Posted by Laura Cincera, Program Manager Google Developer Student Clubs Europe
Mental health remains one of the most neglected areas of healthcare worldwide, with nearly 1 billion people currently living with a mental health condition that requires support. But what if there was a way to make mental health care more accessible and tailored to individual needs?
The Google Developer Student Clubs Solution Challenge aims to inspire and empower university students to tackle our most pressing challenges - like mental health. The Solution Challenge is an annual opportunity to turn visionary ideas into reality and make a real-world impact using the United Nations' 17 Sustainable Development Goals as a blueprint for action. Students from all over the world work together and apply their skills to create innovative solutions using Google technology, creativity and the power of community.
One of last year’s top Solution Challenge proposals, Xtrinsic, was a cooperation between two communities of student leaders - GDSC Freiburg in Germany and GDSC Kyiv in Ukraine. The team developed an innovative mental health research and therapy application that adapts to users' personal habits and needs providing effective support at scale.
Using a wearable device and TensorFlow, Xtrinsic helps users manage their symptoms by providing customized behavioral suggestions based on their physiological signs. It acts as an intervention tool for mental health issues such as nightmares, panic attacks, and anxiety and adapts the user's environment to their specific needs - which is essential for effective interventions. For example, if the user experiences a panic attack, the app detects the physiological signs using a smartwatch and a machine learning model, and triggers appropriate action, such as playing relaxing sounds, changing the room light to blue, or starting a guided breathing exercise. The solution was built using several Google technologies, including Android, Assistant/Actions on Google, Firebase, Flutter, Google Cloud, TensorFlow, WearOS, DialogFlow, and Google Health Services.
The team behind Xtrinsic is diverse. Alexander, Chikordili, Emma and Vandysh come from different backgrounds but share a passion for AI and how it can be leveraged to improve the lives of many. They all recognize the importance of shedding awareness on mental health and creating a supportive culture that is free from stigma. Their personal experiences in conflict areas, such as Syria and Ukraine inspired them to develop the application.
Xtrinsic was recognized as one of the Top 3 winning teams in the 2022 Google Solution Challenge for its innovative approach to mental health research and therapy. The team has since supported several other social impact initiatives - helping grow the network of entrepreneurs and community leaders in Europe and beyond.
Learn more about Google Developer Student Clubs
If you feel inspired to make a positive change through technology, submit your project to SolutionChallenge 2023 here. And if you’re passionate about technology and are ready to use your skills to help your local community, then consider becoming a Google Developer Student Clubs Lead!
We encourage all interested university students to apply here and submit their applications as soon as possible. The applications in Europe, India, North America and MENA are currently open.
Learn more about Google Developer Student Clubs here.
Posted by Lyanne Alfaro, DevRel Program Manager, Google Developer Studio
In honor of Women’s History Month, it’s our pleasure to feature members across the Women Techmakers ecosystem for March’s Developer Journey profiles. These are community leaders who have explored, navigated and built using Google tools. They are active members of the broader Google Developers community.
In March, the WTM program will also celebrate International Women’s Day, centered on the theme “Dare To Be,” celebrating the courage and strength that this community demonstrates, made of thought leaders who are creating a world where women can thrive in tech. You can find more about the Women Techmakers program during IWD here.
Ezinne Osuamadi
Women Techmakers Mentor and Ambassador Waldorf, Germany (A proud Nigerian!) Software Developer/ Technical Product Manager Twitter Linkedln Instagram
What Google tools have you used to build?
Android Studio, Firebase, Google Play Services, Google Analytics. I'm a mobile developer and recently started getting my hands on technical product management and agile product owner. The tools I use for development are Android as the framework and Android Studio as the integrated development environment.
Which tool has been your favorite to use? Why?
I would say Flutter. The Flutter toolkit has a layered architecture that allows for full customization. The fact that Flutter comes with fully-customizable widgets allows you to build native interfaces in minutes. I also love the fact that some of these widgets’ features like scrolling, navigation, icons, and fonts provide a full native performance on both iOS and Android. Flutter is one code base and it makes building mobile applications much easier. I don't have to build a separate app for Android, and another separate app for IOS. Another Flutter feature I like so much is the “hot reload.” It allows me to easily build UIs, add new features, and fix bugs faster. It also allows easy compilation of Flutter code to native ARM machine code using Dart native compilers.
Please share with us about something you’ve built in the past using Google tools.
The first app I built was for one of my former employers. It happened almost three years ago, and it was the first project I worked on when I started learning Flutter. I was super excited about it. It was a timesheet app targeted specifically for employees. The sole purpose of the app is for employees to be able to schedule tasks and also give a time slot to each task.
What advice would you give someone starting in their developer journey?
