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Machine Learning Communities: Q2 ‘22 highlights and achievements

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

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

TensorFlow/Keras

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

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

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

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

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

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

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

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

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

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

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

JAX/Flax

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

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

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

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

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

Kaggle

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

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















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













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

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

TFX

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

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

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

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

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

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

Cloud AI

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

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

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

Research

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

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

Introducing Earth Engine for governments and businesses

We’re at a unique inflection point in our relationship with the planet. We face existential climate threats — a growing crisis already manifesting in extreme weather events, coupled with the loss of nature resulting from human activities such as deforestation. But at the same time, the world is mobilizing around climate action. Citizens are demanding progress, and governments and companies are making unprecedented commitments to transform how we live on this planet — from policy decisions to business practices. Over the years, one of the top climate challenges I’ve heard from businesses, governments and organizations is that they’re drowning in data but thirsty for insights.

So starting today, we’re making Google Earth Engine available to businesses and governments worldwide as an enterprise-grade service through Google Cloud. With access to reliable, up-to-date insights on how our planet is changing, organizations will be better equipped to move their sustainability efforts forward.

Google Earth Engine, which originally launched to scientists and NGOs in 2010, is a leading technology for planetary-scale environmental monitoring. Google Earth Engine combines data from hundreds of satellites and earth observation datasets with powerful cloud computing to show timely, accurate, high-resolution insights about the state of the world’s habitats and ecosystems — and how they’re changing over time. With one of the largest publicly available data catalogs and a global data archive that goes back 50 years and updates every 15 minutes, it’s possible to detect trends and understand correlations between human activities and environmental impact. This technology is already beginning to bring greater transparency and traceability to commodity supply chains, supporting climate resilience and allowing for more sustainable management of natural resources such as forests and water.

Earth Engine will be available at no charge to government researchers, least-developed countries, tribal nations and news organizations. And it will remain available at no cost for nonprofit organizations, research scientists, and other impact users for their non-commercial and research projects.

Earth Engine will also be available to startups that are a part of the Google for Startups Cloud Program. Through this initiative we provide funded startups with access to dedicated mentors, industry experts, product and technical support, and Cloud cost coverage (up to $100,000) for each of the first two years and more.

How organizations are using Earth Engine

Since we announced the preview of Earth Engine in Google Cloud last October, we’ve been working with dozens of companies and organizations across industries — from consumer packaged goods and insurance companies to agriculture technology and the public sector — to use Earth Engine’s satellite imagery and geospatial data in incredible ways.

Land cover change over time from Dynamic World

Dynamic World, a global machine learning derived land classification over time available in Earth Engine's public data catalog, was developed in partnership with World Resources Institute (WRI).

For example, Regrow, a company that helps large consumer packaged goods corporations decarbonize their agricultural practices, started using Earth Engine to report and verify regenerative and sustainable techniques. Through Earth Engine’s analysis of historical and satellite imagery, Regrow can generate granular field data at the state or country levels across millions of acres of farmland around the world.

  • Quote from Juan Delard de Rigoulieres Mantelli, CTO of Regrow: “Regrow aims to make regenerative agriculture ubiquitous across the globe with an overall mission to mitigate climate change. Earth Engine has allowed us to scale our technology and increase confidence in our data and reports.”
  • Quote from Dr. Erin McCreless, Conservation Scientist at Rainforest Trust: “NGIS TraceMark enables near real-time fire and deforestation alerts to be sent directly to the inbox of Rainforest Trust staff, who immediately engage partners on the ground to investigate and intervene.”
  • Quote from Maude Meler, SC Johnson entomologist: “We are putting the power in consumers’ hands in providing them with a tool to help predict their exposure and prevent mosquito bites. It’s an exciting time to be working in the field of insect science as we find new opportunities to combine science and technology, like Google Earth Engine, to be a force for good in our mission to prevent the spread of insect-borne diseases.”
  • Animation of a globe with three gears turning.

As climate change causes shifts in biodiversity, Earth Engine is helping communities adapt to the effects of these changes, such as new mosquito outbreaks. SC Johnson partnered with Google Cloud to use Earth Engine to develop a publicly accessible, predictive model of when and where mosquito populations are emerging nationwide. The forecast accounts for billions of individual weather data points and over 60 years of mosquito knowledge in forecasting models.

Animated gif showing the Off!Cast, SC Johnson’s mosquito forecasting tool. A zip code is entered into the tool to show a 7-day forecast that indicates medium, high and very-high.

