We’re introducing Google Cloud healthcare customers using Med-PaLM 2 and other generative AI solutions.
Following the announcement of a pilot for a national data research cloud by the UK Government and UK Research and Innovation (UKRI), there is unprecedented momentum towa…
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!
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!
- ML Study Jams: TFUG Bauchi, GDSC Uninter, TFUG Abidjan, MLAct, Universitas Pendidikan Indonesia, National Institute of Technology (Kosen), Kumamoto College, GDG Assiut, GDG Bassam, GDG Cloud Abidjan, GDG Antananarivo, Madan Mohan Malaviya University of Technology - Gorakhpur, Université d'Abomey-Calavi (UAC), ABES Engineering College - Ghaziabad, ABV-IIITM, Vishwakarma University - Pune, Pimpri Chinchwad College of Engineering and Research - Pune, GDG Cloud Edmonton, GDG Cocody, GDG Cloud Wilmington, University of Lay Adventist of Kigali
- ML Paper Reading Clubs: GalsenAI, TFUG Dhaka, Pseudo Lab, TFUG Durg, TFUG Ibadan, Universidad Nacional de Ingeniería, GDG Karaganda, Western University, GDG Raipur, University College Dublin
- ML Math Clubs: TFUG Dhaka, TFUG Hajipur, GDG Yangon, GalsenAI
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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.
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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.
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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.
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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.
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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.
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.
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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.
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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.
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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.
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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.
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.
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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.
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.
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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.
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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.
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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.
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.
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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.
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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.
Please note that the information, uses, and applications expressed in the below post are solely those of our guest author, KDDI.
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| KDDI is integrating text-to-speech & Cloud Rendering to virtual human ‘Metako’ |
VTubers, or virtual YouTubers, are online entertainers who use a virtual avatar generated using computer graphics. This digital trend originated in Japan in the mid-2010s, and has become an international online phenomenon. A majority of VTubers are English and Japanese-speaking YouTubers or live streamers who use avatar designs.
KDDI, a telecommunications operator in Japan with over 40 million customers, wanted to experiment with various technologies built on its 5G network but found that getting accurate movements and human-like facial expressions in real-time was challenging.
Announced at Google I/O 2023 in May, the MediaPipe Face Landmarker solution detects facial landmarks and outputs blendshape scores to render a 3D face model that matches the user. With the MediaPipe Face Landmarker solution, KDDI and the Google Partner Innovation team successfully brought realism to their avatars.
Using Mediapipe's powerful and efficient Python package, KDDI developers were able to detect the performer’s facial features and extract 52 blendshapes in real-time.
import mediapipe as mp
from mediapipe.tasks import python as mp_python
MP_TASK_FILE = "face_landmarker_with_blendshapes.task"
class FaceMeshDetector:
def __init__(self):
with open(MP_TASK_FILE, mode="rb") as f:
f_buffer = f.read()
base_options = mp_python.BaseOptions(model_asset_buffer=f_buffer)
options = mp_python.vision.FaceLandmarkerOptions(
base_options=base_options,
output_face_blendshapes=True,
output_facial_transformation_matrixes=True,
running_mode=mp.tasks.vision.RunningMode.LIVE_STREAM,
num_faces=1,
result_callback=self.mp_callback)
self.model = mp_python.vision.FaceLandmarker.create_from_options(
options)
self.landmarks = None
self.blendshapes = None
self.latest_time_ms = 0
def mp_callback(self, mp_result, output_image, timestamp_ms: int):
if len(mp_result.face_landmarks) >= 1 and len(
mp_result.face_blendshapes) >= 1:
self.landmarks = mp_result.face_landmarks[0]
self.blendshapes = [b.score for b in mp_result.face_blendshapes[0]]
def update(self, frame):
t_ms = int(time.time() * 1000)
if t_ms <= self.latest_time_ms:
return
frame_mp = mp.Image(image_format=mp.ImageFormat.SRGB, data=frame)
self.model.detect_async(frame_mp, t_ms)
self.latest_time_ms = t_ms
def get_results(self):
return self.landmarks, self.blendshapes |
The Firebase Realtime Database stores a collection of 52 blendshape float values. Each row corresponds to a specific blendshape, listed in order.
_neutral,
browDownLeft,
browDownRight,
browInnerUp,
browOuterUpLeft,
... |
These blendshape values are continuously updated in real-time as the camera is open and the FaceMesh model is running. With each frame, the database reflects the latest blendshape values, capturing the dynamic changes in facial expressions as detected by the FaceMesh model.