From my experience running an NGO called Ladies Crushing IT Africa and organizing a couple of tech events, I would say this: Don’t go into software development if you are not passionate or interested in it. Going into development because you think they pay developers well or because your friends are earning money from it is a wrong reason to start your development journey. A tech career journey should be about what you want to be in the future. Does it align with your future goals and objectives? How or what are strategies in achieving that path? Also note that the path to becoming a successful developer is a process. It is not all roses, and there are times when debugging will make it look difficult. But you should be resilient and diligent in making the most out of it when you encounter difficulties. It is always about continuous improvement. Never stop learning to keep yourself up to date with latest technologies and development tools.
Patty O’Callaghan
GDG Glasgow and Women Techmakers Ambassador Glasgow, Scotland Tech Lead @ Charles River Laboratories Twitter Linkedln What Google tools have you used to build?
I use the Chrome DevTools daily. I find them very helpful. I also enjoy working on projects using TensorFlow.JS and Firebase.
Which tool has been your favorite to use? Why?
I would have to say TensorFlow.JS and its pre-made models are my favorite. I enjoy the fact that I can build cool machine learning projects directly in the browser. Even developers unfamiliar with this technology can quickly build, train, and deploy machine learning models using just a few lines of code. Some kids at my code club have used TensorFlow.JS for amazing projects, like building class attendance applications using facial recognition, or a site that checks correct form while practicing karate at home, and another for studying with the help of an AI agent.
Please share with us about something you’ve built in the past using Google tools.
I've worked on several side-projects using TensorFlow.JS for my workshops. One of my favorites is an emotion recognition app, using the Teachable Machine. Additionally, for work, I used TF.JS to develop a machine learning solution that suggests taxonomies for articles based on their content. It analyzes over 30 taxonomies to find the best match for the given article.
What advice would you give someone starting in their developer journey?
First of all, focus on learning the fundamentals of programming. A strong foundation will benefit you in the long run. Practice coding regularly and find a mentor or a community to help you along the way. For example, contributing to an open-source project is an excellent way to learn. And remember: Making mistakes is a natural part of the learning process, so don't get discouraged if you encounter difficulties. Keep pushing forward!
Firestore has been our favorite due to its scalability and real-time data capabilities, through websockets and triggers, the data flexibility, plus query capabilities. This is how we’ve built out our modern event-driven architecture to allow for a completely real-time application providing immediate data and collaboration across our entire white label application suite.
Please share with us about something you’ve built in the past using Google tools.
We built the WeTransact Innovation Platform: From Idea to ROI which offers a learning-based distributed social platform for learning, collaborating and presenting yourself and your innovations.
For customers, we’ve created a White Label SaaS Platform, licensed by universities, incubators, developer groups and any program looking to provide education, collaboration, and AI assisted auto generated presentation and communication tools. Our platform combines features similar to LinkedIn, Coursera, AngelList and Zoom in one simple and modern unified platform for communities to make collaboration & lifelong learning globally accessible to everyone. The WeTransact platform accelerates & scales your program’s impact to solve the world's biggest problems better together.
Here’s just a few other ways we’ve used Google tools:
What advice would you give someone starting in their developer journey?
There’s a few pieces of advice we’d offer! Among them is to start early. Find a friend who is already developing or shares your passion. Find an open source project that inspires you or represents something you're passionate about. Dig in, change stuff, break stuff and then learn why. Search is your best friend – use it to always question and reset your assumptions, learn new approaches, and practice not getting stuck in a “boilerplate” or “standard” solution to each problem. It’s not about memorizing – technology changes every day and you should too. Finally, know that it’s about the process and the journey, not the destination.
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 last quarter of 2022. We are enthusiastic and grateful about all the activities by the global network of ML communities. Here are the highlights!
ML at DevFest 2022
A large number of members of ML GDE, TFUG, and 3P ML communities participated in DevFests 2022 worldwide covering various ML topics with Google products. Machine Learning with Jax: Zero to Hero (DevFest Conakry) by ML GDE Yannick Serge Obam Akou (Cameroon) and Easy ML on Google Cloud (DevFest Med) by ML GDE Nathaly Alarcon Torrico (Bolivia) hosted great sessions.
ML Community Summit 2022
ML Community Summit 2022 was hosted on Oct 22-23, 2022, in Bangkok, Thailand. Twenty-five most active community members (ML GDE or TFUG organizer) were invited and shared their past activities and thoughts on Google’s ML products. A video sketch from ML Developer Programs team and a blog posting by ML GDE Margaret Maynard-Reid (United States) help us revisit the moments.
TensorFlow
MAXIM in TensorFlow by ML GDE Sayak Paul (India) shows his implementation of the MAXIM family of models in TensorFlow.
Building Computer Vision Model using TensorFlow: Part 2 by TFUG Pune for the developers who want to deep dive into training an object detection model on Google Colab, inspecting the TF Lite model, and deploying the model on an Android application. ML GDE Nitin Tiwari (India) covered detailed aspects for end-to-end training and deployment of object model detection.
