About The Event
The Global AI Bootcamp Singapore is a free two-days event organized by local communities that are passionate about artificial intelligence on Microsoft Azure.
Join us for the full day event on Azure to learn, share from the best minds from industry, experts and community leaders!
16th-17th January 2021
We are having a great line-up speakers from around the world to share their passion, experiences in Azure with you!
AI and Ethics
Done well, AI can amplify human ingenuity. Done badly, however, it can create a plethora of new problems. At Microsoft, we’ve spent a lot of time thinking about the ethics of AI with a multidisciplinary group focused on the ethical impact of AI and other emerging data technologies. I’ll share the recommended corporate policies and principles to address the ethical, social, and societal issues raised by these technologies.
Create real value in your business process by automated data and form extraction
Business processes are crucial and documentation are often done with high human involvement. The possibilities of automating it can be valuable to us. Join us to explore, discuss and share more.
Model Interpretability using AzureML SDK
Machine learning model interpretability and explainability is as important model accuracy. It not only helps debug the model performance but can also help draw key insights from the data that business stakeholders can use to make strategic decisions. In this lightening talk, I will show how to use the model interpretability library in AzureML and its use cases.
Integrating Google Assistant for Continuous Integration (CI)
The talk aims to explain the steps to integrate Google Assistant using DialogFlow for Jenkins CI environment. It includes to steps to build an interaction model to know the status of the running jobs, run jobs (if not running) and automate the overall process.
Journey of a data point through Azure Machine Learning Studio
Azure machine learning studio is the new one-stop portal on Azure which enables you to design, depvelop and deploy complex machine learning workflows in a well managed manner. This sessions is a walkthrough on the capabilities of Azure machine learning studio with hands-on demonstration.
Introduction to AI and Cognitive Services for Microsoft 365 Developers and Information Workers
Prashant G Bhoyar
“Artificial Intelligence and Machine Learning are the new buzzwords in the industry. Microsoft's vision is to make AI accessible to every enterprise, data scientist, developer, information worker, consumer and device everywhere in the world. AI has a big role to play in the enterprise space. The field of AI is progressing at a rapid pace. Without understanding the concepts behind these advanced technologies, developers and administrators will struggle to evaluate the potential impact of new tools and solutions. In this session, we will break down the concepts behind existing technologies, outline various tools available today, and discuss the direction of AI and ML for Microsoft 365 Developers. We will cover how developers, Power Users, and Information workers can take advantages of the Microsoft's AI and Cognitive Services offerings to build real-life enterprise solutions. You will learn: 1) Overview of Microsoft AI Platform 2) What are the cognitive services? 3) What tools are available today? 4) How to use Cognitive Services to implement real-life business solutions in Microsoft 365?
Random Walk of the Penguins - 1st prize competition solution
The talk will be on the 1st prize-winning solution in the Random Walk of the Penguins ( hosted by Driven Data ) and sponsored by NASA. This competition solution involves using Supervised ML and Time Series Algorithms
E2E ML Model Operations and ML Lifecycle Management with GitHub Actions and Azure ML
Do you know that over 85% of data science projects fail according to Gartner analyst Nick Heudecker. A report from Dimensional Research indicated that only 4% of companies have succeeded in deploying ML models to production environment. MLOps provides critical capabilities to enable machine learning in production, including model reproducibility & versioning, model auditability & explainability, model packaging & validation, and model deployment & monitoring. I am excited to demonstrate to you how you or your organization can easily set up a data science or machine learning project with automated training and deployment using GitHub Actions and Azure Machine Learning.
Quantum machine learning and optimization
Walk through the emerging quantum machine learning and optimization area, its usage and current applications.
Build your own KITT with Azure Cognitive Services
Cognitive services can recognise objects, faces, describe images, understand speech and language, perform advanced searches and so much more. You can use these services with Azure Bots and IoT to build human interactive cars and houses, or simply interact with clients. If you are a developer and know nothing about Azure Cognitive Services, this is a good place to start. Disclaimer: Azure Cognitive Services can't drive a car. They're not 18 years old yet.
AI Experiences with Power Virtual Agent
M A Nakib
Easily integrate your chatbots with the products and services you use every day. Look up records, personalize conversations, handoff conversations to live agents, and call APIs. Choose from hundreds of pre-built connectors, build custom workflows using Power Automate, or create complex scenarios with Microsoft Bot Framework.
