Tag Archives: developers

Build your first AppSheet app: how I built a food tracker

Posted by Filipe Gracio, PhD - Customer Engineer

I keep forgetting what I have in the freezer. At first I used Google Sheets to keep track of it, but I wanted something that was easy to consult and update on my smartphone. So I turned to AppSheet! Here’s a tutorial to follow to make a similar tracking solution.

Creating the database

First I created a database that imported my data from the Sheet:

A cropped screen shot illustrating creating a database in AppSheet by importing data from sheets

After I selected “Import from Sheets” and selected the sheet I was cumbersomely maintaining, I get the preview of the new database:

A cropped screen shot illustrating creating a database in AppSheet by importing data from sheets

Creating the App

Then I can go back and create an App:

A cropped screen shot illustrating creating an app

After I name it I can select the database I just created.

A cropped screen shot illustrating step 1 of selecting the database

Then

A cropped screen shot illustrating step 2 of selecting the database

The App now starts getting created, and then I can start customizing it!

Customizing the App:

I decided I want to actually add more information to the App. For example, I want to categorize my items, so I need another column. I can edit the data for this and I'll add a column “Category”.

A cropped screen shot illustrating editing the data

After adding the extra column, this is the result:

A cropped screen shot showing the data with the new column added

That’s going to come in handy later for presentation and organization!

Now let's do some configuration about how the items are presented on the actual app. That’s in the UX section of the App builder. I want to select “Table”, Group by “category” and then sort alphabetically by “Item”

A cropped screen shot showing The Pirmary views in the UX Section of the App builder

After tweaking a few more options in UX “Brand” and “Format Rules”, this is how my app is looks:

A screen shot of the app on a mobile device displaying with content from the original dataset

Using the App - adding and updating items.

Now, I can see what I have in the freezer at all times. If I cook something and have a leftover, I can just add it by clicking the + button. After that, I just need to add in the info:

A screen shot illustrating the functionality of the app on a mobile device

And of course, if I use something I can just tap on it to edit the amount (or delete it).

Try it yourself!

This small App is something I use every week now! It is much easier than my old method, plus I learned how to use AppSheet. And this was just a quite simple use case - which only touched the tip of the iceberg of AppSheet’s features. If you work for organizations that have information to share and organize, this technology could be useful for you.

Try it out for yourself: you can use the complete set of AppSheet features at no cost while building one or many app prototypes. You can also invite up to 10 test users at no cost to use your apps and share feedback.

Thank you to my colleague Florian Opitz, Customer Engineer - Google Workspace + Security , for his useful edits and suggestions.

ML Olympiad 2023: Globally Distributed ML Competitions by Google ML Community

Posted by Hee Jung, DevRel Community Manager

What is the ML Olympiad?

The ML Olympiad is an associated Kaggle Community Competitions hosted by ML GDE, TFUG, 3rd-party ML communities, supported by Google Developers. The ML Developer Programs team and the communities successfully ran the first round of the campaign in 2022 and are now launching the second round. The goal of this campaign is to provide ML training opportunities for developers by leveraging Kaggle’s features.

ML Olympiad Community Competitions

17 ML Olympiad community competitions are currently open. Visit the ML Olympiad page to participate.

Into the Space

  • Predicting which spaceship passengers were transported by the anomaly using records recovered from the spaceship’s damaged computer system.
  • Host: MD Shahriyar Al Mustakim Mitul / TFUG Dhaka

    Water Quality Prediction

    • Estimating the quality of water.
    • Hosts: Usha Rengaraju, Vijayabharathi Karuppasamy (TFUG Chennai), Samuel T (TFUG Mysuru)

      Breast Cancer Diagnosis

      • Predicting medical diagnosis [breast cancer].
      • Host: Ankit Kumar Verma / TFUG Prayagraj

        Book Recommendations

        • To provide personalized recommendations to users based on their reading history and preferences using various machine learning algorithms.
        • Hosts: Anushka Raj, Yugandhar Surya / TFUG Hajipur