For organizations that may not have resources dedicated to working with Earth Engine, we’ve continued to grow our partner network to support them. For example, our partner NGIS worked with Rainforest Trust to get action-oriented and tailored insights that can help them conserve 39 million acres of tropical forests around the world.

Itauba, Brazil | Google Earth Timelapse
10:25

It’s not too late to protect and restore a livable planet for ourselves and generations to come. Climate change experts have declared the next ten years the ‘Decade of Action’, a critical time to act in order to curb the effects of climate change. Making a global difference will require a transformational change from everyone, including businesses and governments. With Google Earth Engine, we hope to help organizations contribute to this change.

Introducing Earth Engine for governments and businesses

We’re at a unique inflection point in our relationship with the planet. We face existential climate threats — a growing crisis already manifesting in extreme weather events, coupled with the loss of nature resulting from human activities such as deforestation. But at the same time, the world is mobilizing around climate action. Citizens are demanding progress, and governments and companies are making unprecedented commitments to transform how we live on this planet — from policy decisions to business practices. Over the years, one of the top climate challenges I’ve heard from businesses, governments and organizations is that they’re drowning in data but thirsty for insights.

So starting today, we’re making Google Earth Engine available to businesses and governments worldwide as an enterprise-grade service through Google Cloud. With access to reliable, up-to-date insights on how our planet is changing, organizations will be better equipped to move their sustainability efforts forward.

Google Earth Engine, which originally launched to scientists and NGOs in 2010, is a leading technology for planetary-scale environmental monitoring. Google Earth Engine combines data from hundreds of satellites and earth observation datasets with powerful cloud computing to show timely, accurate, high-resolution insights about the state of the world’s habitats and ecosystems — and how they’re changing over time. With one of the largest publicly available data catalogs and a global data archive that goes back 50 years and updates every 15 minutes, it’s possible to detect trends and understand correlations between human activities and environmental impact. This technology is already beginning to bring greater transparency and traceability to commodity supply chains, supporting climate resilience and allowing for more sustainable management of natural resources such as forests and water.

Earth Engine will be available at no charge to government researchers, least-developed countries, tribal nations and news organizations. And it will remain available at no cost for nonprofit organizations, research scientists, and other impact users for their non-commercial and research projects.

Earth Engine will also be available to startups that are a part of the Google for Startups Cloud Program. Through this initiative we provide funded startups with access to dedicated mentors, industry experts, product and technical support, and Cloud cost coverage (up to $100,000) for each of the first two years and more.

How organizations are using Earth Engine

Since we announced the preview of Earth Engine in Google Cloud last October, we’ve been working with dozens of companies and organizations across industries — from consumer packaged goods and insurance companies to agriculture technology and the public sector — to use Earth Engine’s satellite imagery and geospatial data in incredible ways.

Land cover change over time from Dynamic World

Dynamic World, a global machine learning derived land classification over time available in Earth Engine's public data catalog, was developed in partnership with World Resources Institute (WRI).

For example, Regrow, a company that helps large consumer packaged goods corporations decarbonize their agricultural practices, started using Earth Engine to report and verify regenerative and sustainable techniques. Through Earth Engine’s analysis of historical and satellite imagery, Regrow can generate granular field data at the state or country levels across millions of acres of farmland around the world.

  • Quote from Juan Delard de Rigoulieres Mantelli, CTO of Regrow: “Regrow aims to make regenerative agriculture ubiquitous across the globe with an overall mission to mitigate climate change. Earth Engine has allowed us to scale our technology and increase confidence in our data and reports.”
  • Quote from Dr. Erin McCreless, Conservation Scientist at Rainforest Trust: “NGIS TraceMark enables near real-time fire and deforestation alerts to be sent directly to the inbox of Rainforest Trust staff, who immediately engage partners on the ground to investigate and intervene.”
  • Quote from Maude Meler, SC Johnson entomologist: “We are putting the power in consumers’ hands in providing them with a tool to help predict their exposure and prevent mosquito bites. It’s an exciting time to be working in the field of insect science as we find new opportunities to combine science and technology, like Google Earth Engine, to be a force for good in our mission to prevent the spread of insect-borne diseases.”
  • Animation of a globe with three gears turning.