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After extracting the blendshapes data, the next step involves transmitting it to the Firebase Realtime Database. Leveraging this advanced database system ensures a seamless flow of real-time data to the clients, eliminating concerns about server scalability and enabling KDDI to focus on delivering a streamlined user experience.
import concurrent.futures
import time
import cv2
import firebase_admin
import mediapipe as mp
import numpy as np
from firebase_admin import credentials, db
pool = concurrent.futures.ThreadPoolExecutor(max_workers=4)
cred = credentials.Certificate('your-certificate.json')
firebase_admin.initialize_app(
cred, {
'databaseURL': 'https://your-project.firebasedatabase.app/'
})
ref = db.reference('projects/1234/blendshapes')
def main():
facemesh_detector = FaceMeshDetector()
cap = cv2.VideoCapture(0)
while True:
ret, frame = cap.read()
facemesh_detector.update(frame)
landmarks, blendshapes = facemesh_detector.get_results()
if (landmarks is None) or (blendshapes is None):
continue
blendshapes_dict = {k: v for k, v in enumerate(blendshapes)}
exe = pool.submit(ref.set, blendshapes_dict)
cv2.imshow('frame', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
exit() |
To continue the progress, developers seamlessly transmit the blendshapes data from the Firebase Realtime Database to Google Cloud's Immersive Stream for XR instances in real-time. Google Cloud’s Immersive Stream for XR is a managed service that runs Unreal Engine project in the cloud, renders and streams immersive photorealistic 3D and Augmented Reality (AR) experiences to smartphones and browsers in real time.
This integration enables KDDI to drive character face animation and achieve real-time streaming of facial animation with minimal latency, ensuring an immersive user experience.
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On the Unreal Engine side running by the Immersive Stream for XR, we use the Firebase C++ SDK to seamlessly receive data from the Firebase. By establishing a database listener, we can instantly retrieve blendshape values as soon as updates occur in the Firebase Realtime database table. This integration allows for real-time access to the latest blendshape data, enabling dynamic and responsive facial animation in Unreal Engine projects.
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After retrieving blendshape values from the Firebase SDK, we can drive the face animation in Unreal Engine by using the "Modify Curve" node in the animation blueprint. Each blendshape value is assigned to the character individually on every frame, allowing for precise and real-time control over the character's facial expressions.
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An effective approach for implementing a realtime database listener in Unreal Engine is to utilize the GameInstance Subsystem, which serves as an alternative singleton pattern. This allows for the creation of a dedicated BlendshapesReceiver instance responsible for handling the database connection, authentication, and continuous data reception in the background.
By leveraging the GameInstance Subsystem, the BlendshapesReceiver instance can be instantiated and maintained throughout the lifespan of the game session. This ensures a persistent database connection while the animation blueprint reads and drives the face animation using the received blendshape data.
Using just a local PC running MediaPipe, KDDI succeeded in capturing the real performer’s facial expression and movement, and created high-quality 3D re-target animation in real time.
To learn more, watch Google I/O 2023 sessions: Easy on-device ML with MediaPipe, Supercharge your web app with machine learning and MediaPipe, What's new in machine learning, and check out the official documentation over on developers.google.com/mediapipe.
This MediaPipe integration is one example of how KDDI is eliminating the boundary between the real and virtual worlds, allowing users to enjoy everyday experiences such as attending live music performances, enjoying art, having conversations with friends, and shopping―anytime, anywhere.
KDDI’s αU provides services for the Web3 era, including the metaverse, live streaming, and virtual shopping, shaping an ecosystem where anyone can become a creator, supporting the new generation of users who effortlessly move between the real and virtual worlds.
Posted by Max Saltonstall, Developer Relations Engineer
In this ongoing interview series we sit down with Google Cloud Champion Innovators across the world to learn more about their journeys, their technology focus, and what excites them. Today we're talking to Elyes Manai. Elyes is a Machine Learning Consultant, Educator and Mentor. He helps companies tap into the power of data science to reduce costs and increase revenue as well as build relationships with relevant target audiences through educational content and community building.
Champion Innovators are a global network of more than 500 non-Google professionals, who are technical experts in Google Cloud products and services. Each Champion specializes in one of nine different technical categories which are cloud AI/ML, data analytics, hybrid multi-cloud, modern architecture, security and networking, serverless app development, storage, Workspace and databases.
