The document provides an overview of a presentation about Google Cloud developer tools and an easier path to machine learning. It introduces the speaker and their background and experience. It then outlines the agenda which includes introductions to machine learning and Google Cloud, Google APIs, Cloud ML APIs, and other APIs to consider. It provides examples of using various Cloud ML APIs like Vision, Natural Language, and Speech for tasks like image labeling, text analysis, and speech recognition. The goal is to demonstrate how APIs powered by machine learning can help ease the burden of learning machine learning by allowing users to leverage pre-built models if they can call APIs.
Exploring Google (Cloud) APIs with Python & JavaScriptwesley chun
Half-hour tech talk given at user groups or technical conferences to introducing developers to integrating with Google (Cloud) APIs from Python or JavaScript.
ABSTRACT
Want to integrate Google technologies into the web+mobile apps that you build? Google has various open source libraries & developer tools that help you do exactly that. Users who have run into roadblocks like authentication or found our APIs confusing/challenging, are welcome to come and make these non-issues moving forward. Learn how to leverage the power of Google technologies in the next apps you build!!
Easy path to machine learning (2023-2024)wesley chun
1-hr tech talk introducing Machine Learning and the GCP ML APIs and other Google Cloud developer tools to a technical audience:
Easier onramp to getting into AI/ML by using GCP AI/ML APIs (Vision, Video Intelligence, Natural Language, Speech-to-Text, Text-to-Speech, Translation) backed by single-task pre-trained models found in Vertex AI, AutoML for finetuning those pre-trained models, and other "friends of AI/ML" Google dev tools & platforms that can help: BigQuery (data warehouse & analysis), Cloud SQL+AlloyDB & Firestore (SQL & NoSQL databases), serverless platforms (App Engine, Cloud Functions, Cloud Run), and introducing the Gemini API (from both Google AI and GCP Vertex AI)
This is an inspirational lightning talk on how developers can take on the future with Google Cloud and other non-Cloud Google tools. It presents various application ideas that are meant to both inspire what's possible as well as show what some of those tools could be.
Introduction to Cloud Computing with Google Cloudwesley chun
This is a 20-30 minute technical talk introducing developers to cloud computing including an overview of Google Cloud computing products. There is a special focus on serverless tools as a convenient way for developers to run code. The talk ends with several inspirational apps showcasing what is possible with Google Cloud tools meant to plant a seed as to consider what is possible.
Exploring Google (Cloud) APIs with Python & JavaScriptwesley chun
Half-hour tech talk given at user groups or technical conferences to introducing developers to integrating with Google (Cloud) APIs from Python or JavaScript.
ABSTRACT
Want to integrate Google technologies into the web+mobile apps that you build? Google has various open source libraries & developer tools that help you do exactly that. Users who have run into roadblocks like authentication or found our APIs confusing/challenging, are welcome to come and make these non-issues moving forward. Learn how to leverage the power of Google technologies in the next apps you build!!
Easy path to machine learning (2023-2024)wesley chun
1-hr tech talk introducing Machine Learning and the GCP ML APIs and other Google Cloud developer tools to a technical audience:
Easier onramp to getting into AI/ML by using GCP AI/ML APIs (Vision, Video Intelligence, Natural Language, Speech-to-Text, Text-to-Speech, Translation) backed by single-task pre-trained models found in Vertex AI, AutoML for finetuning those pre-trained models, and other "friends of AI/ML" Google dev tools & platforms that can help: BigQuery (data warehouse & analysis), Cloud SQL+AlloyDB & Firestore (SQL & NoSQL databases), serverless platforms (App Engine, Cloud Functions, Cloud Run), and introducing the Gemini API (from both Google AI and GCP Vertex AI)
This is an inspirational lightning talk on how developers can take on the future with Google Cloud and other non-Cloud Google tools. It presents various application ideas that are meant to both inspire what's possible as well as show what some of those tools could be.
Introduction to Cloud Computing with Google Cloudwesley chun
This is a 20-30 minute technical talk introducing developers to cloud computing including an overview of Google Cloud computing products. There is a special focus on serverless tools as a convenient way for developers to run code. The talk ends with several inspirational apps showcasing what is possible with Google Cloud tools meant to plant a seed as to consider what is possible.
Being able to build, train, and deploy your own machine learning models requires a level of math & sophistication that not everyone has. In an effort to "democratize" ML, Google has made several of its pre-trained models available via APIs. This 15-20 minute talk introduces developers to 6 of those APIs, providing a short demo app for each one.
Analytics Zoo: Building Analytics and AI Pipeline for Apache Spark and BigDL ...Databricks
A long time ago, there was Caffe and Theano, then came Torch and CNTK and Tensorflow, Keras and MXNet and Pytorch and Caffe2….a sea of Deep learning tools but none for Spark developers to dip into. Finally, there was BigDL, a deep learning library for Apache Spark. While BigDL is integrated into Spark and extends its capabilities to address the challenges of Big Data developers, will a library alone be enough to simplify and accelerate the deployment of ML/DL workloads on production clusters? From high level pipeline API support to feature transformers to pre-defined models and reference use cases, a rich repository of easy to use tools are now available with the ‘Analytics Zoo’. We’ll unpack the production challenges and opportunities with ML/DL on Spark and what the Zoo can do
This is a 15-20 minute tech talk designed for those who wish to use Google APIs, but don't know how to get started quickly. This session introduces developers to two distinct ways of accessing our APIs, the standard HTTP-based REST APIs or Google Apps Script, a customized JS environment which provides Google API access via objects. It concludes with some inspirational app ideas of what you can build using Google Cloud APIs (covering both GCP & G Suite).