          Argania Tree Deforestation Detection

          • Use Sentinel-2 satellite imagery to detect and map areas of deforestation in the Argania region.
          • Hosts: Taha Bouhsine / TFUG Agadir

            Multilingual Spell Correction

            • Reconstruct noisy sentences in European languages: English, French, German, Bulgarian and Turkish.
            • Host: Radostin Cholakov (ML GDE)

              CO2 Emissions Forecasting

              • Forecasting CO2 emissions based on deforestation in Côte d'Ivoire.
              • Hosts: Armel Yara, Kimana Misago, Jordan Erifried / TFUG Abidjan

                Ensure Healthy Lives (in local language) 

                • Use ML techniques to help achieve common good health and well-being.
                • Hosts: Vinicius Fernandes Caridá (ML GDE), Pedro Gengo, Alex Fernandes Mansano / TFUG São Paulo

                  Predictive Maintenance

                  • Predict future engine’s failures.
                  • Host: Daniel Pereda / TFUG Santiago

                    Firetrucks Are Red And Cars Are Blue

                    • To create a model that can accurately predict the correct class for each image, without overfitting.
                    • Host: Prasoon Kottarathil / TFUG Thrissur

                      Dialect Recognition (in Arabic) 

                      • Dialect recognition in order to improve user experience in AI applications.
                      • Hosts: Ruqiya Bin Safi (ML GDE), Eyad Sibai, Hussain Alfayez / Saudi TFUG & Applied ML/AI group

                        Sentiment Analysis Of JUMIA Tunisia  (in local language) 

                        • Use JUMIA customer reviews to determine the sentiment of content from text data.
                        • Host: Boulbaba BEN AMMAR / TFUG Sfax

                          Kolkata Housing Prediction

                          • Kolkata housing prediction results can be used to address related social and economic issues.
                          • Host: Rishiraj Acharya / TFUG Kolkata

                            Can You Guess The Beer Style?

                            • This is a machine learning competition focused on classifying beer into 17 distinct styles based on key descriptors.
                            • Host: Marvik

                              Detect ChatGpt answers

                              • The goal of this competition is to classify ChatGpt answers vs real human answers for a variety of questions.
                              • Host: Elyes Manai (ML GDE) / IEEE ESSTHS + GDSC ISETSO + PyData Tunisia

                                MLAct Pose Detection

                                • Raising awareness about some basic yoga poses, and encouraging our community members to practice the basic parts of computer vision.
                                • Host: Imen Masmoudi / MLAct ML Community

                                  Hausa Sentiment Analysis 2.0 (in local language) 

                                  • Classify the sentiment of sentences of Hausa Language.
                                  • Hosts: Nuruddeen Sambo, Dattijo Murtala Makama / TFUG Bauchi

                                    Navigating ML Olympiad

                                    You can search “ML Olympiad” on Kaggle Community Competitions page to see them all. And for further info, look for #MLOlympiad on social media.

                                    Google Developers supports the hosts of each competition. Browse through the available competitions and participate in those that interest you!

                                    Machine Learning Communities: Q4 ‘22 highlights and achievements

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

                                    Let’s explore highlights and accomplishments of vast Google Machine Learning communities over the last quarter of 2022. We are enthusiastic and grateful about all the activities by the global network of ML communities. Here are the highlights!


                                    ML at DevFest 2022

                                    A group of ML Developers attending DevFest 2022

                                    A large number of members of ML GDE, TFUG, and 3P ML communities participated in DevFests 2022 worldwide covering various ML topics with Google products. Machine Learning with Jax: Zero to Hero (DevFest Conakry) by ML GDE Yannick Serge Obam Akou (Cameroon) and Easy ML on Google Cloud (DevFest Med) by ML GDE Nathaly Alarcon Torrico (Bolivia) hosted great sessions.

                                    ML Community Summit 2022

                                    A group of ML Developers attending ML Community Summit

                                    ML Community Summit 2022 was hosted on Oct 22-23, 2022, in Bangkok, Thailand. Twenty-five most active community members (ML GDE or TFUG organizer) were invited and shared their past activities and thoughts on Google’s ML products. A video sketch from ML Developer Programs team and a blog posting by ML GDE Margaret Maynard-Reid (United States) help us revisit the moments.

