or textbooks, but rather basic keypad phones and radios. The children would dial a certain number and receive a set of information, dial another one for more insights, and so on. It became even more useful during the pandemic.

Since I’ve always been involved with the community and sustainable development goals, I decided to reach out. After a meeting with the YIYA team, I offered my help with the Python scripts or any bugs they came up with, any issues with the portal. We worked together for a brief amount of time.

Monika: What are your plans for 2022? Is there anything you’re focusing on in particular?

Nishu: I’m switching jobs and moving to another country. I’ll be working on the web in general, improving the site performance, and also on the backend, using Golang. I’ll continue to zero in on the web performance area since it’s very interesting and complex, and there’s a lot to understand and optimize. Even now, after dedicating a lot of my time to that, there’s still so much to learn. For example, I’d love to understand how using a CDN for my image resources would help me make my app even faster. I want to become THE expert of web performance — I’m gradually getting there, I like to believe :)

Monika: You’ve mentioned starting to write at a point when you were not an expert, you were just writing what you were learning. What would your advice be to new developers coming through and feeling they don’t have anything to share?

Nishu: That’s exactly how I felt when I started writing. I thought that maybe I should not put this out? Maybe it’s just wrong? I was worried my writing was not going to help the readers. But the important thing was that my writing was helping me. I would forget things after some time and then come back to something I wrote earlier. Writing things down is a great idea.

Close up of the front of a book titled Step-by-step Angular Routing, authored by Nishu Goel

So I would suggest everyone — just write, at whatever stage. Even if you’ve only finished one part of a course you’re going through — you’re learning by writing it down. A piece of information that you got to know at some point may be useful to others who don’t know that yet. You don’t need to be an expert. Writing will help you. And anyone, at any stage of their career.

Monika: It’s best to follow people who just learned something because they know all the things they had to figure out. Once you’re an expert, it’s hard to remember what it was like when you were new. And any advice for someone who’s just getting into web development?

Nishu: Many people ask which framework they should choose when they’re starting, but I think that’s not the right question. Whatever we are learning at any point should be useful at a later stage as well. I would advise anyone to drop the limitations and start with HTML or JavaScript — that’s going to be profitable in the future.

And then take any opportunity that comes your way. This happened to me when I stumbled upon information about the Web Almanac looking for authors. I just thought, “oh, this is interesting, this may help everyone with the performance side of things”. That’s how I became a content lead for the JavaScript chapter, and I’ve spent six months writing it. So I think it’s just about grabbing the opportunities and working hard.

Monika: Do you have any predictions or ideas about the future of web technology in general? What’s going to be the next hot topic? What’s going to be growing fast?

Nishu: I love the fact that we’re able to run servers within browsers now, this is a great advancement. For example, running Node.js from the browser has been introduced lately, meanwhile in the past we could not run anything without having Node.js installed in our systems. Now we can do anything from the browser. This is a huge step further in the web ecosystem. And the OMT — Off the Main Thread. Working on the threads is going to be much improved as well. Web Assembly is advancing and enables developers to do that, and I think that is the future of the web ecosystem.