Challenges of Deep Learning in Computer Vision Webinar - Tessellate ImagingAdhesh Shrivastava
Slides from the webinar on Challenges of Deep Learning in Computer Vision presented by Tessellate Imaging and powered by E2E Networks.
The webinar discusses the growth and applications of Computer Vision in modern-day real life. Challenges with implementing and developing Deep Learning and Computer Vision projects for both enterprises and developers.
We introduce MonkAI (https://monkai.org) an Open Sourced Deep Learning wrapper library for Computer Vision development and talk about features tackling some of the challenges in Deep Learning.
Exploring Google (Cloud) APIs & Cloud Computing overviewwesley chun
This is a 100-minute tech talk designed for developers to give a comprehensive overview of using Google APIs, primarily those from Google Cloud (G Suite and Google Cloud Platform)
Amazon SageMaker is a fully-managed platform that lets developers and data scientists build and scale machine learning solutions. First, we'll show you how SageMaker Ground Truth helps you label large training datasets. Then, using Jupyter notebooks, we'll show you how to build, train and deploy models using built-in algorithms and frameworks (TensorFlow, Apache MXNet, etc). Finally, we'll show you how to use 3rd-party models from the AWS marketplace.
While the adoption of machine learning and deep learning techniques continue to grow, many organizations find it difficult to actually deploy these sophisticated models into production. It is common to see data scientists build powerful models, yet these models are not deployed because of the complexity of the technology used or lack of understanding related to the process of pushing these models into production.
As part of this talk, I will review several deployment design patterns for both real-time and batch use cases. I’ll show how these models can be deployed as scalable, distributed deployments within the cloud, scaled across hadoop clusters, as APIs, and deployed within streaming analytics pipelines. I will also touch on topics related to security, end-to-end governance, pitfalls, challenges, and useful tools across a variety of platforms. This presentation will involve demos and sample code for the the deployment design patterns.
How Google Cloud Platform can help in the classroom/labwesley chun
This is a 90-min tech talk along with hands-on exercises gives a comprehensive, vendor-agnostic overview of cloud computing, primarily targeting educators in the higher education market but is open to any developer. This is followed by an introduction to products in Google Cloud Platform, focusing on its serverless and machine learning products. .
Der Erfolg einer App hängt maßgeblich davon ab, wie sie sich dem Nutzer präsentiert. der Vortrag beleuchtet die Möglichkeiten von Android, außergewöhnliche Custom-Widgets, 3-D-Animationen und grafische Effekte aufzuwerten. Der Vortrag enthält jede Menge Beispielcode, Performancetipps und Best Practices.
Deeper into ARKit with CoreML and Turi CreateSoojin Ro
Have you ever tried to make something cool and fun with ARKit, only to find out there is a missing piece? Then this talk is for you. I struggled to make my first AR app (Notable Me), but CoreML and Turi Create was there for me. This framework and tool allowed me to create something I never knew I could make.
I will share all the lessons I learned from developing this app, focusing on how to utilize machine learning into an ARKit app. Also how to unlock hidden features of Turi Create, Apple’s Open Source tool for easily creating custom ML models, to drastically improve the quality.
Monitoring AI applications with AI
The best performing offline algorithm can lose in production. The most accurate model does not always improve business metrics. Environment misconfiguration or upstream data pipeline inconsistency can silently kill the model performance. Neither prodops, data science or engineering teams are skilled to detect, monitor and debug such types of incidents.
Was it possible for Microsoft to test Tay chatbot in advance and then monitor and adjust it continuously in production to prevent its unexpected behaviour? Real mission critical AI systems require advanced monitoring and testing ecosystem which enables continuous and reliable delivery of machine learning models and data pipelines into production. Common production incidents include:
Data drifts, new data, wrong features
Vulnerability issues, malicious users
Concept drifts
Model Degradation
Biased Training set / training issue
Performance issue
In this demo based talk we discuss a solution, tooling and architecture that allows machine learning engineer to be involved in delivery phase and take ownership over deployment and monitoring of machine learning pipelines.
It allows data scientists to safely deploy early results as end-to-end AI applications in a self serve mode without assistance from engineering and operations teams. It shifts experimentation and even training phases from offline datasets to live production and closes a feedback loop between research and production.
Technical part of the talk will cover the following topics:
Automatic Data Profiling
Anomaly Detection
Clustering of inputs and outputs of the model
A/B Testing
Service Mesh, Envoy Proxy, trafic shadowing
Stateless and stateful models
Monitoring of regression, classification and prediction models
Data Summer Conf 2018, “Monitoring AI with AI (RUS)” — Stepan Pushkarev, CTO ...Provectus
In this demo based talk we discuss a solution, tooling and architecture that allows machine learning engineer to be involved in delivery phase and take ownership over deployment and monitoring of machine learning pipelines. It allows data scientists to safely deploy early results as end-to-end AI applications in a self serve mode without assistance from engineering and operations teams. It shifts experimentation and even training phases from offline datasets to live production and closes a feedback loop between research and production.
Slidedeck for my session on Insider Dev Tour 2019 (Lisbon Jul 29th).
Mostly based on tools and platform support for AI workloads and the options for edge computing and cloud computing.
ML.NET, WinML, DirectML, Model Builder, Azure Cognitive Services, ...
Creating a custom ML model for your application - DevFest Lima 2019Isabel Palomar
Aprenderás como puede ser creado un modelo de Machine Learning que puedas implementar en tus aplicaciones. Iré mostrando cada uno de los pasos que se tienen que seguir, los tipos de problemas que se pueden resolver, los datos que necesitas para que funcione y por último, las opciones para realizar la implementación de nuestro modelo en nuestras aplicaciones.
