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This fall ‘22 highlights and achievements

Posted by Nari Yoon, Hee Jung, DevRel Neighborhood Supervisor / Soonson Kwon, DevRel Program Supervisor

Let’s discover highlights and accomplishments of huge Google Machine Studying communities during the last quarter of 2022. We’re enthusiastic and grateful about all of the actions by the worldwide community of ML communities. Listed below are the highlights!

ML at DevFest 2022

A group of ML Developers attending DevFest 2022
Numerous members of ML GDE, TFUG, and 3P ML communities participated in DevFests 2022 worldwide protecting varied ML subjects with Google merchandise. Machine Studying with Jax: Zero to Hero (DevFest Conakry) by ML GDE Yannick Serge Obam Akou (Cameroon) and Simple ML on Google Cloud (DevFest Med) by ML GDE Nathaly Alarcon Torrico (Bolivia) hosted nice periods.

ML Neighborhood Summit 2022

A group of ML Developers attending ML Community Summit
ML Neighborhood Summit 2022 was hosted on Oct 22-23, 2022, in Bangkok, Thailand. Twenty-five most lively group members (ML GDE or TFUG organizer) had been invited and shared their previous actions and ideas on Google’s ML merchandise. A video sketch from ML Developer Packages crew and a weblog posting by ML GDE Margaret Maynard-Reid (United States) assist us revisit the moments.


MAXIM in TensorFlow by ML GDE Sayak Paul (India) reveals his implementation of the MAXIM household of fashions in TensorFlow.
Diagram of gMLP block

gMLP: What it’s and find out how to use it in apply with Tensorflow and Keras? by ML GDE Radostin Cholakov (Bulgaria) demonstrates the state-of-the-art outcomes on NLP and laptop imaginative and prescient duties utilizing rather a lot much less trainable parameters than corresponding Transformer fashions. He additionally wrote Differentiable discrete sampling in TensorFlow.

Constructing Laptop Imaginative and prescient Mannequin utilizing TensorFlow: Half 2 by TFUG Pune for the builders who need to deep dive into coaching an object detection mannequin on Google Colab, inspecting the TF Lite mannequin, and deploying the mannequin on an Android utility. ML GDE Nitin Tiwari (India) coated detailed points for end-to-end coaching and deployment of object mannequin detection.

Introduction of Code 2022 in pure TensorFlow (days 1-5) by ML GDE Paolo Galeone (Italy) fixing the Introduction of Code (AoC) puzzles utilizing solely TensorFlow. The articles comprise an outline of the options of the Introduction of Code puzzles 1-5, in pure TensorFlow.

Screen shot of TensorFlow Lite on Android Project Practical Course

Construct tensorflow-lite-select-tf-ops.aar and tensorflow-lite.aar recordsdata with Colab by ML GDE George Soloupis (Greece) guides how one can shrink the ultimate dimension of your Android utility’s .apk by constructing tensorflow-lite-select-tf-ops.aar and tensorflow-lite.aar recordsdata with out the necessity of Docker or private PC surroundings.

TensorFlow Lite and MediaPipe Utility by ML GDE XuHua Hu (China) explains find out how to use TFLite to deploy an ML mannequin into an utility on gadgets. He shared experiences with creating a movement sensing recreation with MediaPipe, and find out how to remedy issues that we might meet often.


Diagram of feature maps concatenated together and flattened
Mixed precision in Keras based Stable Diffusion
Let’s Generate Pictures with Keras based mostly Steady Diffusion by ML GDE Chansung Park (Korea) delivered find out how to generate photos with given textual content and what steady diffusion is. He additionally talked about Keras-based steady diffusion, fundamental constructing blocks, and the benefits of utilizing Keras-based steady diffusion.


Digits + TFX banner
How startups can profit from TFX by ML GDE Hannes Hapke (United States) explains how the San Francisco-based FinTech startup Digits has benefitted from making use of TFX early, how TFX helps Digits develop, and the way different startups can profit from TFX too.

Usha Rengaraju (India) shared TensorFlow Prolonged (TFX) Tutorials (Half 1, Half 2, Half 3) and the next TF tasks: TensorFlow Determination Forests Tutorial and FT Transformer TensorFlow Implementation.