Let’s code a drone to follow faces syncing everything with Azure IoT
You can control a drone using 20 lines of code. That’s the easy part. However, adding extra features like face or object detection and program the drone to follow and object or a face requires … another 20 lines of code! During this workshop we will review how to connect to a drone, how to send and receive commands from the drone, how to read the camera video feed and how to apply AI on top of the camera feed to recognize objects or faces. We will use a simple house drone ($100) and Python. And, when we review some enterprise scenarios, we will use Azure IoT to sync the drone information in IoT mode. Let’s build this!
AI & ML in SQL Server 2019
SQL Server 2019 boasts tons of new features enabling it to be the hub for all your data. In this session we will focus on the new features of SQL server 2019 for all your AI and ML needs. Why move your data around when you can do it all in SQL server 2019? Join the session to find out how.
Building Intelligent Enterprise-Grade Help Desk Bots using Conversational AI
Prashant G Bhoyar
Microsoft's CEO Satya Nadella has said: "Human Language is the new UI layer, bots are like new application". As more and more bots are getting popular in homes and enterprises, the demand for custom bots is increasing at rapid space. The Microsoft Bot framework is a comprehensive open source offering that we can use to build and deploy high-quality bots. Microsoft Cognitive Services let you build apps with powerful algorithms to see, hear, speak, understand and interpret our needs using natural methods of communication, with just a few lines of code. Easily add intelligent features – such as emotion and sentiment detection, vision and speech recognition, language understanding, knowledge, and search – into your app, across devices and platforms such as iOS, Android, and Windows, keep improving and are easy to set up. In this demo-driven session, we will cover how to build the enterprise-grade intelligent bots in using Microsoft Bot Framework and Cognitive Services and deploy in multiple platforms (channels) like Microsoft Teams, SharePoint, Public-Facing Web Sites, etc You will learn: What is Microsoft Bot Framework? What is Azure Bot Service? How to create bots using Microsoft Bot Framework? What are Cognitive Services? How to leverage Bot Framework and Cognitive Services to implement enterprise-grade bots? How to deploy Bots to Microsoft Teams, SharePoint and Public facing Websites?
Preparing for Data and AI Driven Future
This session is aimed to address the concerns of undergraduates and future workforce on how they can prepare for Data and AI Driven Future. Sharing of needed skills and available resources to be part of the Data and AI community
Face Recognition and Visual Search with Azure Cognitive Services
Azure Cognitive services package AI as APIs ready to use with in our applications. Two such compelling offerings are Azure Face services and Bing visual search. Face API is a part of Cognitive vision service that enables face recognition, detection, emotion identification etc. Whereas bing visual search helps interpret images to create compelling visual experiences. Bing search can be used to recognise celebrities, monuments, artwork, identify barcodes or extract text from image, enrich search experience with visually similar images and more- all through the single API. Let us walkthrough these exciting services and their usage in applications to provide rich insights
Time Series Forecasting in Power BI
Time Series Forecasting is essential for any size business to prepare and plan for the future. While Power BI has an in-built forecasting feature that is easy to use, it is severely limited, poorly documented and often misused. In this session, I will explain how Power BI forecasting works, its strengths/limitations, how to overcome those limitations and create an advanced forecasting model.
Machine learning in the browser using TensorFlow.js
Transfer Learning for Deep Learning using TensorFlow & ML.NET
Transfer learning is a machine learning technique in which a model that was developed for an initial task serves now as the starting point for a model on a second duty. It is quite useful in Deep Learning since compute and time resources are limited, so you a pre-trained model can be used as an input for a computer vision or natural language processing task. Let's demonstrate how Transfer Learning works in ML.NET by exploring the following scenario: - Firstly, a pre-trained TensorFlow Inception model deep learning model will be incorporated in an ML.NET workflow. - Then, transfer learning is applied to this model using the ML.NET Image Classification API in order to create a new, custom deep learning model that identifies specific image categories. All the knowledge gained when solving the initial classification problem is useful for shortcutting another training process and solve a second classification. - If time allows it, we can deploy this model into a web API for consumption from another application (such as a mobile app)
Building Smarter Solutions Using Azure And Cognitive Services
There's a lot of discussion going on around Artificial Intelligence, and for good reason. AI and Cognitive Services are getting more powerful all the time, and it can be confronting to see all these developments. But how can we leverage this power in our own solutions, using it for making the life of our users and customers easier? In this session, you will see learn we can get data from the real world, and use this to drive our business, and all this in a serverless manner. Thanks to Microsoft Cognitive Services it's easy to integrate and work with speech, text, images and videos into our processes. Come and learn how to use this to your own advantage, driving your business forward.
