This document is a curriculum vitae for Nguyen Truong Xuan, a PHP developer with experience since 2009. It lists his personal details, education, technical skills, employment history and descriptions of projects he has worked on, including developing web platforms for advertising and real estate using technologies like Laravel, AngularJS, and MySQL. His most recent role is as a client-side developer at Savvycom Software building projects like Curiocity, an advertising platform.
The talk was given at OReilly Strata Data Conference September 2018 in NYC
All the conferences and thought leaders have been painting a vision of the businesses of the future being powered by data, but if we’re honest with ourselves, the vast majority of our massive data science investments are being deployed to PowerPoint or maybe a business dashboard. Productionizing your machine learning (ML) portfolio is the next big step on the path to ROI from AI.
You probably started out years ago on a “big data” initiative: You collected and cleaned your data and built data warehouses, and when those filled up you upgraded to data lakes. You hired data engineers and data scientists, and around the organization, everyone brushed up their SQL querying skills and got some licenses to Tableau and PowerBI.
Then you saw what Google, Uber, Facebook, and Amazon were doing with machine learning to automate business processes and customer interactions. To not get broadsided, you hired more data scientists and machine learning engineers. They were put on your teams and started using your big data investments to train models. But what you probably found is that your tech stack and DevOps processes don’t fit ML models. Unlike most of your systems, ML models require short spikes of massive compute; they are often written in different languages than your core code; they need different hardware to perform well; one model probably has applications across many teams; and the people making the models often don’t have the engineering experience to write production code but need to iterate faster than traditional engineers. Expecting your engineering and DevOps teams to deploy ML models well is like showing up to Seaworld with a giraffe since they are already handling large mammals.
There is a path forward. Almost five years ago Algorithmia launched a marketplace for models, functions, and algorithms. Today 65,000 developers are on the platform deploying 4,500 models—the result has been a layer of tools and best practices to make deploying ML models frictionless, scalable, and low maintenance. The company refers to it as the “AI layer.”
Drawing on this experience, Diego Oppenheimer covers the strategic and technical hurdles each company must overcome and the best practices developed while deploying over 4,000 ML models for 70,000 engineers.
Topics include:
Best practices for your organization
Continuous model deployment
Varying languages (Your code base probably isn’t in Python or R, but your ML models probably are.)
Managing your portfolio of ML models
Standardize versioning
Enabling models across your organization
Analytics on how and where models are being used
Maintaining auditability
This presentation will show you overview of Google Cloud Service and show step-by-step example with Wordpress to introduce each service on GCP
Google Cloud Study Jam Bangkok 2019 #1 and #2 at ITKMITL and CPE KU on October 19-20, 2019
The talk was given at OReilly Strata Data Conference September 2018 in NYC
All the conferences and thought leaders have been painting a vision of the businesses of the future being powered by data, but if we’re honest with ourselves, the vast majority of our massive data science investments are being deployed to PowerPoint or maybe a business dashboard. Productionizing your machine learning (ML) portfolio is the next big step on the path to ROI from AI.
You probably started out years ago on a “big data” initiative: You collected and cleaned your data and built data warehouses, and when those filled up you upgraded to data lakes. You hired data engineers and data scientists, and around the organization, everyone brushed up their SQL querying skills and got some licenses to Tableau and PowerBI.
Then you saw what Google, Uber, Facebook, and Amazon were doing with machine learning to automate business processes and customer interactions. To not get broadsided, you hired more data scientists and machine learning engineers. They were put on your teams and started using your big data investments to train models. But what you probably found is that your tech stack and DevOps processes don’t fit ML models. Unlike most of your systems, ML models require short spikes of massive compute; they are often written in different languages than your core code; they need different hardware to perform well; one model probably has applications across many teams; and the people making the models often don’t have the engineering experience to write production code but need to iterate faster than traditional engineers. Expecting your engineering and DevOps teams to deploy ML models well is like showing up to Seaworld with a giraffe since they are already handling large mammals.