As climate change causes shifts in biodiversity, Earth Engine is helping communities adapt to the effects of these changes, such as new mosquito outbreaks. SC Johnson partnered with Google Cloud to use Earth Engine to develop a publicly accessible, predictive model of when and where mosquito populations are emerging nationwide. The forecast accounts for billions of individual weather data points and over 60 years of mosquito knowledge in forecasting models.

Animated gif showing the Off!Cast, SC Johnson’s mosquito forecasting tool. A zip code is entered into the tool to show a 7-day forecast that indicates medium, high and very-high.

For organizations that may not have resources dedicated to working with Earth Engine, we’ve continued to grow our partner network to support them. For example, our partner NGIS worked with Rainforest Trust to get action-oriented and tailored insights that can help them conserve 39 million acres of tropical forests around the world.

Itauba, Brazil | Google Earth Timelapse
10:25

It’s not too late to protect and restore a livable planet for ourselves and generations to come. Climate change experts have declared the next ten years the ‘Decade of Action’, a critical time to act in order to curb the effects of climate change. Making a global difference will require a transformational change from everyone, including businesses and governments. With Google Earth Engine, we hope to help organizations contribute to this change.

A bigger piece of the pi: Finding the 100-trillionth digit

The 100-trillionth decimal place of π (pi) is 0. A few months ago, on an average Tuesday morning in March, I sat down with my coffee to check on the program that had been running a calculation from my home office for 157 days. It was finally time — I was going to be the first and only person to ever see the number. The results were in and it was a new record: We’d calculated the most digits of π ever — 100 trillion to be exact.

Calculating π — or finding as many digits of it as possible — is a project that mathematicians, scientists and engineers around the world have worked on for thousands of years, myself included. The well-known approximation 3.14 is believed to have been found by Archimedes around the year 250 BCE. Computer scientist Donald Knuth wrote "human progress in calculation has traditionally been measured by the number of decimal digits of π" in his book “The Art of Computer Programming” (Dr. Knuth even wrote about me in the book). In the past, people would manually — meaning without calculators or computers — determine the digits of pi. Today, we use computers to do this calculation, which helps us learn how much faster they’ve become. It’s one of the few ways to measure how much progress we're making across centuries, including before the invention of electronic computers.

An illustration of pie crust stretching from the Earth to the moon. Above it reads: "100 trillion inches of pie crust stretches from Earth to the moon an back ~3,304 times."

As a developer advocate at Google Cloud, part of my job is to create demos and run experiments that show the cool things developers can do with our platform; one of those things, you guessed it, is using a program to calculate digits of pi. Breaking the record of π was my childhood dream, so a few years ago I decided to try using Google Cloud to take on this project. I also wanted to see how much data processing these computers could handle. In 2019, I became the third woman tobreak this world record, with a π calculation of 31.4 trillion digits.

But I couldn’t stop there, and I decided to try again. And now we have a new record of 100 trillion decimal places. This shows us, again, just how far computers have come: In three years, the computers have calculated three times as many numbers. What’s more, in 2019, it took the computers 121 days to get to 31.4 million digits. This time, it took them 157 days to get to 100 trillion — more than twice as fast as the first project.

A illustrated chart showing how quickly we reached the new pi record compared to the last time in 2019.

But let’s look back farther than my 2019 record: The first world record of computing π with an electronic computer was in 1949, which calculated 2,037 decimal places. It took humans thousands of years to reach the two-thousandth place, and we've reached the 100 trillionth decimal just 73 years later. Not only are we adding more digits than all the numbers in the past combined, but we're spending less and less time hitting new milestones.

An illustration of a person holding a phone and tapping on the screen. Above it reads: "The 82,000 terabytes of data processed during calculations is the equivalent of 160,156 Pixel 6 Pros with max storage (512 GB)."

I used the same tools and techniques as I did in 2019 (for more details, we have a technical explanation in the Google Cloud blog), but I was able to hit the new number more quickly thanks to Google Cloud’s infrastructure improvements in compute, storage and networking. One of the most remarkable phenomena in computer science is that every year we have made incremental progress, and in return we have reaped exponentially faster compute speeds. This is what’s made a lot of the recent computer-assisted research possible in areas like climate science and astronomy.

An illustration of a person with a megaphone. Above it reads: "If you read all 100 trillion digits out loud, one second at a time, it would take you 3,170,929 years to read the whole thing."