Powerful Google developer tools for immediate impact! (2023-24 C)wesley chun
This presentations targets students or working professionals. You may know Google for search, YouTube, Android, Chrome, and Gmail, but did you know Google has many developer tools, platforms & APIs? This comprehensive yet still high-level overview outlines the most impactful tools for where to run your code, store & analyze your data. It will also inspire you as to what's possible. This talk is 50 minutes in length.
Being able to build, train, and deploy your own machine learning models requires a level of math & sophistication that not everyone has. In an effort to "democratize" ML, Google has made several of its pre-trained models available via APIs. This 15-20 minute talk introduces developers to 6 of those APIs, providing a short demo app for each one.
Analytics Zoo: Building Analytics and AI Pipeline for Apache Spark and BigDL ...Databricks
A long time ago, there was Caffe and Theano, then came Torch and CNTK and Tensorflow, Keras and MXNet and Pytorch and Caffe2….a sea of Deep learning tools but none for Spark developers to dip into. Finally, there was BigDL, a deep learning library for Apache Spark. While BigDL is integrated into Spark and extends its capabilities to address the challenges of Big Data developers, will a library alone be enough to simplify and accelerate the deployment of ML/DL workloads on production clusters? From high level pipeline API support to feature transformers to pre-defined models and reference use cases, a rich repository of easy to use tools are now available with the ‘Analytics Zoo’. We’ll unpack the production challenges and opportunities with ML/DL on Spark and what the Zoo can do
This is a 15-20 minute tech talk designed for those who wish to use Google APIs, but don't know how to get started quickly. This session introduces developers to two distinct ways of accessing our APIs, the standard HTTP-based REST APIs or Google Apps Script, a customized JS environment which provides Google API access via objects. It concludes with some inspirational app ideas of what you can build using Google Cloud APIs (covering both GCP & G Suite).
Challenges of Deep Learning in Computer Vision Webinar - Tessellate ImagingAdhesh Shrivastava
Slides from the webinar on Challenges of Deep Learning in Computer Vision presented by Tessellate Imaging and powered by E2E Networks.
The webinar discusses the growth and applications of Computer Vision in modern-day real life. Challenges with implementing and developing Deep Learning and Computer Vision projects for both enterprises and developers.
We introduce MonkAI (https://monkai.org) an Open Sourced Deep Learning wrapper library for Computer Vision development and talk about features tackling some of the challenges in Deep Learning.
Exploring Google (Cloud) APIs & Cloud Computing overviewwesley chun
This is a 100-minute tech talk designed for developers to give a comprehensive overview of using Google APIs, primarily those from Google Cloud (G Suite and Google Cloud Platform)
Amazon SageMaker is a fully-managed platform that lets developers and data scientists build and scale machine learning solutions. First, we'll show you how SageMaker Ground Truth helps you label large training datasets. Then, using Jupyter notebooks, we'll show you how to build, train and deploy models using built-in algorithms and frameworks (TensorFlow, Apache MXNet, etc). Finally, we'll show you how to use 3rd-party models from the AWS marketplace.
While the adoption of machine learning and deep learning techniques continue to grow, many organizations find it difficult to actually deploy these sophisticated models into production. It is common to see data scientists build powerful models, yet these models are not deployed because of the complexity of the technology used or lack of understanding related to the process of pushing these models into production.
As part of this talk, I will review several deployment design patterns for both real-time and batch use cases. I’ll show how these models can be deployed as scalable, distributed deployments within the cloud, scaled across hadoop clusters, as APIs, and deployed within streaming analytics pipelines. I will also touch on topics related to security, end-to-end governance, pitfalls, challenges, and useful tools across a variety of platforms. This presentation will involve demos and sample code for the the deployment design patterns.
How Google Cloud Platform can help in the classroom/labwesley chun
This is a 90-min tech talk along with hands-on exercises gives a comprehensive, vendor-agnostic overview of cloud computing, primarily targeting educators in the higher education market but is open to any developer. This is followed by an introduction to products in Google Cloud Platform, focusing on its serverless and machine learning products. .
Der Erfolg einer App hängt maßgeblich davon ab, wie sie sich dem Nutzer präsentiert. der Vortrag beleuchtet die Möglichkeiten von Android, außergewöhnliche Custom-Widgets, 3-D-Animationen und grafische Effekte aufzuwerten. Der Vortrag enthält jede Menge Beispielcode, Performancetipps und Best Practices.
Deeper into ARKit with CoreML and Turi CreateSoojin Ro
Have you ever tried to make something cool and fun with ARKit, only to find out there is a missing piece? Then this talk is for you. I struggled to make my first AR app (Notable Me), but CoreML and Turi Create was there for me. This framework and tool allowed me to create something I never knew I could make.
I will share all the lessons I learned from developing this app, focusing on how to utilize machine learning into an ARKit app. Also how to unlock hidden features of Turi Create, Apple’s Open Source tool for easily creating custom ML models, to drastically improve the quality.
Monitoring AI applications with AI
The best performing offline algorithm can lose in production. The most accurate model does not always improve business metrics. Environment misconfiguration or upstream data pipeline inconsistency can silently kill the model performance. Neither prodops, data science or engineering teams are skilled to detect, monitor and debug such types of incidents.
Was it possible for Microsoft to test Tay chatbot in advance and then monitor and adjust it continuously in production to prevent its unexpected behaviour? Real mission critical AI systems require advanced monitoring and testing ecosystem which enables continuous and reliable delivery of machine learning models and data pipelines into production. Common production incidents include:
Data drifts, new data, wrong features
Vulnerability issues, malicious users
Concept drifts
Model Degradation
Biased Training set / training issue
Performance issue
In this demo based talk we discuss a solution, tooling and architecture that allows machine learning engineer to be involved in delivery phase and take ownership over deployment and monitoring of machine learning pipelines.
