Tag Archives: TF.js

Machine Learning Communities: Q2 ‘23 highlights and achievements

Posted by Nari Yoon, Bitnoori Keum, 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 2023. We are enthusiastic and grateful about all the activities by the global network of ML communities. Here are the highlights!

ML Training Campaigns Summary

More than 35 communities around the world have hosted ML Campaigns distributed by the ML Developer Programs team during the first half of the year. Thank you all for your training efforts for the entire ML community!


Community Highlights


Keras

Screengrab of Tensorflow & Deep Learning Malaysia June 2023 Webinar - 'KerasCV for the Young and Restless'

Image Segmentation using Composable Fully-Convolutional Networks by ML GDE Suvaditya Mukherjee (India) is a Kears.io example explaining how to implement a fully-convolutional network with a VGG-16 backend and how to use it for performing image segmentation. His presentation, KerasCV for the Young and Restless (slides | video) at TFUG Malaysia and TFUG Kolkata was an introduction to KerasCV. He discussed how basic computer vision components work, why Keras is an important tool, and how KerasCV builds on top of the established TFX and Keras ecosystem.

[ML Story] My Keras Chronicles by ML GDE Aritra Roy Gosthipaty (India) summarized his story of getting into deep learning with Keras. He included pointers as to how one could get into the open source community. Plus, his Kaggle notebook, [0.11] keras starter: unet + tf data pipeline is a starter guide for Vesuvius Challenge. He and Subvaditya also shared Keras implementation of Temporal Latent Bottleneck Networks, proposed in the paper.

KerasFuse by ML GDE Ayse Ayyuce Demirbas (Portugal) is a Python library that combines the power of TensorFlow and Keras with various computer vision techniques for medical image analysis tasks. It provides a collection of modules and functions to facilitate the development of deep learning models in TensorFlow & Keras for tasks such as image segmentation, classification, and more.

TensorFlow at Google I/O 23: A Preview of the New Features and Tools by TFUG Ibadan explored the preview of the latest features and tools in TensorFlow. They covered a wide range of topics including Dtensor, KerasCV & KerasNLP, TF quantization API, and JAX2TF.

StableDiffusion- Textual Inversion app

StableDiffusion - Textual-Inversion implementation app by ML GDE Dimitre Oliveira (Brazil) is an example of how to implement code from research and fine-tunes it using the Textual Inversion process. It also provides relevant use cases for valuable tools and frameworks such as HuggingFace, Gradio, TensorFlow serving, and KerasCV.

In Understanding Gradient Descent and Building an Image Classifier in TF From Scratch, ML GDE Tanmay Bakshi (Canada) talked about how to develop a solid intuition for the fundamentals backing ML tech, and actually built a real image classification system for dogs and cats, from scratch in TF.Keras.

TensorFlow and Keras Implementation of the CVPR 2023 paper by Usha Rengaraju (India) is a research paper implementation of BiFormer: Vision Transformer with Bi-Level Routing Attention.

Smile Detection with Python, OpenCV, and Deep Learning by Rouizi Yacine is a tutorial explaining how to use deep learning to build a more robust smile detector using TensorFlow, Keras, and OpenCV.


Kaggle

Screengrab of ML Olympiad for Students - TopVistos USA

ML Olympiad for Students by GDSC UNINTER was for students and aspiring ML practitioners who want to improve their ML skills. It consisted of a challenge of predicting US working visa applications. 320+ attendees registered for the opening event, 700+ views on YouTube, 66 teams competed, and the winner got a 71% F1-score.

ICR | EDA & Baseline by ML GDE Ertuğrul Demir (Turkey) is a starter notebook for newcomers interested in the latest featured code competition on Kaggle. It got 200+ Upvotes and 490+ forks.

Screengrab of Compete More Effectively on Kaggle using Weights and Biases showing participants in the video call

Compete More Effectively on Kaggle using Weights and Biases by TFUG Hajipur was a meetup to explore techniques using Weights and Biases to improve model performance in Kaggle competitions. Usha Rengaraju (India) joined as a speaker and delivered her insights on Kaggle and strategies to win competitions. She shared tips and tricks and demonstrated how to set up a W&B account and how to integrate with Google Colab and Kaggle.

Skeleton Based Action Recognition: A failed attempt by ML GDE Ayush Thakur (India) is a discussion post about documenting his learnings from competing in the Kaggle competition, Google - Isolated Sign Language Recognition. He shared his repository, training logs, and ideas he approached in the competition. Plus, his article Keras Dense Layer: How to Use It Correctly) explored what the dense layer in Keras is and how it works in practice.