Back when I hit that record in 2019 — and again now — many people asked "what's next?" And I’m happy to say that the scientific community just keeps counting. There's no end to π, it’s a transcendental number, meaning it can't be written as a finite polynomial. Plus, we don't see an end to the evolution of computing. Like the introduction of electronic computers in the 1940s and discovery of faster algorithms in the 1960-80s, we could still see another fundamental shift that keeps the momentum going.

So, like I said: I’ll just keep counting.

Helping farmers with cloud technology, up close and global

Global warming brings humankind a host of challenges, from forest fires to heavy storms and desertification. Perhaps none matters more than maintaining and increasing food production. Unseasonal heat and cold snaps, new pest infestations and diseases at unexpected times, or extraordinary drought, wildfire and heavy rain, are just some of the challenges the world's food producers face today and in coming years.

Solutions to the challenges posed by climate change will likely require a two-fold approach. First, we should seek to limit the damage, through more sustainable, less carbon-intensive practices, along with carbon capturing and regenerative agriculture. Second, is to create new ways for farmers to gather and apply information about their crops, to better deal with the challenging new realities of growing food.

Paradoxically, this global challenge calls for better focus on local farming conditions. Farmers worldwide know the particulars of their soil, crops, and rainfall. Farmers can benefit from a better read on how unexpected conditions are affecting their specific farms, so they can take the right steps of prevention and remediation for their farms.

This is why Google Cloud is proud and excited to be working with companies like Agrology, a Virginia-based public benefit company who developed a predictive agriculture system that uses machine learning models, IoT sensors and Artificial Intelligence to deliver farmers timely predictions and insights on everything from temperature, rainfall, and soil conditions, to reducing greenhouse gas emissions from nutrient and fertilizer applications.

Agrology was founded in 2019 with a National Science Foundation SBIR Award, and has gone on to service a number of specialty farms across the country from California to Virginia. The present focus is in wine grape growing and specialty crops, where local soil and climate conditions are particularly important and are under extreme threat. Over time, Agrology will roll out their custom data-driven platform and localized approach to many more farms.

"Early on, we met an apple grower who told us that a weather report from 75 miles away wasn't helping him anymore with figuring out how to apply pesticides, there was too much variation," says Adam Koeppel, Agrology's chief executive. "No farmer wants to overspray pesticides. We started thinking about how holistic agriculture is, and how site-specific it should be."

Agrology developed a custom platform with agricultural sensors which continuously gather a range of data above and below ground. This data is combined with other information, including highly local weather forecasts and macro information like baseline satellite data Agrology then makes sense of all the influences and interactions with TensorFlow, our Machine Learning platform. Google Earth helps the team figure out where to lay out their hardware and wireless gateways so that the team has the necessary tools to deliver data from remote locations to the cloud. “That's a big deal”, says Tyler Locke, Agrology's Chief Technology Officer. "Rural agriculture areas tend to be underserved in technology and infrastructure most of the time," he says. "Farmers want technology to help solve their climate change challenges, but they’ve had a hard time getting it."

We're also pleased to play a role in helping Agrology develop its first data models. Kevin Kelly, Agrology's head of Engineering and Machine Learning, taught himself on Google Colab, a dynamic tool for learning and building and sharing Machine Learning solutions. "Like most engineers, I'm a hands-on learner," Kelly says. "With Colab I was able to step through and execute every line of code, change it, and run it again to see how it affected the output."

Using TensorFlow, Kelly adds, was likewise an easy choice, since "studying model architectures and reading blogs, I found that AI researchers, applications engineers and even hobbyists interested in problems like ours – lots of quality data, lots of interactions among seemingly disparate data sets – overwhelmingly used Tensorflow and Keras to develop their models."

Agrology's cutting-edge approach to agriculture is already showing benefits to its clients, and the team is confident its approach and learnings can scale to an even bigger impact.

"We believe we can help maintain and improve yields, but even more," says Adam. "We are finding ways to help farmers with regenerative agriculture, understanding their ability to enhance soil carbon sequestration with the right crops, better water use, or fertilizer applications that avoid releasing excessive greenhouse gasses. The rate at which the climate is changing is driving growers to alter how they farm and do business. There simply aren’t enough farmers and agronomists, and technology can help growers thrive in spite of the growing challenges.”

The Google Cloud Startup Summit is coming on June 2, 2022

Posted by Chris Curtis, Startup Marketing Manager at Google Cloud

We’re excited to announce our annual Google Cloud Startup Summit will be taking place on June 2nd, 2022.