It allows data scientists to safely deploy early results as end-to-end AI applications in a self serve mode without assistance from engineering and operations teams. It shifts experimentation and even training phases from offline datasets to live production and closes a feedback loop between research and production.
Technical part of the talk will cover the following topics:
Automatic Data Profiling
Anomaly Detection
Clustering of inputs and outputs of the model
A/B Testing
Service Mesh, Envoy Proxy, trafic shadowing
Stateless and stateful models
Monitoring of regression, classification and prediction models
Data Summer Conf 2018, “Monitoring AI with AI (RUS)” — Stepan Pushkarev, CTO ...Provectus
In this demo based talk we discuss a solution, tooling and architecture that allows machine learning engineer to be involved in delivery phase and take ownership over deployment and monitoring of machine learning pipelines. It allows data scientists to safely deploy early results as end-to-end AI applications in a self serve mode without assistance from engineering and operations teams. It shifts experimentation and even training phases from offline datasets to live production and closes a feedback loop between research and production.
Slidedeck for my session on Insider Dev Tour 2019 (Lisbon Jul 29th).
Mostly based on tools and platform support for AI workloads and the options for edge computing and cloud computing.
ML.NET, WinML, DirectML, Model Builder, Azure Cognitive Services, ...
Creating a custom ML model for your application - DevFest Lima 2019Isabel Palomar
Aprenderás como puede ser creado un modelo de Machine Learning que puedas implementar en tus aplicaciones. Iré mostrando cada uno de los pasos que se tienen que seguir, los tipos de problemas que se pueden resolver, los datos que necesitas para que funcione y por último, las opciones para realizar la implementación de nuestro modelo en nuestras aplicaciones.
Powerful Google developer tools for immediate impact! (2023-24 C)wesley chun
This presentations targets students or working professionals. You may know Google for search, YouTube, Android, Chrome, and Gmail, but did you know Google has many developer tools, platforms & APIs? This comprehensive yet still high-level overview outlines the most impactful tools for where to run your code, store & analyze your data. It will also inspire you as to what's possible. This talk is 50 minutes in length.
Powerful Google developer tools for immediate impact! (2023-24 B)wesley chun
This is one of two presentations to students or working professionals. You may know Google for search, YouTube, Android, Chrome, and Gmail, but did you know Google has many other cloud services? In this comprehensive yet still high-level overview of Google Cloud tools & APIs with the purpose of inspiring you as to what's possible. The session introduces Google's serverless platforms and machine learning & other APIs, tools that have an immediate impact on projects, alleviating the need to think about computing infrastructure as well as dispensing with the need to have machine learning expertise. We'll wrap up w/online resources like videos & hands-on tutorials to get you started! The main takeaways are where to run your code, store your data, and analyze your data, all in the cloud!
This talk is 1-hr in length.
The other version of this talk ("A") is an 45-mins long and focuses more on APIs platforms.
30-45-min tech talk given at user groups or technical conferences to introducing developers to integrating with Google APIs from Python .
ABSTRACT
Want to integrate Google technologies into the web+mobile apps that you build? Google has various open source libraries & developer tools that help you do exactly that. Users who have run into roadblocks like authentication or found our APIs confusing/challenging, are welcome to come and make these non-issues moving forward. Learn how to leverage the power of Google technologies in the next apps you build!!
Serverless computing with Google Cloud (2023-24)wesley chun
This is a half-hour technical talk on serverless computing with Google Cloud (Platform). It starts with a review of all of cloud computing then dives into serverless computing, demonstrates multiple products, and shows inspirational examples of apps built using these technologies.
Powerful Google developer tools for immediate impact! (2023-24 A)wesley chun
This is one of two 45-60-min presentations to students or working professionals. You may know Google for search, YouTube, Android, Chrome, and Gmail, but did you know Google has many other cloud services? In this comprehensive yet still high-level overview of Google Cloud tools & APIs with the purpose of inspiring you as to what's possible. The session introduces Google's machine learning & other APIs, tools that have an immediate impact on projects, alleviating the need to think about computing infrastructure as well as dispensing with the need to have machine learning expertise. We'll wrap up w/online resources like videos & hands-on tutorials to get you started! The main takeaways are where to run your code, store your data, and analyze your data, all in the cloud!
The other version of this talk ("B") focuses more on serverless platforms.
Build an AI/ML-driven image archive processing workflow: Image archive, analy...wesley chun
Google provides a diverse array of services to realize the ambition of solving real business problems, like constrained resources. An image archive & analysis plus report generation use-case can be realized with just GWS (Google Workspace) & GCP (Google Cloud) APIs. The principle of mixing-and-matching Google technologies is applicable to many other challenges faced by you, your organization, or your customers. These slides are from the half-hour presentation about this case study.
Exploring Google APIs 102: Cloud vs. non-GCP Google APIswesley chun
As a follow-up to his "Exploring Google APIs" talk in 2019 (https://www.youtube.com/watch?v=ri8Bfptgo9Q) on Google APIs and running code on Google Cloud, tech consultant Wesley Chun dives deeper into using the REST APIs available for many Google services, Cloud and otherwise. While developers should expect a common user experience across all Google APIs, this isn't the case, so Wesley, who has spent 13+ years working on different Google API teams, will walk you through the differences you need to know if any of your current or future projects plan on using any Google API, esp. Cloud vs. non-GCP Google APIs. Two of the key topics in this session include an overview of the different client libraries available as well as what's required for authorizing your app's access to Google APIs. Knowledge of accessing APIs from Python or Javascript may be helpful but not necessary.