On-device ML

Google for developers Edu Program Tech Talks for Educators Add Machine Learning to your Android App June 22, 2023 12:00pm - 01:00 pm goo.gle/techtalksforedu with headshot of Pankaj Rai GDE - Android, Firebase, Machine Learning

Add Machine Learning to your Android App by ML GDE Pankaj Rai (India) at Tech Talks for Educators was a session on on-device ML and how to add ML capabilities to Android apps such as object detection and gesture detection. He explained capabilities of ML Kit, MediaPipe, TF Lite and how to use these tools. 700+ people registered for his talk.

In MediaPipe with a bit of Bard at I/O Extended Singapore 2023, ML GDE Martin Andrews (Singapore) shared how MediaPipe fits into the ecosystem, and showed 4 different demonstrations of MediaPipe functionality: audio classification, facial landmarks, interactive segmentation, and text classification.

Adding ML to our apps with Google ML Kit and MediaPipe by ML GDE Juan Guillermo Gomez Torres (Bolivia) introduced ML Kit & MediaPipe, and the benefits of on-device ML. In Startup Academy México (Google for Startups), he shared how to increase the value for clients with ML and MediaPipe.


LLM

Introduction to Google's PaLM 2 API by ML GDE Hannes Hapke (United States) introduced how to use PaLM2 and summarized major advantages of it. His another article The role of ML Engineering in the time of GPT-4 & PaLM 2 explains the role of ML experts in finding the right balance and alignment among stakeholders to optimally navigate the opportunities and challenges posed by this emerging technology. He did presentations under the same title at North America Connect 2023 and the GDG Portland event.

Image of a cellphone with ChatBard on the display in front of a computer display with Firebase PaLM in Cloud Firestore

ChatBard : An Intelligent Customer Service Center App by ML GDE Ruqiya Bin Safi (Saudi Arabia) is an intelligent customer service center app powered by generative AI and LLMs using PaLM2 APIs.

Bard can now code and put that code in Colab for you by ML GDE Sam Witteveen (Singapore) showed how Bard makes code. He runs a Youtube channel exploring ML and AI, with playlists such as Generative AI, Paper Reviews, LLMs, and LangChain.

Google’s Bard Can Write Code by ML GDE Bhavesh Bhatt (India) shows the coding capabilities of Bard, how to create a 2048 game with it, and how to add some basic features to the game. He also uploaded videos about LangChain in a playlist and introduced Google Cloud’s new course on Generative AI in this video.

Screengrab of GDG Deep Learning Course Attention Mechanisms and Transformers led by Ruqiya Bin Safi ML GDE & WTM Ambassador, @Ru0Sa

Attention Mechanisms and Transformers by GDG Cloud Saudi talked about Attention and Transformer in NLP and ML GDE Ruqiya Bin Safi (Saudi Arabia) participated as a speaker. Another event, Hands-on with the PaLM2 API to create smart apps(Jeddah) explored what LLMs, PaLM2, and Bard are, how to use PaLM2 API, and how to create smart apps using PaLM2 API.

Hands-on with Generative AI: Google I/O Extended [Virtual] by ML GDE Henry Ruiz (United States) and Web GDE Rabimba Karanjai (United States) was a workshop on generative AI showing hands-on demons of how to get started using tools such as PaLM API, Hugging Face Transformers, and LangChain framework.

Generative AI with Google PaLM and MakerSuite by ML GDE Kuan Hoong (Malaysia) at Google I/O Extended George Town 2023 was a talk about LLMs with Google PaLM and MakerSuite. The event hosted by GDG George Town and also included ML topics such as LLMs, responsible AI, and MLOps.

Intor to Gen AI with PaLM API and MakerSuite led by GUS Luis Gustavo and Tensorflow User Group Sao Paolo

Intro to Gen AI with PaLM API and MakerSuite by TFUG São Paulo was for people who want to learn generative AI and how Google tools can help with adoption and value creation. They covered how to start prototyping Gen AI ideas with MakerSuite and how to access advanced features of PaLM2 and PaLM API. The group also hosted Opening Pandora's box: Understanding the paper that revolutionized the field of NLP (video) and ML GDE Pedro Gengo (Brazil) and ML GDE Vinicius Caridá (Brazil) shared the secret behind the famous LLM and other Gen AI models.The group members studied Attention Is All You Need paper together and learned the full potential that the technology can offer.