We hope you will join us as we bring together our startup & VC communities. Join us to dive into topics relevant to startups and enjoy sessions such as:

The future of web3

  • Hear from Google Cloud CEO, Thomas Kurian and Dapper Labs Co-founder and CEO, Roham Gharegozlou, as they discuss web3 and how startups can prepare for the paradigm changes it brings.

VC AMA: Startup Summit Edition

  • Join us for a very special edition of the VC AMA series where we’ll have a discussion with Derek Zanutto from CapitalG, Alison Lange Engel from Greycroft and Matt Turck from FirstMark to discuss investment trends and advice for founders around cloud, data, and the future of disruption in legacy industries.

What’s new for the Google for Startups Cloud Program

  • Exciting announcements from Ryan Kiskis, Director of the Startup Ecosystem at Google Cloud, on how Google Cloud is investing in the startup ecosystem with tailored programs and offers.

Technical leaders & business sessions

  • Growth insights from top startups Discord, Swit, and Streak on how their tech stack helped propel their growth.

Additionally, startups will have an opportunity to join ‘Ask me Anything’ live sessions after the event to interact with Google Cloud startup experts and technical teams to discuss questions that may come up throughout the event.

You can see the full agenda here to get more details on the sessions.

We can’t wait to see you at the Google Cloud Startup Summit. Register to secure your spot today.

Creating new career opportunities with Google Cloud

A year ago, in a forum with chief technology officers from our Google Cloud Partner network, there was one topic on everybody's mind: talent. Or more specifically, a lack of it. All the leaders in the room were finding it incredibly difficult to hire, train and retain top cloud talent. I was hosting this forum and so went away to think how we could best solve this challenge and grow the pool of available cloud-skilled individuals.

In my day job, I lead a team of engineers in the U.K. and Ireland who work with our partners’ technical teams to enable and support them in delivering Google Cloud technologies to our customers. So I was motivated to solve this skills gap. This is not unique to us, either: we know from Gartner that through 2022, insufficient cloud Infrastructure as a service skills will delay half of enterprise IT organisations’ migration to the cloud by two years or more. So this is an industry-wide challenge.

We wanted to do something locally, to help grow the pool of available skilled individuals, ideally tapping into underrepresented groups. This was the genesis of Project Katalyst: to create a programme that would provide equal access to job opportunities for young people who may not have had the chance to go university, giving underrepresented groups a path into a rewarding, well-paid and growing tech sector. Yes, it’s Katalyst with a K, not the traditional C; this is a nod to Kubernetes, a key component of the training. In the recent LinuxFoundation 2021 Jobs Report, cloud and container technologies were ranked as the hottest skill.

To do this quickly at a large scale, we needed to work with a partner with experience in this area. We were introduced to Generation UK, a charity which already does exactly what we are looking to achieve. After our first meeting, it was clear we were completely aligned. Over the following months, as we developed the programme with Generation UK, their drive and expertise has been invaluable in creating the ideal way to prepare, place and support people into careers that would otherwise be inaccessible, all on Google Cloud.

Google already does a lot to make the workplace as inclusive as possible. For me, the Katalyst programme helps us to bring part of that inclusivity to our partners and the wider communities we live in. Growing up, I always thought one day I would be a teacher, following in my mother’s footsteps. While I took a different career path, for me it’s fantastic to have the opportunity, through this programme, to enable life-changing careers, supporting others to learn and hopefully enjoy working with Google Cloud as much as I do, fulfilling, in part, a dream I once had.

The Katalyst programme is 12 weeks long, with the initial pilot running this summer 2022, covering both technical and soft skills training. On the course, participants will go through the Google Cloud Digital Leader certification,and will also do much of the training for the Google Cloud Associate Cloud Engineer certification, which they will be expected to complete in the first six months of their new roles, once they start at our Google Cloud Partners.

Participants will then get to meet and interview for confirmed roles at our Google Cloud Partners with an expected annual salary of up to £30,000 in London. To grow the pool of underrepresented people working on our technology and the workplace in general, the programme is aimed at participants representing a balance of genders, ethnic minority communities, young people who are furthest away from the labour market through no fault of their own, individuals who are not in education, employment or training for more than 6 months, or those with a mental or physical challenge, who've not had a chance to develop their skills. The hope is to then expand this out to other locations in the U.K. and beyond, as well as our customers’ organisations, after we deliver a successful pilot.

