HomeSoftware DevelopmentThis autumn ‘22 highlights and achievements

This autumn ‘22 highlights and achievements

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

Let’s discover highlights and accomplishments of huge Google Machine Studying communities over the past 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 overlaying varied ML subjects with Google merchandise. Machine Studying with Jax: Zero to Hero (DevFest Conakry) by ML GDE Yannick Serge Obam Akou (Cameroon) and Straightforward ML on Google Cloud (DevFest Med) by ML GDE Nathaly Alarcon Torrico (Bolivia) hosted nice classes.

ML Group Summit 2022

A group of ML Developers attending ML Community Summit
ML Group 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 group 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) exhibits his implementation of the MAXIM household of fashions in TensorFlow.
Diagram of gMLP block

gMLP: What it’s and methods 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 pc imaginative and prescient duties utilizing lots 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 wish 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 facets for end-to-end coaching and deployment of object mannequin detection.

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

TensorFlow Lite and MediaPipe Utility by ML GDE XuHua Hu (China) explains methods 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 methods to remedy issues that we could meet normally.


Diagram of feature maps concatenated together and flattened
Mixed precision in Keras based Stable Diffusion
Let’s Generate Pictures with Keras based mostly Secure Diffusion by ML GDE Chansung Park (Korea) delivered methods to generate photographs with given textual content and what steady diffusion is. He additionally talked about Keras-based steady diffusion, primary 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 initiatives: TensorFlow Choice 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 important; Introduction to KerasTuner, primary utilization; methods 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 methods to begin utilizing JAX to resolve 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 now have on a DL framework to what JAX has to supply. Additional, he centered on the highly effective function-oriented view JAX provides and the way Flax lets you 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 assessment of recent options (particularly Shared Arrays) within the latest launch of JAX and demonstrated stay 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 will get higher provision {hardware} with code utilizing Vertex AI Workbench cases 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 newcomers and intermediate-level practitioners to provide hands-on expertise on the E2E MLOps pipeline with GCP. Within the workshop, they shared the varied levels of an ML pipeline, the highest instruments to construct an answer, and methods 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 dealing with AI in its adoption into the well being system.

Girls in AI APAC completed their journey at ML Paper Studying Membership. Throughout 10 weeks, members gained data on excellent machine studying analysis, realized the newest 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 Initiatives 🫀by ML GDE Sayak Paul (India) mentioned engaged on capstone ML initiatives that can 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 subject engaged on a number of facets.

Screen grab of  statement of objectives in MLOps Development Environment by ML GDE Vinicius Carida
MLOps Improvement Atmosphere by ML GDE Vinicius Caridá (Brazil) goals to construct a full growth setting the place you’ll be able to write your personal pipelines connecting MLFLow, Airflow, GCP and Streamlit, and construct wonderful MLOps pipelines to apply your abilities.

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

Again in Particular person – Prompting, Directions and the Way forward for Massive Language Fashions by TFUG Singapore and ML GDE Sam Witteveen (Singapore) and Martin Andrews (Singapore). This occasion coated latest advances within the subject 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 might help organizations automate a variety of ache factors within the ML manufacturing course of. It additionally coated the instruments used within the TensorFlow ecosystem.


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