Language models which PaLM can speak, see, move, and understand by GDG Cloud Taipei was for those who want to understand the concept and application of PaLM. ML GED Jerry Wu (Taiwan) shared the PaLM’s main characteristics, functions, and etc.

Flow chart illustrating flexible serving structure of stable diffusion

Serving With TF and GKE: Stable Diffusion by ML GDE Chansung Park (Korea) and ML GDE Sayak Paul (India) discusses how TF Serving and Kubernetes Engine can serve a system with online deployment. They broke down Stable Diffusion into main components and how they influence the subsequent consideration for deployment. Then they also covered the deployment-specific bits such as TF Serving deployment and k8s cluster configuration.

TFX + W&B Integration by ML GDE Chansung Park (Korea) shows how KerasTuner can be used with W&B’s experiment tracking feature within the TFX Tuner component. He developed a custom TFX component to push a full-trained model to the W&B Artifact store and publish a working application on Hugging Face Space with the current version of the model. Also, his talk titled, ML Infra and High Level Framework in Google Cloud Platform, delivered what MLOps is, why it is hard, why cloud + TFX is a good starter, and how TFX is seamlessly integrated with Vertex AI and Dataflow. He shared use cases from the past projects that he and ML GDE Sayak Paul (India) have done in the last 2 years.

Open and Collaborative MLOps by ML GDE Sayak Paul (India) was a talk about why openness and collaboration are two important aspects of MLOps. He gave an overview of Hugging Face Hub and how it integrates well with TFX to promote openness and collaboration in MLOps workflows.


ML Research

Paper review: PaLM 2 Technical Report by ML GDE Grigory Sapunov (UK) looked into the details of PaLM2 and the paper. He shares reviews of papers related to Google and DeepMind through his social channels and here are some of them: Model evaluation for extreme risks (paper), Faster sorting algorithms discovered using deep reinforcement learning (paper), Power-seeking can be probable and predictive for trained agents (paper).

Learning JAX in 2023: Part 3 — A Step-by-Step Guide to Training Your First Machine Learning Model with JAX by ML GDE Aritra Roy Gosthipaty (India) and ML GDE Ritwik Raha (India) shows how JAX can train linear and nonlinear regression models and the usage of PyTrees library to train a multilayer perceptron model. In addition, at May 2023 Meetup hosted by TFUG Mumbai, they gave a talk titled Decoding End to End Object Detection with Transformers and covered the architecture of the mode and the various components that led to DETR’s inception.

20 steps to train a deployed version of the GPT model on TPU by ML GDE Jerry Wu (Taiwan) shared how to use JAX and TPU to train and infer Chinese question-answering data.

Photo of the audience from the back of the room at Developer Space @Google Singapore during Multimodal Transformers - Custom LLMs, ViTs & BLIPs

Multimodal Transformers - Custom LLMs, ViTs & BLIPs by TFUG Singapore looked at what models, systems, and techniques have come out recently related to multimodal tasks. ML GDE Sam Witteveen (Singapore) looked into various multimodal models and systems and how you can build your own with the PaLM2 Model. In June, this group invited Blaise Agüera y Arcas (VP and Fellow at Google Research) and shared the Cerebra project and the research going on at Google DeepMind including the current and future developments in generative AI and emerging trends.


TensorFlow

Training a recommendation model with dynamic embeddings by ML GDE Thushan Ganegedara (Australia) explains how to build a movie recommender model by leveraging TensorFlow Recommenders (TFRS) and TensorFlow Recommenders Addons (TFRA). The primary focus was to show how the dynamic embeddings provided in the TFRA library can be used to dynamically grow and shrink the size of the embedding tables in the recommendation setting.

Screengrab of a tweet by Mathis Hammel showcasing his talk, 'How I built the most efficient deepfake detector in the world for $100'

How I built the most efficient deepfake detector in the world for $100 by ML GDE Mathis Hammel (France) was a talk exploring a method to detect images generated via ThisPersonDoesNotExist.com and even a way to know the exact time the photo was produced. Plus, his Twitter thread, OSINT Investigation on LinkedIn, investigated a network of fake companies on LinkedIn. He used a homemade tool based on a TensorFlow model and hosted it on Google Cloud. Technical explanations of generative neural networks were also included. More than 701K people viewed this thread and it got 1200+ RTs and 3100+ Likes.