If you would like to offer a place to one of our participants at your organisation, you can learn more here or if you are interested in applying for one of the places, or know someone who might, you can apply on Generation UK’s site

Mosquitos get the swat with new forecasting technology

Mosquitoes aren’t just the peskiest creatures on Earth; they infect more than 700 million people a year with dangerous diseases like Zika, Malaria, Dengue Fever, and Yellow Fever. Prevention is the best protection, and stopping mosquito bites before they happen is a critical step.

SC Johnson — a leading developer and manufacturer of pest control products, consumer packaged goods, and other professional products — has an outsized impact in reducing the transmission of mosquito-borne diseases. That’s why Google Cloud was honored to team up with one of the company’s leading pest control brands, OFF!®, to develop a new publicly available, predictive model of when and where mosquito populations are emerging nationwide. 

As the planet warms and weather changes, OFF! noticed month-to-month and year-to-year fluctuations in consumer habits at a regional level, due to changes in mosquito populations. Because of these rapid changes, it’s difficult for people to know when to protect themselves. The OFF!Cast Mosquito Forecast™, built on Google Cloud and available today, will predict mosquito outbreaks across the United States, helping communities protect themselves from both the nuisance of mosquitoes and the dangers of mosquito-borne diseases — with the goal of expanding to other markets, like Brazil and Mexico, in the near future. 

An animated gif titled ‘Mosquito Habitat: Current & Projected’ shows projections for the number of months per year when disease transmission from the Aedes aegypti mosquito is possible as it increases over time from 2019 to 2080. The projection is based on a worst-case scenario in which the impact of climate change is unmitigated.

Source: Sadie J. Ryan, Colin J. Carlson, Erin A. Mordecai, and Leah R. Johnson

With the OFF!Cast Mosquito Forecast™, anyone can get their local mosquito prediction as easily as a daily weather update. Powered by Google Cloud’s geospatial and data analytics technologies, OFF!Cast Mosquito Forecast is the world’s first public technology platform that predicts and shares mosquito abundance information. By applying data that is informed by the science of mosquito biology, OFF!Cast accurately predicts mosquito behavior and mosquito populations in specific geographical locations.

Starting today, anyone can easily explore OFF!Cast on a desktop or mobile device and get their local seven-day mosquito forecast for any zip code in the continental United States. People can also sign up to receive a weekly forecast. To make this forecasting tool as helpful as possible, OFF! modeled its user interface after popular weather apps, a familiar frame of reference for consumers.

Animated gif shows how you enter your zip code into the Off!Cast Mosquita forecast to see a 7-day mosquito forecast for your area, similar to a weather forecast. It shows the mosquito forecast range from medium, high to very high.

SC Johnon’s OFF!Cast platform gives free, accurate and local seven-day mosquito forecasts for zip codes across the continental United States.

The technology behind the OFF!Cast Mosquito Forecast

To create this first-of-its-kind forecast, OFF! stood up a secure and production-scale Google Cloud Platform environment and tapped into Google Earth Engine, our cloud-based geospatial analysis platform that combines satellite imagery and geospatial data with powerful computing to help people and organizations understand how the planet is changing. 

The OFF!Cast Mosquito Forecast is the result of multiple data sources coming together to provide consumers with an accurate view of mosquito activity. First, Google Earth Engine extracts billions of individual weather data points. Then, a scientific algorithm co-developed by the SC Johnson Center for Insect Science and Family Health and Climate Engine experts translates that weather data into relevant mosquito information. Finally, the collected information is put into the model and distilled into a color-coded, seven-day forecast of mosquito populations. The model is applied to the lifecycle of a mosquito, starting from when it lays eggs to when it could bite a human.

It takes an ecosystem to battle mosquitos

Over the past decade, academics, scientists and NGOs have used Google Earth Engine and its earth observation data to make meaningful progress on climate research, natural resource protection, carbon emissions reduction and other sustainability goals. It has made it possible for organizations to monitor global forest loss in near real-time and has helped more than 160 countries map and protect freshwater ecosystems. Google Earth Engine is now available in preview with Google Cloud for commercial use.

Our partner, Climate Engine, was a key player in helping make the OFF!Cast Mosquito Forecast a reality. Climate Engine is a scientist-led company that works with Google Cloud and our customers to accelerate and scale the use of Google Earth Engine, in addition to those of Google Cloud Storage and BigQuery, among other tools. With Climate Engine, OFF! integrated insect data from VectorBase, an organization that collects and counts mosquitoes and is funded by the U.S. National Institute of Allergy and Infectious Diseases.