There is a path forward. Almost five years ago Algorithmia launched a marketplace for models, functions, and algorithms. Today 65,000 developers are on the platform deploying 4,500 models—the result has been a layer of tools and best practices to make deploying ML models frictionless, scalable, and low maintenance. The company refers to it as the “AI layer.”
Drawing on this experience, Diego Oppenheimer covers the strategic and technical hurdles each company must overcome and the best practices developed while deploying over 4,000 ML models for 70,000 engineers.
Topics include:
Best practices for your organization
Continuous model deployment
Varying languages (Your code base probably isn’t in Python or R, but your ML models probably are.)
Managing your portfolio of ML models
Standardize versioning
Enabling models across your organization
Analytics on how and where models are being used
Maintaining auditability
This presentation will show you overview of Google Cloud Service and show step-by-step example with Wordpress to introduce each service on GCP
Google Cloud Study Jam Bangkok 2019 #1 and #2 at ITKMITL and CPE KU on October 19-20, 2019
Vertex AI - Unified ML Platform for the entire AI workflow on Google CloudMárton Kodok
Vertex AI is a managed ML platform for practitioners to accelerate experiments and deploy AI models.
Enhanced developer experience
- Build with the groundbreaking ML tools that power Google
- Approachable from the non-ML developer perspective (AutoML, managed models, training)
- Ease the life of a data scientist/ML (has feature store, managed datasets, endpoints, notebooks)
- Infrastructure management overhead have been almost completely eliminated
- Unified UI for the entire ML workflow
- End-to-end integration for data and AI with build pipelines that outperform and solve complex ML tasks
- Explainable AI and TensorBoard to visualize and track ML experiments
Common Object Request Broker Architecture - CORBAPeter R. Egli
Overview of CORBA (Common Object Request Broker Architecture) object technology.
CORBA is a distributed object technology (DOT) that extends the remote procedure call semantics to distributed objects.
Object interfaces are described in a formal language called IDL (Interface Description Language) that allows generating stubs and skeletons through an IDL compiler.
FIWARE Wednesday Webinars - How to Design DataModelsFIWARE
How to Design DataModels - 8th May 2019
Corresponding webinar recording: https://youtu.be/T_1DpKf6C_c
Understanding and applying Standard Data Models.
Chapter: Core
Difficulty: 3
Audience: Technical Domain Specific
Presenter: José Manuel Cantera (Senior Standardization Expert, FIWARE Foundation)
Apache Camel is a Java-based open-source integration framework based on Enterprise Integration Patterns (EIP). Camel uses the established Enterprise Integration Patterns and out-of-the-box adapters with highly expressive Domain Specific Language (DSL) - Java, Spring XML, Scala, and Groovy. Apache Camel is a great implementation of Enterprise Integration Patterns and can be very helpful in integrating huge applications.
Building large scale, job processing systems with Scala Akka Actor frameworkVignesh Sukumar
The Akka Actor framework is designed to be a fast message processing system. In this talk, we will explain how, at Box, we have used this framework to develop a large scale job processing system that works on billions of data files and achieves a high degree of throughput and fault tolerance. Over the course of the talk, we will explore the usage of Akka framework’s Supervisor functionality to provide a more controllable fault-tolerance strategy, and how we can use Futures to manage asynchronous jobs.
Creating a Context-Aware solution, Complex Event Processing with FIWARE PerseoFernando Lopez Aguilar
Introduction to Complex Event Processing (CEP). How FIWARE deals with CEP through FIWARE Perseo. How to connect FIWARE Perseo with FIWARE Orion Context Broker. How can we define an event with Event Processing Language (EPL) and what are the predefined actions to include in FIWARE Perseo.