Screengrab of Few-shot learning: Creating a real-time object detection using TensorFlow and python by ML GDE Hugo Zanini

Few-shot learning: Creating a real-time object detection using TensorFlow and Python by ML GDE Hugo Zanini (Brazil) shows how to take pictures of an object using a webcam, label the images, and train a few-shot learning model to run in real-time. Also, his article, Custom YOLOv7 Object Detection with TensorFlow.js explains how he trained a custom YOLOv7 model to run it directly in the browser in real time and offline with TensorFlow.js.

The Lord of the Words Transformation of a Sequence Encoder/Decoder Attention

The Lord of the Words : The Return of the experiments with DVC (slides) by ML GDE Gema Parreno Piqueras (Spain) was a talk explaining Transformers in the neural machine learning scenario, and how to use Tensorflow and DVC. In the project, she used Tensorflow Datasets translation catalog to load data from various languages, and TensorFlow Transformers library to train several models.

Accelerate your TensorFlow models with XLA (slides) and Ship faster TensorFlow models with XLA by ML GDE Sayak Paul (India) shared how to accelerate TensorFlow models with XLA in Cloud Community Days Kolkata 2023 and Cloud Community Days Pune 2023.

Setup of NVIDIA Merlin and Tensorflow for Recommendation Models by ML GDE Rubens Zimbres (Brazil) presented a review of recommendation algorithms as well as the Two Towers algorithm, and setup of NVIDIA Merlin on premises and on Vertex AI.


Cloud

AutoML pipeline for tabular data on VertexAI in Go by ML GDE Paolo Galeone (Italy) delved into the development and deployment of tabular models using VertexAI and AutoML with Go, showcasing the actual Go code and sharing insights gained through trial & error and extensive Google research to overcome documentation limitations.

Search engine architecture

Beyond images: searching information in videos using AI (slides) by ML GDE Pedro Gengo (Brazil) and ML GDE Vinicius Caridá (Brazil) showed how to create a search engine where you can search for information in videos. They presented an architecture where they transcribe the audio and caption the frames, convert this text into embeddings, and save them in a vector DB to be able to search given a user query.

The secret sauce to creating amazing ML experiences for developers by ML GDE Gant Laborde (United States) was a podcast sharing his “aha” moment, 20 years of experience in ML, and the secret to creating enjoyable and meaningful experiences for developers.

What's inside Google’s Generative AI Studio? by ML GDE Gad Benram (Portugal) shared the preview of the new features and what you can expect from it. Additionally, in How to pitch Vertex AI in 2023, he shared the six simple and honest sales pitch points for Google Cloud representatives on how to convince customers that Vertex AI is the right platform.

In How to build a conversational AI Augmented Reality Experience with Sachin Kumar, ML GDE Sachin Kumar (Qatar) talked about how to build an AR app combining multiple technologies like Google Cloud AI, Unity, and etc. The session walked through the step-by-step process of building the app from scratch.

Machine Learning on Google Cloud Platform led by Nitin Tiwari, Google Developer Expert - Machine Learning, Software Engineer @LTMIMindtree

Machine Learning on Google Cloud Platform by ML GDE Nitin Tiwari (India) was a mentoring aiming to provide students with an in-depth understanding of the processes involved in training an ML model and deploying it using GCP. In Building robust ML solutions with TensorFlow and GCP, he shared how to leverage the capabilities of GCP and TensorFlow for ML solutions and deploy custom ML models.

Data to AI on Google cloud: Auto ML, Gen AI, and more by TFUG Prayagraj educated students on how to leverage Google Cloud’s advanced AI technologies, including AutoML and generative AI.

Machine Learning Communities: Q1 ‘23 highlights and achievements

Posted by Nari Yoon, Bitnoori Keum, 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 2023. We are enthusiastic and grateful about all the activities by the global network of ML communities. Here are the highlights!



ML Campaigns



ML Community Sprint

ML Community Sprint is a campaign, a collaborative attempt bridging ML GDEs with Googlers to produce relevant content for the broader ML community. Throughout Feb and Mar, MediaPipe/TF Recommendation Sprint was carried out and 5 projects were completed.