Advent of Code 2022 in pure TensorFlow (days 1-5) by ML GDE Paolo Galeone (Italy) solving the Advent of Code (AoC) puzzles using only TensorFlow. The articles contain a description of the solutions of the Advent of Code puzzles 1-5, in pure TensorFlow.
A hands-on course on TensorFlow Lite projects on Android by ML GDE Xiaoxing Wang (China) is the book mainly introducing the application of TensorFlow Lite in Android development. The content focuses on applying three typical ML applications in Android development.
Build tensorflow-lite-select-tf-ops.aar and tensorflow-lite.aar files with Colab by ML GDE George Soloupis (Greece) guides how you can shrink the final size of your Android application’s .apk by building tensorflow-lite-select-tf-ops.aar and tensorflow-lite.aar files without the need of Docker or personal PC environment.
TensorFlow Lite and MediaPipe Application by ML GDE XuHua Hu (China) explains how to use TFLite to deploy an ML model into an application on devices. He shared experiences with developing a motion sensing game with MediaPipe, and how to solve problems that we may meet usually.
Train and Deploy TensorFlow models in Go by ML GDE Paolo Galeone (Italy) delivered the basics of the TensorFlow Go bindings, the limitations, and how the tfgo library simplifies their usage.
Let’s Generate Images with Keras based Stable Diffusion by ML GDE Chansung Park (Korea) delivered how to generate images with given text and what stable diffusion is. He also talked about Keras-based stable diffusion, basic building blocks, and the advantages of using Keras-based stable diffusion.
How startups can benefit from TFX by ML GDE Hannes Hapke (United States) explains how the San Francisco-based FinTech startup Digits has benefitted from applying TFX early, how TFX helps Digits grow, and how other startups can benefit from TFX too.
Hyperparameter Tuning and ML Pipeline by ML GDE Chansung Park (Korea) explained hyperparam tuning, why it is important; Introduction to KerasTuner, basic usage; how to visualize hyperparam tuning results with TensorBoard; and integration within ML pipeline with TFX.
Introduction to JAX with Flax (slides) by ML GDE Phillip Lippe (Netherlands) reviewed from the basics of the requirements we have on a DL framework to what JAX has to offer. Further, he focused on the powerful function-oriented view JAX offers and how Flax allows you to use them in training neural networks.
JAX Streams: Exploring JAX 0.4 by ML GDE David Cardozo (Canada) and Cristian Garcia (Colombia) showed a review of new features (specifically Shared Arrays) in the recent release of JAX and demonstrated live coding.
Better Hardware Provisioning for ML Experiments on GCP by ML GDE Sayak Paul (India) discussed the pain points of provisioning hardware (especially for ML experiments) and how we can get better provision hardware with code using Vertex AI Workbench instances and Terraform.
MLOps workshop with TensorFlow and Vertex AI by TFUG Chennai targeted beginners and intermediate-level practitioners to give hands-on experience on the E2E MLOps pipeline with GCP. In the workshop, they shared the various stages of an ML pipeline, the top tools to build a solution, and how to design a workflow using an open-source framework like ZenML.
More practical time-series model with BQML by ML GDE JeongMin Kwon (Korea) introduced BQML and time-series modeling and showed some practical applications with BQML ARIMA+ and Python implementations.
Vertex AI Forecast - Demand Forecasting with AutoML by ML GDE Rio Kurihara (Japan) presented a time series forecast overview, time series fusion transformers, and the benefits and desired features of AutoML.
Research & Ecosystem
AI in Healthcare by ML GDE Sara EL-ATEIF (Morocco) introduced AI applications in healthcare and the challenges facing AI in its adoption into the health system.
Women in AI APAC finished their journey at ML Paper Reading Club. During 10 weeks, participants gained knowledge on outstanding machine learning research, learned the latest techniques, and understood the notion of “ML research” among ML engineers. See their session here.
Chat with ML GDE Vikram & Chat with ML GDE Aakash by ML GDE Margaret Maynard-Reid (United States) shared the stories of ML GDEs’ including how they became ML GDE and how they proceeded with their ML projects.
Anatomy of Capstone ML Projects 🫀by ML GDE Sayak Paul (India) discussed working on capstone ML projects that will stay with you throughout your career. He covered various topics ranging from problem selection to tightening up the technical gotchas to presentation. And in Improving as an ML Practitioner he shared his learning from experience in the field working on several aspects.
MLOps Development Environment by ML GDE Vinicius Caridá (Brazil) aims to build a full development environment where you can write your own pipelines connecting MLFLow, Airflow, GCP and Streamlit, and build amazing MLOps pipelines to practice your skills.