                                    TensorFlow

                                    MAXIM in TensorFlow by ML GDE Sayak Paul (India) shows his implementation of the MAXIM family of models in TensorFlow.

                                    Diagram of gMLP block

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

                                    Building Computer Vision Model using TensorFlow: Part 2 by TFUG Pune for the developers who want to deep dive into training an object detection model on Google Colab, inspecting the TF Lite model, and deploying the model on an Android application. ML GDE Nitin Tiwari (India) covered detailed aspects for end-to-end training and deployment of object model detection.

                                    Advent of Code 2022 in pure TensorFlow (days 1-5) by ML GDE Paolo Galeone (Italy) solving the Advent of Code (AoC) puzzles using only TensorFlow. The articles contain a description of the solutions of the Advent of Code puzzles 1-5, in pure TensorFlow.

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

                                    Screen shot of TensorFlow Lite on Android Project Practical Course
                                    A hands-on course on TensorFlow Lite projects on Android by ML GDE Xiaoxing Wang (China) is the book mainly introducing the application of TensorFlow Lite in Android development. The content focuses on applying three typical ML applications in Android development.

                                    Build tensorflow-lite-select-tf-ops.aar and tensorflow-lite.aar files with Colab by ML GDE George Soloupis (Greece) guides how you can shrink the final size of your Android application’s .apk by building tensorflow-lite-select-tf-ops.aar and tensorflow-lite.aar files without the need of Docker or personal PC environment.

                                    TensorFlow Lite and MediaPipe Application by ML GDE XuHua Hu (China) explains how to use TFLite to deploy an ML model into an application on devices. He shared experiences with developing a motion sensing game with MediaPipe, and how to solve problems that we may meet usually.

                                    Train and Deploy TensorFlow models in Go by ML GDE Paolo Galeone (Italy) delivered the basics of the TensorFlow Go bindings, the limitations, and how the tfgo library simplifies their usage.

                                    Keras

                                    Diagram of feature maps concatenated together and flattened

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

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

                                    Mixed precision in Keras based Stable Diffusion
                                    Let’s Generate Images with Keras based Stable Diffusion by ML GDE Chansung Park (Korea) delivered how to generate images with given text and what stable diffusion is. He also talked about Keras-based stable diffusion, basic building blocks, and the advantages of using Keras-based stable diffusion.

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

                                    TFX

                                    Digits + TFX banner

                                    How startups can benefit from TFX by ML GDE Hannes Hapke (United States) explains how the San Francisco-based FinTech startup Digits has benefitted from applying TFX early, how TFX helps Digits grow, and how other startups can benefit from TFX too.

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

                                    Hyperparameter Tuning and ML Pipeline by ML GDE Chansung Park (Korea) explained hyperparam tuning, why it is important; Introduction to KerasTuner, basic usage; how to visualize hyperparam tuning results with TensorBoard; and integration within ML pipeline with TFX.

                                    JAX/Flax

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

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

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

                                    Introduction to JAX with Flax (slides) by ML GDE Phillip Lippe (Netherlands) reviewed from the basics of the requirements we have on a DL framework to what JAX has to offer. Further, he focused on the powerful function-oriented view JAX offers and how Flax allows you to use them in training neural networks.

                                    Screen grab of ML GDE David Cardozo and Cristian Garcia during a live coding session of a review of new features, specifically Shared Arrays, in the recent release of JAX
                                    JAX Streams: Exploring JAX 0.4 by ML GDE David Cardozo (Canada) and Cristian Garcia (Colombia) showed a review of new features (specifically Shared Arrays) in the recent release of JAX and demonstrated live coding.

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

                                    Kaggle

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

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

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

                                    Cloud AI

                                    Better Hardware Provisioning for ML Experiments on GCP by ML GDE Sayak Paul (India) discussed the pain points of provisioning hardware (especially for ML experiments) and how we can get better provision hardware with code using Vertex AI Workbench instances and Terraform.