MLflow: Infrastructure for a Complete Machine Learning Life Cycle with Mani ...Databricks
ML development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools, and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. To address these problems, many companies are building custom “ML platforms” that automate this lifecycle, but even these platforms are limited to a few supported algorithms and to each company’s internal infrastructure. In this session, we introduce MLflow, a new open source project from Databricks that aims to design an open ML platform where organizations can use any ML library and development tool of their choice to reliably build and share ML applications. MLflow introduces simple abstractions to package reproducible projects, track results, and encapsulate models that can be used with many existing tools, accelerating the ML lifecycle for organizations of any size. In this deep-dive session, through a complete ML model life-cycle example, you will walk away with:
MLflow concepts and abstractions for models, experiments, and projects
How to get started with MLFlow
Understand aspects of MLflow APIs
Using tracking APIs during model training
Using MLflow UI to visually compare and contrast experimental runs with different tuning parameters and evaluate metrics
Package, save, and deploy an MLflow model
Serve it using MLflow REST API
What’s next and how to contribute
Machine Learning operations brings data science to the world of devops. Data scientists create models on their workstations. MLOps adds automation, validation and monitoring to any environment including machine learning on kubernetes. In this session you hear about latest developments and see it in action.
This ppt contains basic idea of deep learning, motivation behind deep learning, technical overview of AI and deep learning, types of learning approaches, basic DL model development steps and at last applications of deep learning including various areas where deep learning can prominently applied.
"Automated machine learning (AutoML) is the process of automating the end-to-end process of applying machine learning to real-world problems. In a typical machine learning application, practitioners must apply the appropriate data pre-processing, feature engineering, feature extraction, and feature selection methods that make the dataset amenable for machine learning. Following those preprocessing steps, practitioners must then perform algorithm selection and hyperparameter optimization to maximize the predictive performance of their final machine learning model. As many of these steps are often beyond the abilities of non-experts, AutoML was proposed as an artificial intelligence-based solution to the ever-growing challenge of applying machine learning. Automating the end-to-end process of applying machine learning offers the advantages of producing simpler solutions, faster creation of those solutions, and models that often outperform models that were designed by hand."
In this talk we will discuss how QuSandbox and the Model Analytics Studio can be used in the selection of machine learning models. We will also illustrate AutoML frameworks through demos and examples and show you how to get started
TensorFlow London 12: Oliver Gindele 'Recommender systems in Tensorflow'Seldon
Speaker: Oliver Gindele, Data Scientist at Datatonic
Title: Recommender systems with TensorFlow
Abstract:
Recommender systems are widely used by e-commerce and services companies worldwide to provide the most relevant items to the user. Many different algorithm and models exist to tackle the problem of finding the best product in a huge library of items for every user. In this talk, Oliver explains how some of these models can be implemented in TensorFlow, starting from a collaborative filtering approach and extending that to deep recommender systems.
Speaker Bio:
Oliver is a Data Scientist at Datatonic with a background in computational physics and high performance computing. He is a machine learning practitioner who recently started exploring the world of deep learning.
Thanks to all TensorFlow London meetup organisers and supporters:
Seldon.io
Altoros
Rewired
Google Developers
Rise London
New Ways to Improve Hospital Flow with Predictive AnalyticsHealth Catalyst
Improving hospitalwide patient flow requires an appreciation of the hospital as an interconnected, interdependent system of care. Michael Thompson explores how Cedars-Sinai Medical Center used supervised machine learning to create predictive models for length of stay, emergency department (ED) arrivals, ED admissions, aggregate discharges, and total bed census and leveraged these models to reduce patient wait times and staff overtime and improve patient outcomes and patient and clinician satisfaction.
Learn more about the following topics:
• How to engage leaders up front with the goal of operationalizing analytics.
• What types of machine learning methods best support operationalizing analytics.
• How to operationalize machine learning-driven results to improve patient flow.
Vertex AI - Unified ML Platform for the entire AI workflow on Google CloudMárton Kodok
Vertex AI is a managed ML platform for practitioners to accelerate experiments and deploy AI models.