Hyperparameter Tuning and ML Pipeline by ML GDE Chansung Park (Korea) defined hyperparam tuning, why it will be significant; Introduction to KerasTuner, fundamental utilization; find out how to visualize hyperparam tuning outcomes with TensorBoard; and integration inside ML pipeline with TFX.


JAX Excessive-performance ML Analysis by TFUG Taipei and ML GDE Jerry Wu (Taiwan) launched JAX and find out how to begin utilizing JAX to unravel machine studying issues.

Putting NeRF on a diet: Semantically consistent Few-Shot View Synthesis Implementation
Introduction to JAX with Flax (slides) by ML GDE Phillip Lippe (Netherlands) reviewed from the fundamentals of the necessities we have now on a DL framework to what JAX has to supply. Additional, he centered on the highly effective function-oriented view JAX presents and the way Flax means that you can use them in coaching 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) confirmed a evaluate of recent options (particularly Shared Arrays) within the current launch of JAX and demonstrated reside coding.


Low-light Picture Enhancement utilizing MirNetv2 by ML GDE Soumik Rakshit (India) demonstrated the duty of Low-light Picture Enhancement.

TensorFlow User Group Bangalore Sentiment Analysis Kaggle Competition 1

Cloud AI

Higher {Hardware} Provisioning for ML Experiments on GCP by ML GDE Sayak Paul (India) mentioned the ache factors of provisioning {hardware} (particularly for ML experiments) and the way we are able to get higher provision {hardware} with code utilizing Vertex AI Workbench situations 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 focused learners and intermediate-level practitioners to provide hands-on expertise on the E2E MLOps pipeline with GCP. Within the workshop, they shared the varied phases of an ML pipeline, the highest instruments to construct an answer, and find out how to design a workflow utilizing an open-source framework like ZenML.
Workflow of Google Virtual Career Center

Extra sensible time-series mannequin with BQML by ML GDE JeongMin Kwon (Korea) launched BQML and time-series modeling and confirmed some sensible purposes with BQML ARIMA+ and Python implementations.

Analysis & Ecosystem

AI in Healthcare by ML GDE Sara EL-ATEIF (Morocco) launched AI purposes in healthcare and the challenges going through AI in its adoption into the well being system.

Ladies in AI APAC completed their journey at ML Paper Studying Membership. Throughout 10 weeks, members gained data on excellent machine studying analysis, realized the most recent methods, and understood the notion of “ML analysis” amongst ML engineers. See their session right here.

A Pure Language Understanding Mannequin LaMDA for Dialogue Functions by ML GDE Jerry Wu (Taiwan) launched the pure language understanding (NLU) idea and shared the operation mode of LaMDA, mannequin 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

Anatomy of Capstone ML Tasks 🫀by ML GDE Sayak Paul (India) mentioned engaged on capstone ML tasks that may stick with you all through your profession. He coated varied subjects starting from drawback choice to tightening up the technical gotchas to presentation. And in Enhancing as an ML Practitioner he shared his studying from expertise within the area engaged on a number of points.

Screen grab of  statement of objectives in MLOps Development Environment by ML GDE Vinicius Carida
MLOps Improvement Setting by ML GDE Vinicius Caridá (Brazil) goals to construct a full growth surroundings the place you may write your individual pipelines connecting MLFLow, Airflow, GCP and Streamlit, and construct superb MLOps pipelines to apply your expertise.

Transcending Scaling Legal guidelines with 0.1% Further Compute by ML GDE Grigory Sapunov (UK) reviewed a current Google article on UL2R. And his posting Discovering quicker matrix multiplication algorithms with reinforcement studying defined how AlphaTensor works and why it will be significant.

Again in Particular person – Prompting, Directions and the Way forward for Giant Language Fashions by TFUG Singapore and ML GDE Sam Witteveen (Singapore) and Martin Andrews (Singapore). This occasion coated current advances within the area of huge language fashions (LLMs).

ML for Manufacturing: The artwork of MLOps in TensorFlow Ecosystem with GDG Casablanca by TFUG Agadir mentioned the motivation behind utilizing MLOps and the way it may help organizations automate a number of ache factors within the ML manufacturing course of. It additionally coated the instruments used within the TensorFlow ecosystem.



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