Posted by Paul McCartney, Software Engineer, Vivek Kwatra, Research Scientist, Yu Zhang, Research Scientist, Brian Colonna, Software Engineer, and Mor Miller, Software Engineer
People increasingly look to video as their preferred way to be better informed, to explore their interests, and to be entertained. And yet a video’s spoken language is often a barrier to understanding. For example, a high percentage of YouTube videos are in English but less than 20% of the world's population speaks English as their first or second language. Voice dubbing is increasingly being used to transform video into other languages, by translating and replacing a video’s original spoken dialogue. This is effective in eliminating the language barrier and is also a better accessibility option with regard to both literacy and sightedness in comparison to subtitles.
In today’s post, we share our research for increasing voice dubbing quality using deep learning, providing a viewing experience closer to that of a video produced directly for the target language. Specifically, we describe our work with technologies for cross-lingual voice transfer and lip reanimation, which keeps the voice similar to the original speaker and adjusts the speaker’s lip movements in the video to better match the audio generated in the target language. Both capabilities were developed using TensorFlow, which provides a scalable platform for multimodal machine learning. We share videos produced using our research prototype, which are demonstrably less distracting and - hopefully - more enjoyable for viewers.
Cross-Lingual Voice Transfer
Voice casting is the process of finding a suitable voice to represent each person on screen. Maintaining the audience’s suspension of disbelief by having believable voices for speakers is important in producing a quality dub that supports rather than distracts from the video. We achieve this through cross-lingual voice transfer, where we create synthetic voices in the target language that sound like the original speaker voices. For example, the video below uses an English dubbed voice that was created from the speaker’s original Spanish voice.
Original “Coding TensorFlow” video clip in Spanish.
The “Coding TensorFlow” video clip dubbed from Spanish to English, using cross-lingual voice transfer and lip reanimation.
Inspired by few-shot learning, we first pre-trained a multilingual TTS model based on our cross-language voice transfer approach. This approach uses an attention-based sequence-to-sequence model to generate a series of log-mel spectrogram frames from a multilingual input text sequence with a variational autoencoder-style residual encoder. Subsequently, we fine-tune the model parameters by retraining the decoder and attention modules with a fixed mixing ratio of the adaptation data and original multilingual data as illustrated in Figure 1.
Figure 1: Voice transfer architecture
Note that voice transfer and lip reanimation is only done when the content owner and speakers give consent for these techniques on their content.
Lip Reanimation
With conventionally dubbed videos, you hear the translated / dubbed voices while seeing the original speakers speaking the original dialogue in the source language. The lip movements that you see in the video generally do not match the newly dubbed words that you hear, making the combined audio/video look unnatural. This can distract viewers from engaging fully with the content. In fact, people often even intentionally look away from the speaker’s mouth while watching dubbed videos as a means to avoid seeing this discrepancy.
To help with audience engagement, producers of higher quality dubbed videos may put more effort into carefully tailoring the dialogue and voice performance to partially match the new speech with the existing lip motion in video. But this is extremely time consuming and expensive, making it cost prohibitive for many content producers. Furthermore, it requires changes that may slightly degrade the voice performance and translation accuracy.
To provide the same lip synchronization benefit, but without these problems, we developed a lip reanimation architecture for correcting the video to match the dubbed voice. That is, we adjust speaker lip movements in the video to make the lips move in alignment with the new dubbed dialogue. This makes it appear as though the video was shot with people originally speaking the translated / dubbed dialogue. This approach can be applied when permitted by the content owner and speakers.
For example, the following clip shows a video that was dubbed in the conventional way (without lip reanimation):
"Machine Learning Foundations” video clip dubbed from English to Spanish, with voice transfer, but without lip reanimation
Notice how the speaker’s mouth movements don’t seem to move naturally with the voice. The video below shows the same video with lip reanimation, resulting in lip motion that appears more natural with the translated / dubbed dialogue:
The dubbed “Machine Learning Foundations” video clip, with both voice transfer and lip reanimation
For lip reanimation, we train a personalized multistage model that learns to map audio to lip shapes and facial appearance of the speaker, as shown in Figure 2. Using original videos of the speaker for training, we isolate and represent the faces in a normalized space that decouples 3D geometry, head pose, texture, and lighting, as described in this paper. Taking this approach allows our first stage to focus on synthesizing lip-synced 3D geometry and texture compatible with the dubbed audio, without worrying about pose and lighting. Our second stage employs a conditional GAN-based approach to blend these synthesized textures with the original video to generate faces with consistent pose and lighting. This stage is trained adversarially using multiple discriminators to simultaneously preserve visual quality, temporal smoothness and lip-sync consistency. Finally, we refine the output using a custom super-resolution network to generate a photorealistic lip-reanimated video. The comparison videos shown above can also be viewed here.
Figure 2: Lip-Reanimation Pipeline: inference blocks in blue, training blocks in red.