                                    Jayesh Sharma, Platform Engineer, Zen ML; MLOps workshop with TensorFlow and Vertex AI November 12, 2022|TensorFlow User Group Chennai
                                    MLOps workshop with TensorFlow and Vertex AI by TFUG Chennai targeted beginners and intermediate-level practitioners to give hands-on experience on the E2E MLOps pipeline with GCP. In the workshop, they shared the various stages of an ML pipeline, the top tools to build a solution, and how to design a workflow using an open-source framework like ZenML.

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

                                    More practical time-series model with BQML by ML GDE JeongMin Kwon (Korea) introduced BQML and time-series modeling and showed some practical applications with BQML ARIMA+ and Python implementations.

                                    Vertex AI Forecast - Demand Forecasting with AutoML by ML GDE Rio Kurihara (Japan) presented a time series forecast overview, time series fusion transformers, and the benefits and desired features of AutoML.

                                    Research & Ecosystem

                                    AI in Healthcare by ML GDE Sara EL-ATEIF (Morocco) introduced AI applications in healthcare and the challenges facing AI in its adoption into the health system.

                                    Women in AI APAC finished their journey at ML Paper Reading Club. During 10 weeks, participants gained knowledge on outstanding machine learning research, learned the latest techniques, and understood the notion of “ML research” among ML engineers. See their session here.

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

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

                                    Screengrab of ML GDEs Margaret Maynard-Reid and Akash Nain during Chat with ML GDE Akash
                                    Chat with ML GDE Vikram & Chat with ML GDE Aakash by ML GDE Margaret Maynard-Reid (United States) shared the stories of ML GDEs’ including how they became ML GDE and how they proceeded with their ML projects.

                                    Anatomy of Capstone ML Projects 🫀by ML GDE Sayak Paul (India) discussed working on capstone ML projects that will stay with you throughout your career. He covered various topics ranging from problem selection to tightening up the technical gotchas to presentation. And in Improving as an ML Practitioner he shared his learning from experience in the field working on several aspects.

                                    Screen grab of statement of objectives in MLOps Development Environment by ML GDE Vinicius Carida
                                    MLOps Development Environment by ML GDE Vinicius Caridá (Brazil) aims to build a full development environment where you can write your own pipelines connecting MLFLow, Airflow, GCP and Streamlit, and build amazing MLOps pipelines to practice your skills.

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

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

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

                                    More voices = More Bazel

                                    Takeaways from the BazelCon DEI lunch panel

                                    In front of a standing-room-only lunch panel, Google’s head of Developer X strategy Minu Puranik asks us, “If there is one thing you want to change [about Bazel’s DEI culture], what would it be and why?”

                                    We’d spent the last hour on three main themes: community culture, fostering trust, and growing our next generation of leaders. Moderated by Minu, our panel brought together a slate of brilliant people from underrepresented groups to give a platform to our experiences and ideas. Together with representatives and allies in the community, we explored methods for building inclusivity and sought a better understanding of the institutional and systemic barriers to increasing diversity.

                                    Culture defines how we act, which informs who feels welcome to contribute. Studies show that diverse contributor backgrounds yield more and better results, so how do we create a culture where everyone feels safe to share, ask questions, and contribute? Helen Altshuler, co-founder and CEO of EngFlow, relayed her experience regarding some best practices:

                                    “Having people that can have your back is important to get past the initial push to submit something and feeling like it’s ok. You don’t need to respond to everything in one go. Last year, Cynthia Coah and I gave a talk on how to make contributions to the Bazel community. Best practices: better beginners’ documentation, classifying GitHub issues as ‘good first issue,’ and having Slack channels where code owners can play a more active role.”

                                                        - Helen Altshuler, co-founder and CEO of EngFlow

                                    Diving further, we discussed the need to make sure new contributors get positive, actionable feedback to reward them with context and resources, and encourage them to take the risk of contributing to the codebase. This encouragement of new contributors feeds directly into the next generation of technical influencers and leaders. Eva Howe, co-founder and Legal Counsel for Aspect, addressed the current lack of diversity in the community pipeline.