ML Olympiad 2023
I'm hosting a competiton ML Olympiad 2023 #MLOlympiad

ML Olympiad is an associated Kaggle Community Competitions hosted by ML GDE, TFUG, 3rd-party ML communities, supported by Google Developers. The second, ML Olympiad 2023 has wrapped up successfully with 17 competitions and 300+ participants addressing important issues of our time - diversity, environments, etc. Competition highlights include Breast Cancer Diagnosis, Water Quality Prediction, Detect ChatGpt answers, Ensure healthy lives, etc. Thank you all for participating in ML Olympiad 2023!

Also, “ML Paper Reading Clubs” (GalsenAI and TFUG Dhaka), “ML Math Clubs” (TFUG Hajipur and TFUG Dhaka) and “ML Study Jams” (TFUG Bauchi) were hosted by ML communities around the world.


Community Highlights



Keras
Screen shot of Fine-tuning Stable Diffusion using Keras

Various ways of serving Stable Diffusion by ML GDE Chansung Park (Korea) and ML GDE Sayak Paul (India) shares how to deploy Stable Diffusion with TF Serving, Hugging Face Endpoint, and FastAPI. Their other project Fine-tuning Stable Diffusion using Keras provides how to fine-tune the image encoder of Stable Diffusion on a custom dataset consisting of image-caption pairs.

Serving TensorFlow models with TFServing by ML GDE Dimitre Oliveira (Brazil) is a tutorial explaining how to create a simple MobileNet using the Keras API and how to serve it with TF Serving.

Fine-tuning the multilingual T5 model from Huggingface with Keras by ML GDE Radostin Cholakov (Bulgaria) shows a minimalistic approach for training text generation architectures from Hugging Face with TensorFlow and Keras as the backend.


Image showing a range of low-lit pictures enhanced incljuding inference time and ther metrics

Lighting up Images in the Deep Learning Era by ML GDE Soumik Rakshit (India), ML GDE Saurav Maheshkar (UK), ML GDE Aritra Roy Gosthipaty (India), and Samarendra Dash explores deep learning techniques for low-light image enhancement. The article also talks about a library, Restorers, providing TensorFlow and Keras implementations of SoTA image and video restoration models for tasks such as low-light enhancement, denoising, deblurring, super-resolution, etc.

How to Use Cosine Decay Learning Rate Scheduler in Keras? by ML GDE Ayush Thakur (India) introduces how to correctly use the cosine-decay learning rate scheduler using Keras API.


Screen shot of Implementation of DreamBooth using KerasCV and TensorFlow

Implementation of DreamBooth using KerasCV and TensorFlow (Keras.io tutorial) by ML GDE Sayak Paul (India) and ML GDE Chansung Park (Korea) demonstrates DreamBooth technique to fine-tune Stable Diffusion in KerasCV and TensorFlow. Training code, inference notebooks, a Keras.io tutorial, and more are in the repository. Sayak also shared his story, [ML Story] DreamBoothing Your Way into Greatness on the GDE blog.

Focal Modulation: A replacement for Self-Attention by ML GDE Aritra Roy Gosthipaty (India) shares a Keras implementation of the paper. Usha Rengaraju (India) shared Keras Implementation of NeurIPS 2021 paper, Augmented Shortcuts for Vision Transformers.

Images classification with TensorFlow & Keras (video) by TFUG Abidjan explained how to define an ML model that can classify images according to the category using a CNN.

Hands-on Workshop on KerasNLP by GDG NYC, GDG Hoboken, and Stevens Institute of Technology shared how to use pre-trained Transformers (including BERT) to classify text, fine-tune it on custom data, and build a Transformer from scratch.


On-device ML

Stable diffusion example in an android application — Part 1 & Part 2 by ML GDE George Soloupis (Greece) demonstrates how to deploy a Stable Diffusion pipeline inside an Android app.

AI for Art and Design by ML GDE Margaret Maynard-Reid (United States) delivered a brief overview of how AI can be used to assist and inspire artists & designers in their creative space. She also shared a few use cases of on-device ML for creating artistic Android apps.


ML Engineering (MLOps)
Overall system architecture of End-to-End Pipeline for Segmentation with TFX, Google Cloud, and Hugging Face

End-to-End Pipeline for Segmentation with TFX, Google Cloud, and Hugging Face by ML GDE Sayak Paul (India) and ML GDE Chansung Park (Korea) discussed the crucial details of building an end-to-end ML pipeline for Semantic Segmentation tasks with TFX and various Google Cloud services such as Dataflow, Vertex Pipelines, Vertex Training, and Vertex Endpoint. The pipeline uses a custom TFX component that is integrated with Hugging Face Hub - HFPusher.