Aligning with our AI Principles
The techniques described here fall into the broader category of synthetic media generation, which has rightfully attracted scrutiny due to its potential for abuse. Photorealistically manipulating videos could be misused to produce fake or misleading information that can create downstream societal harms, and researchers should be aware of these risks. Our use case of video dubbing, however, highlights one potential socially beneficial outcome of these technologies. Our new research in voice dubbing could help make educational lectures, video-blogs, public discourse, and other formats more widely accessible across a global audience. This is also only applied when consent has been given by the content owners and speakers.
During our research, we followed our guiding AI Principles for developing and deploying this technology in a responsible manner. First, we work with the creators to ensure that any dubbed content is produced with their consent, and any generated media is identifiable as such. Second, we are actively working on tools and techniques for attributing ownership of original and modified content using provenance and digital watermarking techniques. Finally, our central goal is fidelity to the source-language video. The techniques discussed herein serve that purpose only -- namely, to amplify the potential social benefit to the user, while preserving the content’s original nature, style and creator intent. We are continuing to determine how best to uphold and implement data privacy standards and safeguards before broader deployment of our research.
The Opportunity Ahead
We strongly believe that dubbing is a creative process. With these techniques, we strive to make a broader range of content available and enjoyable in a variety of other languages.
We hope that our research inspires the development of new tools that democratize content in a responsible way. To demonstrate its potential, today we are releasing dubbed content for two online educational series, AI for Anyone and Machine Learning Foundations with Tensorflow on the Google Developers LATAM channel.
We have been actively working on expanding our scope to more languages and larger demographics of speakers — we have previously detailed this work, along with a broader discussion, in our research papers on voice transfer and lip reanimation.
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 third 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
Load-testing TensorFlow Serving’s REST Interface by ML GDE Sayak Paul (India) and Chansung Park (Korea) shares the lessons and findings they learned from conducting load tests for an image classification model across numerous deployment configurations.
Deploying ? ViT on Kubernetes with TF Serving by ML GDE Sayak Paul (India) and Chansung Park (Korea) shows how to scale the deployment of a ViT model from ? Transformers using Docker and Kubernetes.
Text Augmentation in Keras NLP by ML GDE Xiaoquan Kong (China) explains what text augmentation is and how the text augmentation feature in Keras NLP is designed.
CryptoGANs open-source repository by ML GDE Dimitre Oliveira (Brazil) shows simple model implementations following TensorFlow best practices that can be extended to more complex use-cases. It connects the usage of TensorFlow with other relevant frameworks, like HuggingFace, Gradio, and Streamlit, building an end-to-end solution.
TFX
MLOps for Vision Models from ? with TFX by ML GDE Chansung Park (Korea) and Sayak Paul (India) shows how to build a machine learning pipeline for a vision model (TensorFlow) from ? Transformers using the TF ecosystem.
First release of TFX Addons Package by ML GDE Hannes Hapke (United States). The package has been downloaded a few thousand times (source). Google and other developers maintain it through bi-weekly meetings. Google’s Open Source Peer Award has recognized the work.
TFUG São Paulo hosted TFX T1 | E4 & TFX T1 | E5. And ML GDE Vinicius Caridá (Brazil) shared how to train a model in a TFX pipeline. The fifth episode talks about Pusher: publishing your models with TFX.
Semantic Segmentation model within ML pipeline by ML GDE Chansung Park (Korea) and Sayak Paul (India) shows how to build a machine learning pipeline for semantic segmentation task with TFX and various GCP products such as Vertex Pipeline, Training, and Endpoints.
JAX/Flax
JAX Tutorial by ML GDE Phillip Lippe (Netherlands) is meant to briefly introduce JAX, including writing and training neural networks with Flax.
TFUG Malaysia hosted Introduction to JAX for Machine Learning (video) and Leong Lai Fong gave a talk. The attendees learned what JAX is and its fundamental yet unique features, which make it efficient to use when executing deep learning workloads. After that, they started training their first JAX-powered deep learning model.
TFUG Taipei hosted Python+ JAX + Image classification and helped people learn JAX and how to use it in Colab. They shared knowledge about the difference between JAX and Numpy, the advantages of JAX, and how to use it in Colab.
Introduction to JAX by ML GDE João Araújo (Brazil) shared the basics of JAX in Deep Learning Indaba 2022.
Should I change from NumPy to JAX? by ML GDE Gad Benram (Portugal) compares the performance and overview of the issues that may result from changing from NumPy to JAX.
Introduction to JAX: efficient and reproducible ML framework by ML GDE Seunghyun Lee (Korea) introduced JAX/Flax and their key features using practical examples. He explained the pure function and PRNG, which make JAX explicit and reproducible, and XLA and mapping functions which make JAX fast and easily parallelized.