This is a 45-min technical talk on serverless computing with Python featuring products from the Google Cloud. It starts with a review of all of cloud computing then dives into serverless computing, demonstrates multiple products, then shows inspirational examples of apps built using these technologies.
You may know Google for search, YouTube, Android, Chrome, and Gmail, but that's only as an end-user of OUR apps. Did you know you can also integrate Google technologies into YOUR apps? We have many APIs and open source libraries that help you do that! If you have tried and found it challenging, didn't find not enough examples, run into roadblocks, got confused, or just curious about what Google APIs can offer, join us to resolve any blockers. Code samples will be in Python and/or Node.js/JavaScript. This session focuses on showing you how to access Google Cloud APIs from one of Google Cloud's compute platforms, whether serverless or otherwise.
This is a one hour technical talk by @wescpy on serverless computing with Google Cloud (Platform). It starts with a review of all of cloud computing then dives into serverless computing, demonstrates multiple products, and shows inspirational examples of apps built using these technologies. There is a bonus section covering serverless in-practice featuring how to think about app development, common use cases, flexibility, best practices, and local dev & testing.
This is a one hour technical talk on serverless computing with Google Cloud (Platform). It starts with a review of all of cloud computing then dives into serverless computing, demonstrates multiple products, and shows inspirational examples of apps built using these technologies.
Designing flexible apps deployable to App Engine, Cloud Functions, or Cloud Runwesley chun
Many people ask, "Which one is better for me: App Engine, Cloud Functions, or Cloud Run?" To help you learn more about them, understand their differences, appropriate use cases, etc., why not deploy the same app to all 3? With this "test drive," you only need to make minor config changes between platforms. You'll also learn one of Google Cloud's AI/ML "building block" APIs as a bonus as the sample app is a simple "mini" Google Translate "MVP". This is a 45- 60-minute talk that reviews the Google Cloud serverless compute platforms then walks through the same app and its deployments. The code is maintained at https://github.com/googlecodelabs/cloud-nebulous-serverless-python
Image archive, analysis & report generation with Google Cloudwesley chun
Google Cloud provides a diverse array of services to realize the ambition of solving real business problems, like constrained resources. An image archive & analysis plus report generation use-case can be realized with just Google Workspace & GCP APIs. The principle of mixing-and-matching Google technologies is applicable to many other challenges faced by you, your organization, or your customers. These slides are from a half- to 1-hour presentation about this case study.
Half-hour tech talk given at user groups or technical conferences to introducing developers to integrating with Google (Cloud) APIs from Python .
ABSTRACT
Want to integrate Google technologies into the web+mobile apps that you build? Google has various open source libraries & developer tools that help you do exactly that. Users who have run into roadblocks like authentication or found our APIs confusing/challenging, are welcome to come and make these non-issues moving forward. Learn how to leverage the power of Google technologies in the next apps you build!!
This is a half-hour technical talk on serverless computing with Google Cloud (Platform). It starts with a review of all of cloud computing then dives into serverless computing, demonstrates multiple products, and shows inspirational examples of apps built using these technologies.
Run your code serverlessly on Google's open cloudwesley chun
This is a half-hour technical seminar on Google support of the open source ecosystem, a quick high-level overview/review of cloud computing in general, and then focuses on serverless compute products in Google Cloud and how the platforms are more open than ever!
This is a half-hour technical talk on serverless computing with Python featuring products from the Google Cloud Platform. It starts with a review of all of cloud computing then dives into serverless computing, demonstrates multiple products, then shows inspirational examples of apps built using these technologies.
Hackathon opening ceremony 2-5 minute lightning talk introducing Google Cloud tools that students can use for their hacks, whetting their appetites for a more detailed longer tech talk later.
Powerful Google Cloud tools for your hack (2020)wesley chun
You may know Google for search, YouTube, Android, Chrome, and Gmail, but did you know Google has many other cloud services? This session takes hackathon participants on a deeper dive from the opening ceremony lightning intro. In this comprehensive yet still high-level overview of Google Cloud tools & APIs with the purpose of inspiring students for their hacks. We'll look closely at our serverless platforms & machine learning APIs, tools that have an immediate impact on projects, alleviating the need to think about computing infrastructure as well as dispensing with the need to have machine learning expertise. We'll wrap up w/online resources like videos & hands-on tutorials to get you started so you'll know what to do with those Cloud credits you got from MLH!
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Le nuove frontiere dell'AI nell'RPA con UiPath Autopilot™UiPathCommunity
In questo evento online gratuito, organizzato dalla Community Italiana di UiPath, potrai esplorare le nuove funzionalità di Autopilot, il tool che integra l'Intelligenza Artificiale nei processi di sviluppo e utilizzo delle Automazioni.
📕 Vedremo insieme alcuni esempi dell'utilizzo di Autopilot in diversi tool della Suite UiPath:
Autopilot per Studio Web
Autopilot per Studio
Autopilot per Apps
Clipboard AI
GenAI applicata alla Document Understanding
👨🏫👨💻 Speakers:
Stefano Negro, UiPath MVPx3, RPA Tech Lead @ BSP Consultant
Flavio Martinelli, UiPath MVP 2023, Technical Account Manager @UiPath
Andrei Tasca, RPA Solutions Team Lead @NTT Data
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...
Easy path to machine learning
1. Google Cloud developer tools + an
Easyier path to machine learning
Wesley Chun
Developer Advocate, Google
G Suite Dev Show
goo.gl/JpBQ40
About the speaker (not a data scientist!)