The model powering the OFF!Cast Mosquito Forecast combines three inputs — knowledge of a mosquito’s lifecycle, detailed climate data inputs, and mosquito population counts from more than 5,000 locations provided by VectorBase. The model’s accuracy was validated against precise mosquito population data collected over six years from more than 33 million mosquitoes across 141 different species at more than 5,000 unique trapping locations.

A better understanding of entomology, especially things like degree days and how they affect mosquito populations, and helping communities take action is critically important to improving public health.

A version of this blogpost appeared on the Google Cloud blog.

Visualizing Google Cloud with 101 illustrated references

Let’s say you make cat posters, and you want to sell them online. You can create a website, but you need to host it online so people can access it. A server hosts the code that lets customers select which cat poster they want, and then buy it. Another server hosts the database of your inventory, which tells you which posters are available to purchase, and it also hosts the image files to show customers pictures of the posters.

Now imagine your posters go viral online, and are incredibly popular. Everyone is interested in going to your website — and that means you need more servers to keep your website up and running. And that server can’t be on your computer, because imagine what happens if you have a power outage, or your computer crashes?

That’s where the cloud comes in — hosting your website on the cloud lets you just focus on the cat posters. But for someone who’s not an engineer, this stuff can get confusing.

I’m a senior developer advocate at Google Cloud, and I’m also an artist. As part of my job at Google, I constantly learn new things and find new ways to share that information with other developers in the community. I’ve learned the power of visual storytelling from my art, and I recently decided to pair up my two skill sets to help explain exactly what the cloud is in a new book, “Visualizing Google Cloud.”

  • Where should I run my app?
  • Which database should I use?
  • Which storage is right for your application?
  • What does Google Cloud Network comprise of?
  • How to build a scalable data analytics pipeline on Google Cloud
  • 6 steps of Data Science
  • Security layers in the cloud

Though my book, which is available for preorder, is aimed at cloud engineers and architects, there are a few lessons that anyone could find useful. For example: What is Cloud? How does it work? Why do you need storage? What is a database and what are the different types? How do you build apps? How do you analyze data? My goal with this book is to give you a visual learning path to all things cloud. And my goal is also to contribute to a good cause; part of the books’ proceeds go directly to a charity that fights malnutrition and supports the right to education.

Machine Learning Communities: Q1 ‘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 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 Sachin Kumar (Qatar) posted Build a retail virtual agent from scratch with Dialogflow CX - Ultimate Chatbot Tutorials. In this tutorial, you will learn how to build a chatbot and voice bot from scratch using Dialogflow CX, a Conversational AI Platform (CAIP) for building conversational UIs.

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.

Kaggle announced the Google Open Source Prize early this year (Winners announcement page). In January, ML GDE Aakash Kumar Nain (India)’s Building models in JAX - Part1 (Stax) was awarded.

In February, ML GDE Victor Dibia (USA)’s notebook Signature Image Cleaning with Tensorflow 2.0 and ML GDE Sayak Paul (India) & Soumik Rakshit’s notebook gaugan-keras were awarded.

TFUG organizer Usha Rengaraju posted Variable Selection Networks (AI for Climate Change) and Probabilistic Bayesian Neural Networks using TensorFlow Probability notebooks on Kaggle. They both got gold medals, and she has become a Triple GrandMaster!

TFUG Chennai hosted the two events, Transformers - A Journey into attention and Intro to Deep Reinforcement Learning. Those events were planned for beginners. Events include introductory sessions explaining the transformers research papers and the basic concept of reinforcement learning.

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.

ML GDE Sayak Paul (India) recently wrote Publishing ConvNeXt Models on TensorFlow Hub. This is a contribution from the 30 versions of the model, ready for inference and transfer learning, with documentation and sample code. And he also posted First Steps in GSoC to encourage the fellow ML GDEs’ participation in Google Summer of Code (GSoC).

ML GDE Merve Noyan (Turkey) trained 40 models on keras.io/examples; built demos for them with Streamlit and Gradio. And those are currently being hosted here. She also held workshops entitled NLP workshop with TensorFlow for TFUG Delhi, TFUG Chennai, TFUG Hyderabad and TFUG Casablanca. It covered the basic to advanced topics in NLP right from Transformers till model hosting in Hugging Face, using TFX and TF Serve.