Enhanced developer experience
- Build with the groundbreaking ML tools that power Google
- Approachable from the non-ML developer perspective (AutoML, managed models, training)
- Ease the life of a data scientist/ML (has feature store, managed datasets, endpoints, notebooks)
- Infrastructure management overhead have been almost completely eliminated
- Unified UI for the entire ML workflow
- End-to-end integration for data and AI with build pipelines that outperform and solve complex ML tasks
- Explainable AI and TensorBoard to visualize and track ML experiments
Common Object Request Broker Architecture - CORBAPeter R. Egli
Overview of CORBA (Common Object Request Broker Architecture) object technology.
CORBA is a distributed object technology (DOT) that extends the remote procedure call semantics to distributed objects.
Object interfaces are described in a formal language called IDL (Interface Description Language) that allows generating stubs and skeletons through an IDL compiler.
FIWARE Wednesday Webinars - How to Design DataModelsFIWARE
How to Design DataModels - 8th May 2019
Corresponding webinar recording: https://youtu.be/T_1DpKf6C_c
Understanding and applying Standard Data Models.
Chapter: Core
Difficulty: 3
Audience: Technical Domain Specific
Presenter: José Manuel Cantera (Senior Standardization Expert, FIWARE Foundation)
Apache Camel is a Java-based open-source integration framework based on Enterprise Integration Patterns (EIP). Camel uses the established Enterprise Integration Patterns and out-of-the-box adapters with highly expressive Domain Specific Language (DSL) - Java, Spring XML, Scala, and Groovy. Apache Camel is a great implementation of Enterprise Integration Patterns and can be very helpful in integrating huge applications.
Building large scale, job processing systems with Scala Akka Actor frameworkVignesh Sukumar
The Akka Actor framework is designed to be a fast message processing system. In this talk, we will explain how, at Box, we have used this framework to develop a large scale job processing system that works on billions of data files and achieves a high degree of throughput and fault tolerance. Over the course of the talk, we will explore the usage of Akka framework’s Supervisor functionality to provide a more controllable fault-tolerance strategy, and how we can use Futures to manage asynchronous jobs.
Creating a Context-Aware solution, Complex Event Processing with FIWARE PerseoFernando Lopez Aguilar
Introduction to Complex Event Processing (CEP). How FIWARE deals with CEP through FIWARE Perseo. How to connect FIWARE Perseo with FIWARE Orion Context Broker. How can we define an event with Event Processing Language (EPL) and what are the predefined actions to include in FIWARE Perseo.
MLflow: Infrastructure for a Complete Machine Learning Life Cycle with Mani ...Databricks
ML development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools, and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. To address these problems, many companies are building custom “ML platforms” that automate this lifecycle, but even these platforms are limited to a few supported algorithms and to each company’s internal infrastructure. In this session, we introduce MLflow, a new open source project from Databricks that aims to design an open ML platform where organizations can use any ML library and development tool of their choice to reliably build and share ML applications. MLflow introduces simple abstractions to package reproducible projects, track results, and encapsulate models that can be used with many existing tools, accelerating the ML lifecycle for organizations of any size. In this deep-dive session, through a complete ML model life-cycle example, you will walk away with:
MLflow concepts and abstractions for models, experiments, and projects
How to get started with MLFlow
Understand aspects of MLflow APIs
Using tracking APIs during model training
Using MLflow UI to visually compare and contrast experimental runs with different tuning parameters and evaluate metrics
Package, save, and deploy an MLflow model
Serve it using MLflow REST API
What’s next and how to contribute
Machine Learning operations brings data science to the world of devops. Data scientists create models on their workstations. MLOps adds automation, validation and monitoring to any environment including machine learning on kubernetes. In this session you hear about latest developments and see it in action.
This ppt contains basic idea of deep learning, motivation behind deep learning, technical overview of AI and deep learning, types of learning approaches, basic DL model development steps and at last applications of deep learning including various areas where deep learning can prominently applied.