Developer Advocate, Google Cloud
● Mission: enable current and future
developers everywhere to be
successful using Google Cloud and
other Google developer tools & APIs
● Videos: host of the G Suite Dev Show
on YouTube
● Blogs: developers.googleblog.com &
gsuite-developers.googleblog.com
● Twitters: @wescpy, @GoogleDevs,
@GSuiteDevs
Previous experience / background
● Software engineer & architect for 20+ years
● One of the original Yahoo!Mail engineers
● Author of bestselling "Core Python" books
(corepython.com)
● Technical trainer, teacher, instructor since
1983 (Computer Science, C, Linux, Python)
● Fellow of the Python Software Foundation
● AB (Math/CS) & CMP (Music/Piano), UC
Berkeley and MSCS, UC Santa Barbara
● Adjunct Computer Science Faculty, Foothill
College (Silicon Valley)
2. Why and Agenda
● Big data is everywhere now
● Need the power of AI to help analyze
● Requires certain level of math/statistics background
● AI/ML has somewhat steep learning curve
● APIs powered by ML helps ease this burden
● If you can call APIs, you can use ML!
1
Intro to machine
learning
2
Intro to Google
Cloud
3
Google APIs
4
Cloud ML APIs
5
Other APIs to
consider
6
All of Cloud
(inspiration)
7
Summary &
wrap-up
What is machine learning?
AI, ML, and making computers smarter; to help us
understand more and get more insights than before
1
3. AI
Make code solve
problems commonly
associated with
human intelligence
ML
Make code learn
from experience
instead of explicit
programming
DL
ML using deep neural
networks… make
code learn to be
even better/smarter
4. AI & Machine Learning
Puppy or muffin?
Machine learning is learning
from rules plus experience.
8. How to get started
Enough talk, let's think about first steps
Lots of data
Complex mathematics in
multidimensional spaces
Magical results
Popular imagination of what Machine Learning is
9. Organize data
Use machines to
flesh out the
model from data
Collect
data
Create model
Deploy fleshed
out model
In reality what ML is
Rules
Data
Traditional
Programming
Answers
Answers
Data
RulesMachine
Learning
10. Fashion MNIST
● 70k grayscale images
○ 60k training set
○ 10k testing set
● 10 categories
● Images: 28x28 pixels
● Go train a neural net!
tensorflow.org/tutorials/
keras/classification
11. import tensorflow as tf
from tensorflow import keras
fashion_mnist = keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()
09 09 = ankle boot;
踝靴;
アンクルブーツ;
Bróg rúitín
Training Phase
Answers
Data
Rules/modelMachine
Learning
Model
Data Predictions
Inference Phase
12. Your steps
1. Import MNIST dataset
2. Explore/preprocess data
3. Build model
a. Setup layers
b. Compile model
4. Train model
5. Evaluate accuracy
6. Make predictions
7. (Have fun!)
2 Introduction to
Google Cloud
GCP and G Suite tools & APIs
13. GCP Machine Learning APIs
● Gain insights from data using GCP's
pre-trained machine learning models
● Leverage the same technology as
Google Translate, Photos, and Assistant
● Requires ZERO prior knowledge of ML
● If you can call an API, you can use AI/ML!
Vision Video
Intelligence
Speech
(S2T & T2S)
Natural
Language
Translation
14. Full Spectrum of AI & ML Offerings
App developer
Data Scientist
Data Scientist,
Researcher w/access to
infrastructure, GPUs...
Use pre-built models
Use/extend OSS SDK,
build models, manage
training infrastructure
ML Engine
Auto ML
Build custom models,
use/extend OSS SDK
ML APIs
App developer,
data scientist
Use/customize pre-built
models
3
Google (REST) APIs
What are they? How do you use them?
15. Cloud/GCP console
console.cloud.google.com
● Hub of all developer activity
● Applications == projects
○ New project for new apps
○ Projects have a billing acct
● Manage billing accounts
○ Financial instrument required
○ Personal or corporate credit cards,
Free Trial, and education grants
● Access GCP product settings
● Manage users & security
● Manage APIs in devconsole
● View application statistics
● En-/disable Google APIs
● Obtain application credentials
Using Google APIs
goo.gl/RbyTFD
API manager aka Developers Console (devconsole)
console.developers.google.com
17. Machine Learning: Cloud Vision
Google Cloud Vision API
cloud
labeling = VISION.images().annotate(body=body).execute().get('responses')
for labels in labeling:
if 'labelAnnotations' in labels:
print('** Labels detected (and confidence score):')
for label in labels['labelAnnotations']:
print(('%.2f%%' % (
label['score']*100.)).ljust(10), label['description'])
if 'faceAnnotations' in labels:
print('n** Facial features detected (and likelihood):')
for label, value in labels['faceAnnotations'][0].items():
if label.endswith('Likelihood'):
print(label.split('Likelihood')[0].ljust(16),
value.lower().replace('_', ' '))
Vision: image analysis & metadata extraction
18. $ python viz_demo.py
** Labels detected (and confidence score):
89.94% Sitting
86.09% Interior design
82.08% Furniture
81.52% Table
80.85% Room
79.04% White-collar worker
76.19% Office
68.18% Conversation
60.96% Window
60.07% Desk
** Facial features detected (and likelihood):
anger very unlikely
joy very likely
underExposed very unlikely
sorrow very unlikely
surprise very unlikely
headwear very unlikely