Machine Learning: There are so many new insights we can gain from applying ML and AI to problems right now. Especially in security. I'm currently pursuing my PhD in AI applied to Cybersecurity, and am eager to teach the next generation about computer science, AI and security.
I fell into ML by accident, after trying to pursue something else in university. I had hoped to study architecture, but did not do nearly well enough in high school (in Tunisia, where I'm from). I ended up at my last choice of universities, in an IT program. And then I tried to transfer to an architecture school, but my paperwork got messed up so it didn't work out.
There I was, in a field I had not chosen, and yet I liked it. It felt pretty easy to do, I got good grades, and I realized I could make a career out of it. I liked solving problems with code, and progressed to doing web development and managing a team. From there I started thinking about what I wanted to do next.
I really love teaching, so I began looking into how to become a professor. That led me to the computer science? 50 class at Harvard, where I saw many signs pointing to a big AI trend, and so I decided to pursue a masters in computer science.
I dive right in; learn by doing. I frequently bounce around between subjects. I keep a list of ideas that come to me, and then when I'm ready for something new, I just scan through the list and pick one. This helps me stay fresh and excited.
Whenever I'm learning new skills I remind myself to go with the flow. I start small, learn just enough to start using the technology or tool. I'll ask myself:
- What key concepts or pillars do I need to understand this more deeply?
- How do I branch out from there?
- Who should I talk to?
- What can I make?
Since I'm in the middle of a doctoral program right now, I always challenge myself to make that idea somehow connect to my research, so I can bring it back to a common theme that's pervasive through all my work.
Explainable AI, especially applied to less frequently used spoken languages in the world. We have a wealth of research on English language AI models, but what about applying BERT (and other technologies) on some lesser used languages, to expand the benefit to a wider population?
I'm also very excited about how we (as researchers) can optimize AI models to be more secure, be more private in terms of protecting our data, and be more useful to a wide variety of use cases.
I love biking, and whenever it's warm enough in Québec I will go bike outside.
I like to read, especially science fiction. I recently started reading autobiographies to know more about the world from different perspectives. I'm currently reading autobiographies of Scott Kelley and Sohaila Abdulali.
I also keep a big list of ideas outside of tech for me to pursue: people to meet, foods to try, places to go. I'm always working on new experiences and adventures from that list, to broaden my world and learn more about what's all around me.
I've been a Google Developer Expert (GDE) for two years and then got an invitation to join the Innovators program, after I attended a GDE event. It's helped me gain some respect and credibility, as I have a little bit of Google's reputation behind my voice now when I share my perspective or opinion. Also they have helped me get some great swag!
Very few things stand the test of time, as our industry is shifting so quickly. I think CS50 on YouTube still has relevance for folks new to computer science.
I also want to encourage people to create social connections, and go meet the people behind the systems you are using. There are humans out there who can help you find the next project or position, and getting to know them is so important.
Developers - it’s finally here . . . the Google Cloud Next ‘23 session library is live!
So many awesome sessions to choose from, it's tough!
Of course we start with the big story of the year, the thing on everyone's (everything's?) mind: AI!
Check out "5 practical considerations for adopting AI" to get started or "Build your organization’s future on Google AI and machine learning infrastructure" for teams that are looking to expand into cross-functional AI-powered innovation.
Sometimes you've got an awesome idea, and you are looking for a way to speed up getting it to market. We can help. Attend "Building fast, scalable and reliable apps with Firebase and Cloud Run" to learn about serverless, accessible and language-agnostic tools to enable higher cloud velocity. Or come to "Build your first event driven app in less than 5 minutes" and walk away with a reference app for your own event-driven architecture use later on.
Lots of folks take a measured approach to public cloud adoption, especially with how rapidly technology is changing. This is especially true in corporate IT, where change can be tough. Check out "The future of modern enterprise applications with GKE" to learn more about moving your company's apps and workflows to the cloud.
We're all drowning in data these days, and cloud offers many (MANY!) tools to help. Learn where you can get a handle on your data, analytics and insights with "What's next for Data and AI?" and then point your data engineering teams to "What's new with BigQuery" for the latest advances.
If you are looking at how you secure your own migration to cloud-based apps and services, make sure you attend "What’s new in cloud-first CI/CD" to get up to speed on Cloud Build, Artifact Registry, Cloud Deploy and more. These interconnected tools can accelerate development, help with segmentation of roles and responsibilities, and allow for zero to worldwide scale with very little operational overhead.