Data2Vec Style pre-training in JAX by ML GDE Vasudev Gupta (India) shares a tutorial for demonstrating how to pre-train Data2Vec using the Jax/Flax version of HuggingFace Transformers.
In Grandmasters Journey on Kaggle + The Kaggle Book, ML GDE Luca Massaron (Italy) explained how Kaggle helps people in the data science industry and which skills you must focus on apart from the core technical skills.
In Using machine learning to transform finance with Google Cloud and Digits, ML GDE Hannes Hapke (United States) chats with Fillipo Mandella, Customer Engineering Manager at Google, about how Digits leverages Google Cloud’s machine learning tools to empower accountants and business owners with near-zero latency.
A tour of Vertex AI by TFUG Chennai for ML, cloud, and DevOps engineers who are working in MLOps. This session was about the introduction of Vertex AI, handling datasets and models in Vertex AI, deployment & prediction, and MLOps.
Deploying ? ViT on Vertex AI by ML GDE Sayak Paul (India) and Chansung Park (Korea) shows how to deploy a ViT B/16 model on Vertex AI. They cover some critical aspects of a deployment such as auto-scaling, authentication, endpoint consumption, and load-testing.
TFUG Singapore hosted The World of Diffusion - DALL-E 2, IMAGEN & Stable Diffusion. ML GDE Martin Andrews (Singapore) and Sam Witteveen (Singapore) gave talks named “How Diffusion Works” and “Investigating Prompt Engineering on Diffusion Models” to bring people up-to-date with what has been going on in the world of image generation.
ML Paper Reading Clubs #1: Zero Shot Learning Paper (video) by TFUG Agadir introduced a model that can recognize objects in images even if no training data is available for the objects. TFUG Agadir prepared this event to make people interested in machine learning research and provide them with a broader vision of differentiating good contributions from great ones.
Plant disease classification using Deep learning model by ML GDE Yannick Serge Obam Akou (Cameroon) talked on plant disease classification using deep learning model : an end to end Android app (open source project) that diagnoses plant diseases.
Nystromformer Github repository by Rishit Dagli provides TensorFlow/Keras implementation of Nystromformer, a transformer variant that uses the Nyström method to approximate standard self-attention with O(n) complexity which allows for better scalability.
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 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 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.
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.
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 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.
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.
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).
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.
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.
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!
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 first quarter of the year! We are enthusiastic and grateful about all the activities that the communities across the globe do. Here are the highlights!
ML Ecosystem Campaign Highlights
ML Olympiad is an associated Kaggle Community Competitions hosted by Machine Learning Google Developers Experts (ML GDEs) or TensorFlow User Groups (TFUGs) sponsored by Google. The first round was hosted from January to March, suggesting solving critical problems of our time. Competition highlights include Autism Prediction Challenge, Arabic_Poems, Hausa Sentiment Analysis, Quality Education, Good Health and Well Being. Thank you TFUG Saudi, New York, Guatemala, São Paulo, Pune, Mysuru, Chennai, Bauchi, Casablanca, Agadir, Ibadan, Abidjan, Malaysia and ML GDE Ruqiya Bin Safi, Vinicius Fernandes Caridá, Yogesh Kulkarni, Mohammed buallay, Sayed Ali Alkamel, Yannick Serge Obam, Elyes Manai, Thierno Ibrahima DIOP, Poo Kuan Hoong for hosting ML Olympiad!
Highlights and Achievements of ML Communities
TFUG organizer Ali Mustufa Shaikh (TFUG Mumbai) and Rishit Dagli won the TensorFlow Community Spotlight award (paper and code). This project was supported by provided Google Cloud credit.
ML GDE Ngoc Ba (Vietnam) posted MTet: Multi-domain Translation for English and Vietnamese. This project is about how to collect high quality data and train a state-of-the-art neural machine translation model for Vietnamese. And it utilized Google Cloud TPU, Cloud Storage and related GCP products for faster training.
ML GDE Margaret Maynard-Reid (USA), Nived P A, and Joel Shor posted Our Summer of Code Project on TF-GAN. This article describes enhancements made to the TensorFlow GAN library (TF-GAN) of the last summer.
ML GDE Aakash Nain (India) released a series of tutorials about building models in JAX. In the second tutorial, Aakash uses one of the most famous and most widely used high-level libraries for Jax to build a classifier. In the notebook, you will be taking a deep dive into Flax, too.
ML GDE Bhavesh Bhatt (India) built a model for braille to audio with 95% accuracy. He created a model that translates braille to text and audio, lending a helping hand to people with visual disabilities.
Welcome to #IamaGDE - a series of spotlights presenting Google Developer Experts (GDEs) from across the globe. Discover their stories, passions, and highlights of their community work.