blurred very unlikely
Vision: image analysis & metadata extraction
Higher-level GCP SDK & API client libraries
1. Bad news: Just showed you the "harder
way" of using Google Cloud Platform APIs
2. Good news: it's even easier with the GCP
SDK and higher-level client libraries
3. Why (not)? Not all Google APIs have high-
level client libraries. Lower-level serves as
"LCD" for accessing more Google APIs
cloud.google.com/sdk
cloud.google.com/apis/docs
19. from google.cloud import vision
image_uri = 'gs://cloud-samples-data/vision/using_curl/shanghai.jpeg'
client = vision.ImageAnnotatorClient()
image = vision.types.Image()
image.source.image_uri = image_uri
response = client.label_detection(image=image)
print('Labels (and confidence score):')
print('=' * 30)
for label in response.label_annotations:
print(f'{label.description} ({label.score*100.:.2f}%)')
Vision: label annotation/object detection
$ python3 label-detect.py
Labels (and confidence score):
==============================
People (95.05%)
Street (89.12%)
Mode of transport (89.09%)
Transport (85.13%)
Vehicle (84.69%)
Snapshot (84.11%)
Urban area (80.29%)
Infrastructure (73.14%)
Road (72.74%)
Pedestrian (68.90%)
Vision: label annotation/object detection
codelabs.developers.google.com/codelabs/cloud-vision-api-python#6
20. from google.cloud import vision
image_uri = 'gs://cloud-vision-codelab/otter_crossing.jpg'
client = vision.ImageAnnotatorClient()
image = vision.types.Image()
image.source.image_uri = image_uri
response = client.text_detection(image=image)
for text in response.text_annotations:
print('=' * 30)
print(f'"{text.description}"')
vertices = [f'({v.x},{v.y})' for v in text.bounding_poly.vertices]
print(f'bounds: {",".join(vertices)}')
Vision: OCR, text detection/extraction
$ python3 text-detect.py
==============================
"CAUTION
Otters crossing
for next 6 miles
"
bounds: (61,243),(251,243),(251,340),(61,340)
==============================
"CAUTION"
bounds: (75,245),(235,243),(235,269),(75,271)
==============================
"Otters"
bounds: (65,296),(140,297),(140,315),(65,314)
==============================
"crossing"
bounds: (151,294),(247,295),(247,317),(151,316)
:
Vision: OCR, text detection/extraction
codelabs.developers.google.com/codelabs/cloud-vision-api-python#7
23. Simple sentiment & classification analysis
TEXT = '''Google, headquartered in Mountain View, unveiled the new
Android phone at the Consumer Electronics Show. Sundar Pichai said
in his keynote that users love their new Android phones.'''
print('TEXT:', TEXT)
data = {'type': 'PLAIN_TEXT', 'content': TEXT}
NL = discovery.build('language', 'v1', developerKey=API_KEY)
# sentiment analysis
sent = NL.documents().analyzeSentiment(
body={'document': data}).execute().get('documentSentiment')
print('nSENTIMENT: score (%s), magnitude (%s)' % (sent['score'], sent['magnitude']))
# content classification
print('nCATEGORIES:')
cats = NL.documents().classifyText(body={'document': data}).execute().get('categories')
for cat in cats:
print('* %s (%s)' % (cat['name'][1:], cat['confidence']))
Simple sentiment & classification analysis
$ python nl_sent_simple.py
TEXT: Google, headquartered in Mountain View, unveiled the new Android
phone at the Consumer Electronics Show. Sundar Pichai said in
his keynote that users love their new Android phones.
SENTIMENT: score (0.3), magnitude (0.6)
CATEGORIES:
* Internet & Telecom (0.76)
* Computers & Electronics (0.64)
* News (0.56)
24. Machine Learning: Cloud Speech
Google Cloud Speech APIs
cloud
cloud
Machine Learning: Cloud Video Intelligence
Google Cloud Video Intelligence
API
cloud
26. ● General steps
a. Prep your training data
b. Create dataset
c. Import items into dataset
d. Create/train model
e. Evaluate/validate model
f. Make predictions
Cloud AutoML: how to use
Machine Learning: Cloud ML Engine
Google Cloud Machine Learning Engine
cloud
27. Machine Learning: Cloud TPUs
Google Cloud TPU API
cloud
Other APIs to consider
These may also be helpful5
28. Storing and Analyzing Data: BigQuery
Google BigQuery
cloud
BigQuery: querying Shakespeare words
TITLE = "The top 10 most common words in all of Shakespeare's works"
QUERY = '''
SELECT LOWER(word) AS word, sum(word_count) AS count
FROM [bigquery-public-data:samples.shakespeare]
GROUP BY word ORDER BY count DESC LIMIT 10
'''
rsp = BQ.query(body={'query': QUERY}, projectId=PROJ_ID).execute()
print('n*** Results for %r:n' % TITLE)
for col in rsp['schema']['fields']: # HEADERS
print(col['name'].upper(), end='t')
print()
for row in rsp['rows']: # DATA
for col in row['f']:
print(col['v'], end='t')
print()
29. Top 10 most common Shakespeare words
$ python bq_shake.py
*** Results for "The most common words in all of Shakespeare's works":
WORD COUNT
the 29801
and 27529
i 21029
to 20957
of 18514
a 15370
you 14010
my 12936
in 11722
that 11519
Running Code: Compute Engine
>
Google Compute Engine
cloud
30. Running Code: App Engine
Google App Engine
we
>
cloud
Running Code: Cloud Functions
Google Cloud Functions
cloud
firebase
31. G Suite: Google Sheets
Sheets API
developers
Try our Node.js customized reporting tool codelab:
g.co/codelabs/sheets
Why use the Sheets API?