Extend your TFX pipeline with TFX-Addons by ML GDE Hannes Hapke (United States) explains how you can use the TFX-Addons components or examples.



Textual Inversion Pipeline architecture

Textual Inversion Pipeline for Stable Diffusion by ML GDE Chansung Park (Korea) demonstrates how to manage multiple models and their prototype applications of fine-tuned Stable Diffusion on new concepts by Textual Inversion.

Running a Stable Diffusion Cluster on GCP with tensorflow-serving (Part 1 | Part 2) by ML GDE Thushan Ganegedara (Australia) explains how to set up a GKE cluster, how to use Terraform to set up and manage infrastructure on GCP, and how to deploy a model on GKE using TF Serving.


Photo of Googler Joinal Ahmed giving a talk at TFUG Bangalore

Scalability of ML Applications by TFUG Bangalore focused on the challenges and solutions related to building and deploying ML applications at scale. Googler Joinal Ahmed gave a talk entitled Scaling Large Language Model training and deployments.

Discovering and Building Applications with Stable Diffusion by TFUG São Paulo was for people who are interested in Stable Diffusion. They shared how Stable Diffusion works and showed a complete version created using Google Colab and Vertex AI in production.


Responsible AI
Thumbnail image for Between the Brackets Fairness & Ethics in AI: Perspectives from Journalism, Medicine and Translation

In Fairness & Ethics In AI: From Journalism, Medicine and Translation, ML GDE Samuel Marks (United States) discussed responsible AI.

In The new age of AI: A Convo with Google Brain, ML GDE Vikram Tiwari (United States) discussed responsible AI, open-source vs. closed-source, and the future of LLMs.

Responsible IA Toolkit (video) by ML GDE Lesly Zerna (Bolivia) and Google DSC UNI was a meetup to discuss ethical and sustainable approaches to AI development. Lesly shared about the “ethic” side of building AI products as well as learning about “Responsible AI from Google”, PAIR guidebook, and other experiences to build AI.

Women in AI/ML at Google NYC by GDG NYC discussed hot topics, including LLMs and generative AI. Googler Priya Chakraborty gave a talk entitled Privacy Protections for ML Models.


ML Research

Efficient Task-Oriented Dialogue Systems with Response Selection as an Auxiliary Task by ML GDE Radostin Cholakov (Bulgaria) showcases how, in a task-oriented setting, the T5-small language model can perform on par with existing systems relying on T5-base or even bigger models.

Learning JAX in 2023: Part 1 / Part 2 / Livestream video by ML GDE Aritra Roy Gosthipaty (India) and ML GDE Ritwik Raha (India) covered the power tools of JAX, namely grad, jit, vmap, pmap, and also discussed the nitty-gritty of randomness in JAX.


Screen grab from JAX Streams: Parallelism with Flax | Ep4 with David Cardozo and Cristian Garcia

In Deep Learning Mentoring MILA Quebec, ML GDE David Cardozo (Canada) did mentoring for M.Sc and Ph.D. students who have interests in JAX and MLOps. JAX Streams: Parallelism with Flax | EP4 by David and ML GDE Cristian Garcia (Columbia) explored Flax’s new APIs to support parallelism.

March Machine Learning Meetup hosted by TFUG Kolkata. Two sessions were delivered: 1) You don't know TensorFlow by ML GDE Sayak Paul (India) presented some under-appreciated and under-used features of TensorFlow. 2) A Guide to ML Workflows with JAX by ML GDE Aritra Roy Gosthipaty (India), ML GDE Soumik Rakshit (India), and ML GDE Ritwik Raha (India) delivered on how one could think of using JAX functional transformations for their ML workflows.

A paper review of PaLM-E: An Embodied Multimodal Language Model by ML GDE Grigory Sapunov (UK) explained the details of the model. He also shared his slide deck about NLP in 2022.

An annotated paper of On the importance of noise scheduling in Diffusion Models by ML GDE Aakash Nain (India) outlined the effects of noise schedule on the performance of diffusion models and strategies to get a better schedule for optimal performance.


TensorFlow

Three projects were awarded as TF Community Spotlight winners: 1) Semantic Segmentation model within ML pipeline by ML GDE Chansung Park (Korea), ML GDE Sayak Paul (India), and ML GDE Merve Noyan (France), 2) GatedTabTransformer in TensorFlow + TPU / in Flax by Usha Rengaraju, and 3) Real-time Object Detection in the browser with YOLOv7 and TF.JS by ML GDE Hugo Zanini (Brazil).