For developers building apps for specific industries, we've got a wide variety of sessions from Retail to Games to Public Sector to Manufacturing. Come learn from customers about AI applications in automation and personalization in "From vision to practice: AI applications in financial services" and take advantage of the latest tools. Or you could dive into the latest craze with "Media’s AI frontier: Navigating the future of entertainment” and start to answer the question we've all been asking: was this blog written by a person or an AI?
There are sessions for every flavor of developer, architect, designer and operator, and so many opportunities to engage with experts from industry. So join us at Google Cloud Next to learn about key topics from speakers like Gerrit Kazmaier, Dave Nettleton, Keelin McDonnell, Donna Schit, and more.
And that's not all! You can find a series of training workshops available for all skill levels, and a dedicated learning and certification booth to help you on your way to your new cloudy career and skilling journey. Plus we've got a set of lightning talks to give you bite-sized chunks of knowledge across every cloud topic.
Oh no, I'm out of time and I haven't even gotten to the return of Drone Racing League at Next. Guess you'll just have to come and find out. See you there!
Register for Google Cloud Next ‘23 now: August 29-31 in San Francisco.
Posted by Ashley Francisco Head of Startup Ecosystem, North America, Google & Darren Mowry, Managing Director, Corporate Sales, Google
We’re kicking off a summer of accelerators by welcoming the inaugural 2023 North American Google for Startups Accelerator: Cloud cohort, our new class of cloud-native startups in the United States and Canada.
This 10-week virtual accelerator brings the best of Google's programs, products, people and technology to startups doing interesting work in the cloud. We’re excited to offer these startups cloud mentorship and technical project support, along with deep dives and workshops on product design, customer acquisition and leadership development for technology startup founders and leaders.
We heard from some of the founders from this year’s cohort - including New York City-based Harmonic Discovery, Toronto-based Oncoustics, and Vancouver-based OneCup AI - demonstrating how they are using Google Cloud data, analytics, AI, and other technologies across healthcare, agriculture and farming, and more. Read more on their aspirations for the program below:
"The team at Harmonic Discovery is excited to scale our deep learning infrastructure for drug discovery using Google Cloud. We also want to learn best practices from the Google team on training and developing machine learning models in a cost effective way.” – Rayees Rahman CEO, Harmonic Discovery
"We're very excited to grow our presence in the healthcare space by bringing our ultrasound based "virtual biopsy" solutions to clinics and serve over 2B people with liver diseases globally. Specifically in the Google for Startups Accelerator: Cloud program, we're looking to develop and hone our ability to efficiently scale our ML environments and processes to support the development of multiple new diagnostic products in parallel. We're also very excited about creating an edge-cloud hybrid solution with effective distribution of AI processing across GCP and Pixel 7 Pro.” – Beth Rogozinski CEO, Oncoustics
"Our primary objective is to leverage Google Cloud Platform's (GCP) cutting-edge technologies to enhance BETSY, our computer vision AI for animal care. Our milestones include developing advanced image recognition models and achieving real-time processing speeds for large-scale datasets. The accelerator will play a vital role in helping us refine our algorithms and optimize our infrastructure on GCP.” – Mokah Shmigelsly, Co-Founder & CEO and Geoffrey Shmigelsky, Co-Founder & CTO, OneCup AI
We received so many great applications for this program and we're excited to welcome the 12 startups that make up the the inaugural North American Cloud cohort:
- Aiden Automotive (San Ramon, CA): Aiden is one of the first software solutions to provide streaming two-way communication directly with the vehicle and across vehicle brands. Aiden provides simple and intuitive 100% GDPR and CCPA compliant consent management, enabling car owners to choose which digital services they desire.
- Binarly (Santa Monica, CA): Binarly’s agentless, enterprise-class AI-powered firmware security platform helps protect from advanced threats below the operating system. The company’s technology solves firmware supply chain security problems by identifying vulnerabilities, malicious firmware modifications and providing firmware SBOM visibility without access to the source code. Binarly’s cloud-agnostic solutions give enterprise security teams actionable insights, and reduce the cost and time to respond to security incidents.
- Duality.ai (San Mateo, CA): Duality AI is an augmented digital twin platform that provides end-to-end workflows for predictive simulation and high fidelity visualization. The platform helps close data gaps for machine learning teams working on perception problems and helps robotics teams speed up design and validation of their autonomy software.
- HalloAI (Provo, UT): Hallo is an AI-powered language learning platform for speaking. Press a button and start speaking any language with an AI teacher in 3 seconds.