Leigh Johnson turned her childhood love of Geocities and Neopets into a web development career, and then trained her focus on Machine Learning. Now, she’s a staff software engineer at Slack, a Google Developer Expert in Web and Machine Learning, and founder of Print Nanny, an automated failure detection system and monitoring system for 3D printers.
Meet Leigh Johnson, Google Developer Expert in Web and Machine Learning.
GDE Leigh Johnson
The early days
Leigh Johnson grew up in the Bronx, NY, and got an early start in web development when she became captivated by Geocities and Neopets in elementary school.
“I loved the power of being able to put something online that other people could see, using just HTML and CSS,” she says.
She started college and studied Latin, but it wasn’t the right fit for her, so she dropped out and launched her own business building WordPress sites for small businesses, like local restaurants putting their menus online for the first time or taking orders through a form.
“I was 18, running around a data center trying to rack servers and teaching myself DNS to serve my customer base, which was small business owners,” she says. “I ran my business for five years, until companies like Squarespace and Wix started to edge me out of the market a little bit.”
Leigh went on to chase her dream of working in the video game industry, where she got exposed to low-level C++ programming, graphics engines, and basic statistics, which led her to machine learning.
Machine learning
At the video game studio where she worked, Leigh got into Bayesian inference.
“It’s old school machine learning, where you try to predict things based on the probability of previous events,” she explains. “You look at past events and try to predict the probability of future events, and I did this for marketing offers—what’s the likelihood you’d purchase a yellow hat to match your yellow pants?”
In the first month or two of trying email offers, the company made more small dollar sales than they typically made in a year.
“I realized, this is powerful dark magic; I must learn more,” Leigh says.
She continued working for tech startups like Ansible, which was acquired by Red Hat, and Dave.com, doing heavy data lifting.
“Everything about machine learning is powered by being able to manipulate and get data from point A to point B,” she says.
Today, Leigh works on machine learning and infrastructure at Slack and is a Google Developer Expert in machine learning. She also has a side project she runs: Print Nanny.
Print Nanny: Monitoring 3D printers
When Leigh got into 3D printing as a hobby during the COVID-19 shutdown, she discovered that 3D printers can be unreliable and lack sophisticated monitoring programs.
“When I assembled my 3D printer myself, I realized that over time, the calibration is going to change,” she says. “It's a very finicky process, and it didn't necessarily guarantee the quality of these traditional large batch manufacturing processes.”
She installed a nanny cam to watch her 3D printer and researched solutions, knowing from her machine learning experience that because 3D printers build a print up layer by layer, there’s no one point of failure—failure happens layer by layer, over time. So she wrote that algorithm.
“I saw an opportunity to take some of the traditional machine intelligence strategies used by large manufacturers to ensure there’s a certain consistency and quality to the things they produce, and I made Print Nanny,” she says. “It uses a Raspberry Pi, a credit card-sized computer that costs $30. You can stick a computer vision model on one and do offline inference, which are basically predictions about what the camera sees. You can make predictions about whether a print will fail, help score calculations, and attenuate the print.”
Leigh used Google Cloud Platform AutoML Vision, Google Cloud Platform IoT Core, TensorFlow Model Garden, and TensorFlow.js to build Print Nanny. Using GCP credits provided by Google, she improved and developed Print Nanny with TensorFlow and Google Cloud Platform products.
When Print Nanny detects that a print is failing, the user receives a notification and can remotely pause or stop the printer.
“Print Nanny is an automated failure detection system and monitoring system for 3D printers, which uses computer vision to detect defects and alert you to potential quality or safety hazards,” Leigh says.
Leigh has hired team members who are interested in machine learning to help her with the technical aspects of Print Nanny. Print Nanny currently has 2100 users signed up for a closed beta, with 200 people actively using the beta version. Of that group, 80% are hobbyists and 20% are small business owners. Print Nanny is 100% open source.
Becoming a GDE
Leigh got involved with the GDE program about four years ago, when she began putting machine learning models on Raspberry Pis and building robots. She began writing tutorials about what she was learning.
“The things I was doing were quite hard: TensorFlow Light, the mobile device of TensorFlow—there was a missing documentation opportunity there, and my target platform, the Raspberry Pi, is a hobbyist platform, so there was a little bit of missing documentation there,” Leigh says. “For a hobbyist who wanted to pick up a Raspberry Pi and do a computer vision project for the first time, there was a missing tutorial there, so I started writing about what I was doing, and the response was tremendous.”
Leigh’s work caught the eye of Google staff research engineer Pete Warden, the technical Lead of the TensorFlow Mobile team, who encouraged her, and she leveraged the GDE program to connect to Google experts on TensorFlow and machine learning. Google provides a machine learning course for developers and supports TensorFlow, in addition to its many AI products.
“I had no knowledge of graph programming or what it meant to adapt the low-level kernel operations that would run on a Raspberry Pi, or compiling software, and I learned all that through the GDE program,” Leigh says. “This program changed my life.”