Building ranking models powered by multi-task learning with Merlin and TensorFlow by ML GDE Gabriel Moreira (Brazil) describes how to build TensorFlow models with Merlin for recommender systems using multi-task learning.


Transform your Web Apps with Machine Learning: Unleashing the Power of Open-Source Python Libraries like TensorFlow Hub & Gradio Bhjavesh Bhatt @_bhaveshbhatt

Building ML Powered Web Applications using TensorFlow Hub & Gradio (slide) by ML GDE Bhavesh Bhatt (India) demonstrated how to use TF Hub & Gradio to create a fully functional ML-powered web application. The presentation was held as part of an event called AI Evolution with TensorFlow, covering the fundamentals of ML & TF, hosted by TFUG Nashik.

create-tf-app (repository) by ML GDE Radostin Cholakov (Bulgaria) shows how to set up and maintain an ML project in Tensorflow with a single script.


Cloud

Creating scalable ML solutions to support big techs evolution (slide) by ML GDE Mikaeri Ohana (Brazil) shared how Google can help big techs to generate impact through ML with scalable solutions.

Search of Brazilian Laws using Dialogflow CX and Matching Engine by ML GDE Rubens Zimbres (Brazil) shows how to build a chatbot with Dialogflow CX and query a database of Brazilian laws by calling an endpoint in Cloud Run.


4x4 grid of sample results from Vintedois Diffusion model

Stable Diffusion Finetuning by ML GDE Pedro Gengo (Brazil) and ML GDE Piero Esposito (Brazil) is a fine-tuned Stable Diffusion 1.5 with more aesthetic images. They used Vertex AI with multiple GPUs to fine-tune it. It reached Hugging Face top 3 and more than 150K people downloaded and tested it.

Developer Journey – Women’s History Month: March 2023

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.


Headshot of Ezinne Osuamadi smiling

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.

 

Headshot of Patty O’Callaghan smiling

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!



Headshot of Alexis and David Snelling smiling

Alexis & David Snelling

Alexis – Women Techmakers Ambassador & Lead
Named as Top 10 Women founders to Watch in 2023 by Forbes Group
San Francisco, CA
CEO WeTransact.live
Twitter
Linkedln
Facebook 

David – Google Developer Groups
San Francisco, CA
CTO WeTransact.live
Twitter
Linkedln
Facebook

What Google tools have you used to build?

Here’s just a few of the tools we’ve used:
  • Angular 15
  • Material Design
  • Google Cloud / Firebase
    • Authentication
    • Hosting
    • Firestore
    • Functions
    • Extensions
    • Storage
    • Machine Learning
  • PWA Standards
  • Chrome / DevTools
  • Android

Which tool has been your favorite to use? Why?

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.

Machine Learning Communities: Q4 ‘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 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 group of ML Developers attending 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

A group of ML Developers attending ML Community Summit

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.

Diagram of gMLP block

gMLP: What it is and how to use it in practice with Tensorflow and Keras? by ML GDE Radostin Cholakov (Bulgaria) demonstrates the state-of-the-art results on NLP and computer vision tasks using a lot less trainable parameters than corresponding Transformer models. He also wrote Differentiable discrete sampling 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.

tf.keras.metrics / tf.keras.optimizers by TFUG Taipei helped people learn the TF libraries. They shared basic concepts and how to use them using Colab.

Screen shot of TensorFlow Lite on Android Project Practical Course
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.

Keras

Diagram of feature maps concatenated together and flattened

Complete Guide on Deep Learning Architectures, Chapter 1 on ConvNets by ML GDE Merve Noyan (France) brings you into the theory of ConvNets and shows how it works with Keras.

Hazy Image Restoration Using Keras by ML GDE Soumik Rakshit (India) provides an introduction to building an image restoration model using TensorFlow, Keras, and Weights & Biases. He also shared an article Improving Generative Images with Instructions: Prompt-to-Prompt Image Editing with Cross Attention Control.

Mixed precision in Keras based Stable Diffusion
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.

A Deep Dive into Transformers with TensorFlow and Keras: Part 1, Part 2, Part3 by ML GDE Aritra Roy Gosthipaty (India) covered the journey from the intuition of attention to formulating the multi-head self-attention. And TensorFlow port of GroupViT in 🤗 transformers library was his contribution to Hugging Face transformers library.

TFX

Digits + TFX banner

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.