- Harmonic Discovery (New York, NY): Harmonic Discovery uses machine learning to design multi-targeted kinase drugs for cancer and autoimmune diseases.
- MLtwist (Santa Clara, CA): MLtwist helps companies bring AI to the world faster. It gives data scientists and ML engineers access to the easiest and best way to get out of the weeds of data pipelines and back to what they enjoy and do best – design, build, and deploy AI.
- Oncoustics (Toronto, ON): Oncoustics is creating advanced solutions for low-cost and non-invasive surveillance, diagnostics, and treatment monitoring of diseases with high unmet clinical need through the use of patented AI-based solutions running on ultrasound scans. Using a handheld point of care ultrasound, Oncoustics’ first solution allows clinicians to obtain a liver health assessment within 5 minutes.
- OneCup AI (Vancouver, BC): OneCup uses Computer vision for Animal Care. Our AI, BETSY, is the eyes of the rancher when the rancher is away.
- Passio AI (Menlo Park, CA): Passio AI is a mobile AI platform that helps developers and companies build mobile applications powered by expert-level AI and computer vision.
- RealKey (San Francisco, CA): RealKey is one of the first collaboration platforms built specifically for finance (starting with mortgages), automating documentation collection/review, tasks, and communication for all parties (not just borrowers) involved in transactions to reduce time, effort, and costs to close.
- Sevco Security Inc. (Austin, TX): Sevco Security a leading IT asset visibility and cybersecurity company, that provides the industry’s first unified asset intelligence platform designed to address the new extended attack surface and create a trusted data repository of all devices, users and applications an organization uses.
- VESSL AI (San Jose, CA): VESSL is an end-to-end MLOps platform aiming to be the next Snowflake for AI. The platform enables MLEs to run ML workloads at any scale on any cloud, such as AWS, Google Cloud Platform, Oracle Cloud, and on-premises.
As tech advancements continue at lightning speed, it’s an exciting opportunity to work with these founders and startup teams to help grow and scale their business. Programming for the Google for Startups Accelerator: Cloud begins mid-July and we can’t wait to see how far these startups go!
Earlier this month, LA TechWeek hosted an array of thought leaders and innovative minds in the tech industry. As the Head of VC & Startup Partnerships West Coast at Google, I had the privilege of curating and facilitating an insightful panel event, supported by Google Cloud for Startups, on the topic of "Building with Generative AI" with representatives from:
- Google Cloud: Josh Gwyther, Global AI Lead
- Andreessen Horowitz: Andrew Chen, General Partner
- Gradient Ventures: Darian Shirazi, General Partner
- Alphabet’s X: Clarence Wooten, Director & Entrepreneur in Residence
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Our conversation was as rich in depth as it was in diversity; heightening the LA community's collective excitement for the future of generative AI, and underscoring Google's vision of harnessing the power of collaboration to ignite innovation in the tech startup space. The collaborative event was a unique platform that bridged the gap between startups, venture capitalists, and major players in the tech industry. It was the embodiment of Google's commitment to driving transformative change by fostering robust partnerships with VC firms and startups: We understand that the success of startups is crucial to our communities, economies, and indeed, to Google itself.
Josh Gwyther, Generative AI Global Lead for Google Cloud, kicked things off by tracing Google's impressive journey in AI, shedding light on how we've pioneered in creating transformative AI models, a journey that started back in 2017 with the landmark Transformer whitepaper.
From X, Clarence Wooten elevated our perception of AI's potential, painting an exciting picture of AI as a startup's virtual "co-founder." He powerfully encapsulated AI's role in amplifying, not replacing, human potential, a testament to Google's commitment to AI and its impact.
Venturing into the world of gaming, Andreessen Horowitz's Andrew Chen predicted a revolution in game development driven by generative AI. He saw a future where indie game developers thrived, game types evolved, and the entire gaming landscape shifted, all propelled by generative AI's transformative power.
On the investment side of things, Darian Shirazi from Gradient Ventures shared insights on what makes an excellent AI founder, emphasizing trustworthiness, self-learning, and resilience as critical traits.
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The panel discussion concluded with a deep dive into the intricacies of integrating AI and scalability, the challenges of GPUs/TPUs, and the delicate balance between innovation and proprietary data concerns.
Founders were also left with actionable information around the Google for Cloud Startups Program, which provides startup experts, cloud credits, and technical training to begin their journey on Google Cloud cost-free, with their focus squarely on innovation and growth. We invite all eligible startups to apply as we continue this journey together.