Leigh’s favorite part of the GDE program is going to events like TensorFlow World, which she last attended in 2019, and GDE summits. She hadn’t travelled internationally until she was in her 20’s, so the GDE program has connected her to the international community.
“It’s been life-changing,” she says. “I never would have had access to that many perspectives. It’s changed the way I view the world, my life, and myself. It’s very powerful.”
Leigh’s advice to future developers
Leigh recommends that people find the best environment for themselves and adopt a growth mindset.
“The best advice that I can give is to find your motivation and find the environment where you can be successful,” she says. “Surround yourself with people who are lifelong learners. When you cultivate an environment of learning around you, it's this positive, self-perpetuating process.”
Posted by HyeJung Lee, DevRel Community Manager and Soonson Kwon, DevRel Program Manager
Let’s explore highlights and achievements of vast Google Machine Learning communities by region for the last quarter. Activities of experts (GDE, professional individuals), communities (TFUG, TensorFlow user groups), students (GDSC, student clubs), and developers groups (GDG) are presented here.
Key highlights
30 days of ML with Kaggle is designed to help beginners study ML using Kaggle Learn courses as well as a competition specifically for the participants of this program. Collaborated with the Kaggle team so that +30 the ML GDEs and TFUG organizers participated as volunteers as online mentors as well as speakers for this initiative.
GDE Minori MATSUDA (Japan)’s project on Coca-Cola Bottlers Japan was published on Google Cloud Japan Blog covering creating an ML pipeline to deploy into real business within 2 months by using Vertex AI. This is also published on GCP blog in English.
GDE Chansung Park (Korea) and Sayak Paul (India) published many articles on GCP Blog. First, “Image search with natural language queries” explained how to build a simple image parser from natural language inputs using OpenAI's CLIP model. From this second “Model training as a CI/CD system: (Part I, Part II)” post, you can learn more about why having a resilient CI/CD system for your ML application is crucial for success. Last, “Dual deployments on Vertex AI” talks about end-to-end workflow using Vertex AI, TFX and Kubeflow.
In China, GDE Junpeng Ye used TensorFlow 2.x to significantly reduce the codebase (15k → 2k) on WeChat Finder which is a TikTok alternative in WeChat. GDE Dan lee wrote an article on Understanding TensorFlow Series: Part 1, Part 2, Part 3-1, Part 3-2, Part 4
GDE Matthew Kelcey from Australia gave a talk on JAX at PyConAU event. Mat gave an overview to fundamentals of JAX and an intro to some of the libraries being developed on top.
In Singapore, TFUG Singapore dived back into some of the latest papers, techniques, and fields of research that are delivering state-of-the-art results in a number of fields. GDE Martin Andrews included a brief code walkthrough for the released PerceiverIO code at perceiver- highlighting what JAX looks like, how Haiku relates to Sonnet, but also the data loading stuff which is done via tf.data.
GDE Aakash Nain has published the TF-JAX tutorial series from Part 4 to Part 8. Part 4 gives a brief introduction about JAX (What/Why), and DeviceArray. Part 5 covers why pure functions are good and why JAX prefers them. Part 6 focuses on Pseudo Random Number Generation (PRNG) in Numpy and JAX. Part 7 focuses on Just In Time Compilation (JIT) in JAX. And Part 8 covers vmap and pmap.
GDE Sayak Paul and Soumik Rakshit shared a new NLP dataset for multi-label text classification. The dataset consists of paper titles, abstracts, and term categories scraped from arXiv.
North America
During the GSoC (Google Summer of Code), some GDEs mentored or co-mentored students. GDE Margaret Maynard-Reid (USA) mentored TF-GAN, Model Garden, TF Hub and TFLite products. You can get some of her experience and tips from the GDE Blog. And you can find GDE Sayak Paul (India) and Googler Morgan Roff’s GSoC experience in (co-)mentoring TensorFlow and TF Hub as well.
On the other side of the world, in Brazil, GDE Hugo Zanini Gomes wrote an article about “Custom object detection in the browser using TensorFlow.js” using the TensorFlow 2 Object Detection API and Colab was posted on the TensorFlow blog.
Data Pipelines for ML was talked about by GDE Nathaly Alarcon Torrico from Bolivia explained all the phases involved in the creation of ML and Data Science products, starting with the data collection, transformation, storage and Product creation of ML models.
TechTalk “Machine Learning Competitivo: Top 1% en Kaggle (Video)“ was hosted by TFUG Santiago (Chile). In this talk the speaker gave a tour of the steps to follow to generate a model capable of being in the top 1% of the Kaggle Leaderboard. The focus was on showing the libraries and“ tricks ”that are used to be able to test many ideas quickly both in implementation and in execution and how to use them in productive environments.