"Automated machine learning (AutoML) is the process of automating the end-to-end process of applying machine learning to real-world problems. In a typical machine learning application, practitioners must apply the appropriate data pre-processing, feature engineering, feature extraction, and feature selection methods that make the dataset amenable for machine learning. Following those preprocessing steps, practitioners must then perform algorithm selection and hyperparameter optimization to maximize the predictive performance of their final machine learning model. As many of these steps are often beyond the abilities of non-experts, AutoML was proposed as an artificial intelligence-based solution to the ever-growing challenge of applying machine learning. Automating the end-to-end process of applying machine learning offers the advantages of producing simpler solutions, faster creation of those solutions, and models that often outperform models that were designed by hand."
In this talk we will discuss how QuSandbox and the Model Analytics Studio can be used in the selection of machine learning models. We will also illustrate AutoML frameworks through demos and examples and show you how to get started
TensorFlow London 12: Oliver Gindele 'Recommender systems in Tensorflow'Seldon
Speaker: Oliver Gindele, Data Scientist at Datatonic
Title: Recommender systems with TensorFlow
Abstract:
Recommender systems are widely used by e-commerce and services companies worldwide to provide the most relevant items to the user. Many different algorithm and models exist to tackle the problem of finding the best product in a huge library of items for every user. In this talk, Oliver explains how some of these models can be implemented in TensorFlow, starting from a collaborative filtering approach and extending that to deep recommender systems.
Speaker Bio:
Oliver is a Data Scientist at Datatonic with a background in computational physics and high performance computing. He is a machine learning practitioner who recently started exploring the world of deep learning.
Thanks to all TensorFlow London meetup organisers and supporters:
Seldon.io
Altoros
Rewired
Google Developers
Rise London
New Ways to Improve Hospital Flow with Predictive AnalyticsHealth Catalyst
Improving hospitalwide patient flow requires an appreciation of the hospital as an interconnected, interdependent system of care. Michael Thompson explores how Cedars-Sinai Medical Center used supervised machine learning to create predictive models for length of stay, emergency department (ED) arrivals, ED admissions, aggregate discharges, and total bed census and leveraged these models to reduce patient wait times and staff overtime and improve patient outcomes and patient and clinician satisfaction.
Learn more about the following topics:
• How to engage leaders up front with the goal of operationalizing analytics.
• What types of machine learning methods best support operationalizing analytics.
• How to operationalize machine learning-driven results to improve patient flow.
To pursue career in an organization which offers tremendous growth potential to my career giving Exposure to latest technologies fully utilizing my skills.
1. CURRICULUM VITAE
PERSONAL DETAILS
Full name Nguyen Truong Xuan Telephone (+84) 989.649.075
Date of birth 1988/05/25 Email xuan.0211@gmail.com
Gender Male Time working in IT 2009 - present
Marital
status
Married Position PHP Developer
FOREIGN LANGUAGES
Language Proficiency
English Be accepted
EDUCATION AND TRAINING
Period Institution, specialization Specialty
2006-2009 Hanoi University of Industry Faculty of Electricity
2011-2013 Electric Power University Faculty of Electricity
PRACTICAL EXPERIENCE
Technical skills
Programming Languages
PHP (Zend 1.x, CodeIgniter, Laravel, Wordpress, Opencart …)
Java (Selenium Automation Framework)
JavaScript (Ajax, jQuery, AngularJS, RequireJS, ReactJS …)
HTML/CSS
1 | P a g e
2. Programming tools
Notepad++
Eclipse
Putty
Analysis & Design tools
Navicat
Database
MySQL
OS
Windows
Linux: Fedora
Version Control
Subversion
Git
EMPLOYMENT HISTORY
08/2016 – present
Savvycom Software
Client-side developer
Ha Noi
03/2012 – 07/2016 FPT Software
Client-side developer
PHP Developer
Ha Noi
07/2010 – 01/2012 Hitech Solutions and Services Providing., JSC
PHP developer
Ha Noi
2 | P a g e
3. 11/2009 – 05/2010 Viet River., JSC
PHP developer
Ha Noi
02/2009 – 10/2009 Thien Viet Media., JSC
PHP developer
Ha Noi
PROJECT DETAILS
Curiocity AD Web Platform 09/2016 – Present
Company Savvycom Software
Project
Description
The focus is on getting a product online and ready for sale fast, while still
maintaining the internal system structure that will allow us to maximize
the usage of our new planning and technical platform (AX and M2).