As the curtains fell on LA TechWeek, we were left with more than just a feeling of optimism about the future of generative AI. We walked away with new connections, fresh perspectives, and a renewed conviction that Google, along with startups, investors, and partners, can lead the transformative change that the future beckons. The main takeaway: The AI revolution isn't coming; it's here. And Google, with its deep expertise and unwavering dedication to innovation, is committed to moving forward boldly, responsibly, and in partnership with others.
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As we navigate this thrilling journey, I look forward to continuing to collaborate with startups, investors, and partners, leveraging the vast potential of AI to unlock a future where technology serves us all in unimaginable ways.
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 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.
- Tweet Image Maker - Learning to recommend engaging images for tweets by ML GDE Victor Dibia (United States): a slide deck and a Kaggle Notebook
- Training a recommendation model with dynamic embeddings by ML GDE Thushan Ganegedara (Australia): a slide deck and a Github repository
- Easy Image Segmentation in Android with MediaPipe, TFLite Interpreter or Task Library by ML GDE George Soloupis (Greece): a slide deck and a blog posting
- MediaPipe Intro - Image Classification & Embedding by ML GDE Margaret Maynard-Reid (United States): a slide deck and examples on GitHub
- MediaPipe Web APIs - Image segmentation by ML GDE Henry Ruiz (United States): a slide deck and a GitHub repository
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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.
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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.
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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.
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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.
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.
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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.
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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.
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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.
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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.
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.
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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.
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.
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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.
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.
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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.
Startups are solving the world’s most important challenges with agility, innovative technology, and determination, and Google is proud to support them.
TL;DR: Applications are now open for the inaugural North American Google for Startups Accelerator: Cloud cohort. Designed to help connect founders who are building with Cloud to the people, products, and best practices they need to grow, this 10-week virtual accelerator will help 8-12 startups prepare for the next phase of their growth journey.
Around the world, the cloud is helping businesses and governments accelerate their digital transformations, scale their operations, and innovate in new areas. At Google Cloud, we’re helping businesses solve some of their toughest challenges. For instance, we’ve partnered with innovative digital native companies like cart.com to democratize ecommerce by giving brands of all sizes the full capabilities needed to take on the world’s largest online retailers, and with dynamic startups like kimo.ai which leverages our AI tools to transform traditional approaches to online learning.
The adoption and acceleration of Google Cloud unlocks massive potential for startups as the global cloud market is set to grow to more than $470 billion over the next five years. With the artificial intelligence/machine learning (AI/ML) landscape evolving rapidly, this moment presents an exciting and unique opportunity for startups. The Google for Startups Accelerator: Cloud program helps cloud-native startups using AI/ML to seize the opportunities ahead.
Starting today, U.S.- and Canada-based startups can apply for the Google for Startups Accelerator: Cloud program. This equity-free, 10-week virtual accelerator will offer cloud mentorship and technical project support, as well as deep dives and workshops on product design, customer acquisition and leadership development for cloud startup founders and leaders.
The Accelerator program is designed to bring the best of Google's programs, products, people and technology to startups doing impactful work in the cloud.
Here’s what our recent North American Accelerator alumni had to say:
“Thanks to truly amazing mentorship and direct access to Googlers, we have been able to reach new levels of specialized knowledge and deployment capability in our GCP architecture and artificial intelligence projects. From a technical perspective to a business growth standpoint, this is simply invaluable. What we have built in three months with Google will be a part of our upcoming next-gen product line in both Healthcare and Non-Healthcare settings. We deeply thank all Googlers for their exceptional participation in our journey." – Francois Gand, Founder and CEO, NURO
"The accelerator provided F8th Inc. with so much more than we could have ever dreamed. The meaningful mentorship relationships that have been created continue to endure, the workshops have been impactful in helping our business scale, and we have developed new business contacts both in Canada and the US. The incredible support and guidance we received has been second to none. It’s been great to have access to a multidisciplinary team and Google’s outside-the-box thinking.” — Vivene Salmon, Co-Founder, F8th Inc." – Vivene Salmon, Co-Founder, F8th Inc.
Applications are now being accepted until May 30, and the Accelerator will kick-off this July. Interested startups leveraging cloud to drive growth and innovation are encouraged to apply here.
Posted by Nari Yoon, Bitnoori Keum, Hee Jung, DevRel Community Manager / Soonson Kwon, DevRel Program Manager
A guest post by the XR Development team at 
Posted by Alexandra Dumas, Head of VC & Startup Partnerships, West Coast, Google