Perform image classification at the Azure IoT edge with Custom Vision Service
Azure IoT Edge can make your IoT solution more efficient by moving workloads out of the cloud and to the edge. This capability lends itself well to services that process a lot of data, like computer vision models. The Custom Vision Service lets you build custom image classifiers and deploy them to devices as containers. Together, these two services enable you to find insights from images or video streams without having to transfer all of the data off site first. Custom Vision provides a classifier that compares an image against a trained model to generate insights.
Azure Machine Learning and Cognitive Services [WORKSHOP v4]
This training will introduce you to Machine Learning and AI services and tools in Azure. The session will cover Azure ML, Spark using Azure Databricks, Azure Synapse Analytics, HDInsight, SQL Server Machine Learning services, Cognitive Services tools and will provide for hands-on experience as well. Machine learning is a data science technique that allows computers to use existing data to forecast future behaviour, outcomes, and trends. By using machine learning, computers learn without being explicitly programmed. Forecasts or predictions from machine learning can make apps and devices smarter. For example, when you shop online, machine learning helps recommend other products you might want based on what you've bought. Or when your credit card is swiped, machine learning compares the transaction to a database of transactions and helps detect fraud. And when your robot vacuum cleaner vacuums a room, machine learning helps it decide whether the job is done. Azure Machine Learning, Spark using Azure Databricks and Azure Synapse Analytics, are cloud services that you use to train, deploy, automate, and manage machine learning models, all at the broad scale that the cloud provides. Azure Cognitive Services are APIs / SDKs / services available to help developers build intelligent applications without the need for AI or data science skills or knowledge. Azure Cognitive Services enable developers to easily add cognitive features such as emotion and video detection; facial, speech, and vision recognition; and speech and language understanding – into their applications. The goal of Azure Cognitive Services is to help developers create applications that can see, hear, speak, understand, and even begin to reason. Attendees that want to try the examples during the workshop need to bring: * Laptop * Have an Azure subscription (e.g. set up a free trial at https://azure.com/free , use an Azure Pass free trial, or use a paid subscription); * Your preferred development IDE (e.g. Visual Studio 2019 Community Edition or Visual Studio Code) already installed, to access online webservices; * Excel (2013 or more recent); Note: C# examples will be covered, but the webservices can work with any programming language in your applications. Contents: 1. Introduction to Machine Learning (generic - from scratch, no pre-requisite needed) 2. Introduction to Azure Machine Learning technologies: Azure Machine Learning, Spark with Azure Databricks, Azure Synapse Analytics, HDInsight, SQL Server ML Services with R or Python, etc. 3. Introduction to Azure Cognitive Services and Bots
Here are some of our speakers
NASA Citizen Scientist , TCS Data and Analytics Practice Lead , ETRM Practice Lead
Developer / DBA / Microsoft Certified Trainer (MCT)
APAC Developer Relations at Microsoft
AI Dude - Microsoft AI MVP
Cloud Solution Architect - Azure MVP
Manager AI and Big Data
Microsoft MVP - Artificial Intelligence
Mobile Application Developer at LTI
Microsoft, Global Cloud Solution Architect
Microsoft MVP, Xamarin Certified Mobile Developer
M A Nakib
Azure MCT, Asst. Manager on Cloud Solutions at Corporate Projukti Limited
Lead Data Platform Consultant - Pythian Services | Microsoft MVP - Data Platform
Prashant G Bhoyar
Microsoft AI MVP, Senior Solution Architect-Intelligent Process Automation at Withum
Solution Architect, Franklin Templeton
Data Specialist and MCT Regional Lead
Simulation & Data Analytics Engineer
L&T infotech _ Module Lead _ IIoT
Lead Architect, Microsoft Corp