While this tool can be used for almost any media type (making hotspots
link to 720 and video and links from pictures to pictures) the first version
here only describes it for pictures, but the process for other types are similar.
Role Client-side developer
Team 04 members
Build on
Laravel 5.x
AngularJS 1.x, Jquery
Bootstrap CSS
MySQL
Amazon S3
Curiocity AD Web Platform 07/2016 – 09/2016
Company Savvycom Software
3 | P a g e
4. Project
Description
Curiocity platform is designed to meet advertising requirements from both
corporate and Individual Advertisers, as revolutionary platform, Curiocity
platform connect advertisers and advertisement viewers directly without
any media mediators, allow advertisers to create and publish their own
advertisement through the web platform.
Role Client-side developer
Team 10 members
Build on
Laravel 5.x
ReactJS
Bootstrap CSS
MySQL
RTN – Real Time Neighbourhood 03/2016 – 07/2016
Company FPT Software
Project
Description
RTN is system which will be integrated with EHR systems to calculate wait
time and send notification to user.
RTN system including:
• EHR Systems Integration
• Marketing Portal
• Admin Portal
• Customer Portal
• User Portal: web version and mobile version
Role Client-side developer
Team 10 members
Build on
Laravel 5.x
AngularJS 2.x, Jquery
Bootstrap CSS
MySQL
4 | P a g e
5. NeoPost Statistics 03/2014 – 02/2016
Company FPT Software
Project
Description
The project provide an application will collect data from all franking
machines and then provide for customer reporting about number of items
have franked according period (day, week, month, year).
Role PHP Developer
Team 04 members
Customer Neopost Bagneux
Build on
Zend Framework 1.x
AngularJS 1.x, Jquery
HTML, CSS
MySQL
Selenium Automation Testing 01/2014 – 02/2014
Company FPT Software
Project
Description
Software tools execute pre-scripted tests on a software application before it is
released into production.
Role Java Developer
Team 03 members
5 | P a g e
6. Customer Neopost ID
Build on
Selenium automates browsers (Based on Java)
Online Shipping Service – Singapore Post 05/2012 – 12/2013
Company FPT Software
Project
Description
Provide a system for booking and tracking shipments for Singapore Post Center.
It’s allows users includes: SingPost staffs, corporate customers, retail customers, ad-
hoc customers booking shipments easy.
Role PHP Developer
Team 10 members
Customer Neopost ID
Build on
Zend Framework 1.x
Jquery
HTML, CSS
MySQL
HMIS - Health Management Information System 07/2010 – 01/2012
Company Hitech Solutions and Services Providing., JSC
Project
Description Manage medicine for each commune at Vietnam. Replacing the paper-
based processing system by computerizing.
Provide reports include:
6 | P a g e
7. - Total population
- The population distribution
- Diseases by region
Role PHP Developer
Team 09 members
Build on
PHP4
Javascript, Jquery
HTML, CSS
MySQL
Customer The Population Council, Inc
Address: One Dag Hammarskjold Plaza, 9th Floor, New York, NY 10017,
USA
Bugs Tracker
04/2011 - 05/2011
Company Hitech Solutions and Services Providing., JSC
Project
Description
Bugs tracker management by specific project
Role PHP Developer
Team 04 members
Customer Company product
Address: 1014 Hoang Quoc Viet Street, Ha Noi
Law Office – Brandco.vn 09/2009 - 11/2009
7 | P a g e
8. Company
Thien Viet Media., JSC
Project
Description
A CMS for law office
Role PHP Developer
Team 03 members
Build on
PHP4
Javascript, Jquery
HTML, CSS
MySQL
Customer Brandco Co., LTD
Address: Room 1201, Building N2D, Trung Hoa Urban Area, Nhan Chinh,
Ha Noi
8 | P a g e