data visualization
customized reports
Sheets as a data source
32. Migrate SQL data to a Sheet
# read SQL data then create new spreadsheet & add rows into it
FIELDS = ('ID', 'Customer Name', 'Product Code',
'Units Ordered', 'Unit Price', 'Status')
cxn = sqlite3.connect('db.sqlite')
cur = cxn.cursor()
rows = cur.execute('SELECT * FROM orders').fetchall()
cxn.close()
rows.insert(0, FIELDS)
DATA = {'properties': {'title': 'Customer orders'}}
SHEET_ID = SHEETS.spreadsheets().create(body=DATA,
fields='spreadsheetId').execute().get('spreadsheetId')
SHEETS.spreadsheets().values().update(spreadsheetId=SHEET_ID, range='A1',
body={'values': rows}, valueInputOption='RAW').execute()
Migrate SQL data
to Sheets
goo.gl/N1RPwC
G Suite: Google Slides
Slide API
create
manage
developers
36. Accessing maps from
spreadsheets?!?
goo.gl/oAzBN9
This… with help from Google Maps & Gmail
function sendMap() {
var sheet = SpreadsheetApp.getActiveSheet();
var address = sheet.getRange("A2").getValue();
var map = Maps.newStaticMap().addMarker(address);
GmailApp.sendEmail('friend@example.com', 'Map',
'See below.', {attachments:[map]});
}
JS
37. Simple sentiment & classification analysis
● Analyze sentiment in
Google Docs
● Use simple API call to
Natual Language API
● Call with Apps Script
UrlFetch service
● Build this app yourself at
g.co/codelabs/nlp-docs
[simple API/API key sample]
Simple sentiment & classification analysis
function getSentiment(text) {
var apiKey = YOUR_API_KEY;
var apiEndpoint =
'https://language.googleapis.com/v1/documents:analyzeSentiment?key=' + apiKey;
// NL API metadata JSON object
var nlData = {
document: {
language: 'en',
type: 'PLAIN_TEXT',
content: text
},
encodingType: 'UTF8'
};
38. [simple API/API key sample]
Simple sentiment & classification analysis
// Create API payload
var nlOptions = {
method: 'POST',
contentType: 'application/json',
payload: JSON.stringify(nlData)
};
// Make API call via UrlFetch (when no object available)
var response = UrlFetchApp.fetch(apiEndpoint, nlOptions);
var data = JSON.parse(response);
var sentiment = 0.0;
if (data && data.documentSentiment && data.documentSentiment.score) {
sentiment = data.documentSentiment.score;
}
Logger.log(sentiment);
return sentiment;
}
● Extend functionality of G Suite editors
● Embed your app within ours!
● 2014: Google Docs, Sheets, Forms
● 2017 Q3: Google Slides
● 2017 Q4: Gmail
● 2018 Q1: Hangouts Chat bots
● Apps Script also powers App Maker,
Google Data Studio community
connectors, and Google Ads scripts
Apps Script powers add-ons… and more!
39. 6 All of Cloud
(inspiration)
Build powerful solutions with both
GCP and G Suite
Custom intelligence in Gmail
Analyze G Suite data with GCP
40. Gmail message processing with GCP
Gmail
Cloud
Pub/Sub
Cloud
Functions
Cloud
Vision
G Suite GCP
Star
message
Message
notification
Trigger
function
Extract
images
Categorize
images
41. Inbox augmented with Cloud Function
● Gmail API: sets up notification forwarding to Cloud Pub/Sub
● developers.google.com/gmail/api/guides/push
● Pub/Sub: triggers logic hosted by Cloud Functions
● cloud.google.com/functions/docs/calling/pubsub
● Cloud Functions: "orchestrator" accessing GCP APIs
● Combine all of the above to add custom intelligence to Gmail
● Deep dive code blog post
● cloud.google.com/blog/products/application-development/
adding-custom-intelligence-to-gmail-with-serverless-on-gcp
● Application source code
● github.com/GoogleCloudPlatform/cloud-functions-gmail-nodejs
App summary
42. Big data analysis to slide presentation
Access GCP tools from G Suite
Big data analysis
46. Supercharge G Suite with GCP
G Suite GCP
BigQuery
Apps Script
Slides Sheets
Application
request
Big data
analytics
App summary
● Leverage GCP and build the "final mile" with G Suite
● Driven by Google Apps Script
● Google BigQuery for data analysis
● Google Sheets for visualization
● Google Slides for presentable results
● "Glued" together w/G Suite serverless
● Build this app (codelab)
● g.co/codelabs/bigquery-sheets-slides
● Video and blog post
● bit.ly/2OcptaG
● Application source code
● github.com/googlecodelabs/bigquery-sheets-slides
● Presented at Google Cloud NEXT (Jul 2018 [DEV229] & Apr 2019 [DEV212])
● cloud.withgoogle.com/next18/sf/sessions/session/156878
● cloud.withgoogle.com/next/sf/sessions?session=DEV212
47. 7
Wrap-up
Summary and resources
Session Summary
● What is machine learning again?
○ Solving harder problems by making computers smarter
● How do you machine learning again?
○ Have lots of data (with "labels")
○ Build and train your model then validate it
○ Use your model to make predictions on new data
● Do you need lots of machine learning experience to get started?
○ No: use pre-trained models available through APIs
○ Google Apps Script provides an easy way to do it w/API keys
48. References
● G Suite, Google Apps Script documentation & open source repos
○ developers.google.com/gsuite
○ developers.google.com/apps-script
● Google Cloud Platform (GCP) documentation & open source repos
○ cloud.google.com/bigquery
○ cloud.google.com/vision
○ cloud.google.com/language
○ cloud.google.com/video-intelligence
○ cloud.google.com/speech and cloud.google.com/text-to-speech
● Your next steps… further train our models by customizing them
○ By using the AutoML-enabled ML APIs
○ cloud.google.com/automl
Thank you!
Wesley Chun
@wescpy@
Progress bars: goo.gl/69EJVw
Slides: bit.ly/