                                    “I’d like to see more trainings like the Bazel Community Day. Trainings serve two purposes:

                                    1. You can blend in, start talking to someone in the background, and form connections.
                                    2. We can give a good first educational experience. It needs to be a welcoming space.”

                                                         - Eva Howe, Legal Counsel – Aspect Dev

                                      In addition to industry trainings, the audience and panel brought up bootcamps and university classes as rich sources to find and promote diversity, though they cautioned that it takes active, ongoing effort to maintain an environment that diverse candidates are willing to stay in. There are fewer opportunities to take risks as part of a historically excluded group, and the feeling that you have to succeed for everyone who looks like you creates a high-pressure environment that is worse for learning outcomes.

                                      To bypass this pipeline problem, we can recruit promising candidates and sponsor them through getting the necessary experience on the job. Lyra Levin, Bazel’s internal technical writer at Google, spoke to this process of incentivizing and recognizing contributions outside the codebase, as a way to both encourage necessary glue work, and pull people into tech from parallel careers more hospitable to underrepresented candidates. And Sophia Vargas, Program Manager in Google’s OSPO (Open Source Programs Office), also offered insight regarding contributions.

                                      “If someone gives you an introduction to another person, recognize that. Knowing a system of people is work. Knowing where to find answers is work. Saying I’m going to be available and responding to emails is work. If you see a conversation where someone is getting unhelpful pushback, jump in and moderate it. Reward those who contribute by creating a space that can be collaborative and supportive.”

                                                           - Lyra Levin, Technical Writer

                                      “Create ways to recognize non-code contributions. One example is a markdown file describing other forms of contribution, especially in cases that do not generate activity attached to a name on GitHub.”

                                      An audience member agreed that for the few PRs a positive experience is critical for community trust building: And indeed, open source is all about building trust. So how do we go about building trust? What should we do differently? Radhika Advani, Bazel’s product manager at Google, suggests that the key is to:

                                      “Make some amazing allies. Be kind and engage with empathy. Take your chances—there are lots of good people out there. You have to come from a place of vulnerability.”

                                                          - Radhika Advani, Bazel Product Manager

                                      Vargas also added some ideas for how to be an “amazing ally” and sponsor the careers of those around you, such as creating safe spaces to have these conversations because not everyone is bold enough to speak up or to ask for support since raising issues in a public forum can be intimidating. Making yourself accessible and providing anonymous forms for suggestions or feedback can serve as opportunities to educate yourself and to increase awareness of diverging opinions.

                                      An audience member stated that recognizing an action that is alienating to a member of your group—even just acknowledging their experience or saying something to the room—can be very powerful to create a sense of safety and belonging. And another said that those in leadership positions being forthright about the limits of their knowledge, gives people the freedom to not know everything.

                                      So to Minu’s question, what should we do to improve Bazel’s culture?

                                      Helen: Create a governance group on Slack to ensure posts are complying with the community code of conduct guidelines. Review how this is managed for other OSS communities.

                                      Sophia: Institutionalize mentorship; have someone else review what you’ve done and give you the confidence to push a change. Nurture people. We need to connect new and established members of the community.

                                      Lyra: Recruit people in parallel careers paths with higher representation. Give them sponsorship to transition to tech.

                                      Radhika: Be more inclusive. All the jargon can get overwhelming, so let’s consider how we can make things simpler, including with non-technical metaphors.

                                      Eva: Consider what each of us can do to make the experience for people onboarding better.

                                      There are more ways to be a Bazel contributor than raising PRs. Being courageous, vulnerable and open contributes to the culture that creates the code. Maintainers: practice empathy and remember the human on the other side of the screen. Be a coach and a mentor, knowing that you are opening the door for more people to build the product you love, with you. Developers: be brave and see the opportunities to accept sponsorship into the space. Bazel is for everyone.

                                      By Lyra Levin, Minu Puranik, Keerthana Kumar, Radhika Advani, and Sophia Vargas – Bazel Panel