Usha Rengaraju (India) shared TensorFlow Extended (TFX) Tutorials (Part 1, Part 2, Part 3) and the following TF projects: TensorFlow Decision Forests Tutorial and FT Transformer TensorFlow Implementation.

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.

JAX/Flax

JAX High-performance ML Research by TFUG Taipei and ML GDE Jerry Wu (Taiwan) introduced JAX and how to start using JAX to solve machine learning problems.

[TensorFlow + TPU] GatedTabTransformer[W&B] and its JAX/Flax counterpart GatedTabTransformer-FLAX[W&B] by Usha Rengaraju (India) are tutorial series containing the implementation of GatedTabTransformer paper in both TensorFlow (TPU) and FLAX.

Putting NeRF on a diet: Semantically consistent Few-Shot View Synthesis Implementation
JAX implementation of Diet NeRf by ML GDE Wan Hong Lau (Singapore) implemented the paper “Putting NeRF on a Diet (DietNeRF)” in JAX/Flax. And he also implemented a JAX-and-Flax training pipeline with the ResNet model in his Kaggle notebook, 🐳HappyWhale🔥Flax/JAX⚡TPU&GPU - ResNet Baseline.

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.

Screen grab of ML GDE David Cardozo and Cristian Garcia during a live coding session of a review of new features, specifically Shared Arrays, in the recent release of JAX
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.

[LiveCoding] Train ResNet/MNIST with JAX/Flax by ML GDE Qinghua Duan (China) demonstrated how to train ResNet using JAX by writing code online.

Kaggle

Low-light Image Enhancement using MirNetv2 by ML GDE Soumik Rakshit (India) demonstrated the task of Low-light Image Enhancement.

Heart disease Prediction and Diabetes Prediction Competition hosted by TFUG Chandigarh were to familiarize participants with ML problems and find solutions using classification techniques.

TensorFlow User Group Bangalore Sentiment Analysis Kaggle Competition 1
TFUG Bangalore Kaggle Competition - Sentiment Analysis hosted by TFUG Bangalore was to find the best sentiment analysis algorithm. Participants were given a set of training data and asked to submit an ML/DL algorithm that could predict the sentiment of a text. The group also hosted Kaggle Challenge Finale + Vertex AI Session to support the participants and guide them in learning how to use Vertex AI in a workflow.

Cloud AI

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.

Jayesh Sharma, Platform Engineer, Zen ML; MLOps workshop with TensorFlow and Vertex AI November 12, 2022|TensorFlow User Group Chennai
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.

10 Predictions on the Future of Cloud Computing by 2025: Insights from Google Next Conference by ML GDE Victor Dibia (United States) includes a recap of his notes reflecting on the top 10 cloud technology predictions discussed at the Google Cloud Next 2022 keynote.
Workflow of Google Virtual Career Center
O uso do Vertex AI Matching Engine no Virtual Career Center (VCC) do Google Cloud by ML GDE Rubens Zimbres (Brazil) approaches the use of Vertex AI Matching Engine as part of the Google Cloud Virtual Career Center solution.

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.

A Natural Language Understanding Model LaMDA for Dialogue Applications by ML GDE Jerry Wu (Taiwan) introduced the natural language understanding (NLU) concept and shared the operation mode of LaMDA, model fine-tuning, and measurement indicators.

Python library for Arabic NLP preprocessing (Ruqia) by ML GDE Ruqiya Bin (Saudi Arabia) is her first python library to serve Arabic NLP.

Screengrab of ML GDEs Margaret Maynard-Reid and Akash Nain during Chat with ML GDE Akash
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.

Screen grab of statement of objectives in MLOps Development Environment by ML GDE Vinicius Carida
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.

Transcending Scaling Laws with 0.1% Extra Compute by ML GDE Grigory Sapunov (UK) reviewed a recent Google article on UL2R. And his posting Discovering faster matrix multiplication algorithms with reinforcement learning explained how AlphaTensor works and why it is important.

Back in Person - Prompting, Instructions and the Future of Large Language Models by TFUG Singapore and ML GDE Sam Witteveen (Singapore) and Martin Andrews (Singapore). This event covered recent advances in the field of large language models (LLMs).

ML for Production: The art of MLOps in TensorFlow Ecosystem with GDG Casablanca by TFUG Agadir discussed the motivation behind using MLOps and how it can help organizations automate a lot of pain points in the ML production process. It also covered the tools used in the TensorFlow ecosystem.