Applied Machine Learning

Tuesday, October 25, 2022

- PDT
PRO Workshop (AI): How Route Optimisation Can Be Scaled and Optimised Using Meta Heuristics for Realistic Scenario
Sushant Burnawal
Sushant Burnawal
Publicis Sapient, Senior Associate Level 1

ECommerce platforms drive the current era, and the COVID pandemic gave rise to the need for home delivery. The end consumers have multiple options to cater for their needs, and in that case, the eCommerce platforms have to provide on-time and quality delivery to stay ahead in the market and, at the same time, boost their profit margins.

Route Optimization is one of the most critical aspects of planning and transportation. It ensures that deliveries always arrive on time and carry out with the lowest possible cost and energy consumption. However, there are a lot of variables that eCommerce platforms need to consider in a real-time scenario.

During this unfortunate COVID pandemic, eCommerce platforms deal with a massive inflow of e-commerce orders from customers scattered throughout a city, country or even across the globe. This gives rise to an enormous number of variables come into play that cannot be solved using conventional methods in a reasonable amount of time. With the recent developments in AI, machine learning and cloud data, the entire game of route optimization has begun to change. AI continuously retrieves data, learns from it, and searches for improved methods to ensure the most optimal routes for the drivers.

In the novel solution, we are trying to solve the multi-objective vehicle routing problem with optimization variables like minimizing the delivery cost, the number of vehicles and delivery time. To show this as a real-life simulation, we will dissect through the open-source library of veroviz combined with innovative scaling solutions to showcase the real-time implementation of route optimization in any part of the world. 

- PDT
PRO Workshop (AI): Scaling ML Embedding Models to Serve a Billion Queries
Senthilkumar Gopal
Senthilkumar Gopal
eBay, Senior Engineering Manager, Search ML

This talk is aimed at providing a deeper insight into the scale, challenges and solutions formulated for powering embeddings based visual search in eBay. This talk walks the audience through the model architecture, application archite for serving the users, the workflow pipelines produced for building the embeddings to be used by Cassini, eBay's search engine and the unique challenges faced during this journey. This talk provides key insights specific to embedding handling and how to scale systems to provide real time clustering based solutions for users. 

- PDT
PRO Workshop (AI): Artificial General Intelligence with GPT-3 with Open AI
Cameron Vetter
Cameron Vetter
Octavian Technology Group, Principal Architect

Large Language Models (LLM) have come out of the realm of academia and research and become available to average development teams thanks to the efforts of Open AI and their competitors. Now that we have access to them what can we do with them?

This talk will explore some of the practical uses for GPT-3 made available through Open AI. We will start with a brief introduction to LLM's and transformers and how they bring us a step closer to artificial general intelligence. We will focus on real demonstrations. Each capability will start with a canned demonstration and move on to ad hoc input provided by the audience.

• Text Generation
○ Turn complex text into a simple summary
○ Create an outline of an essay
• Conversation
○ Sarcastic chat bot
• Code Generation
○ Explain Python Code
○ Translate text into programmatic commands
• Question Answering
○ Factual Answering

You will leave this talk with an understanding of Large Language Models and their practical use cases. Walk away inspired on how to apply large language models to your business today! 

Wednesday, October 26, 2022

- PDT
OPEN TALK (AI): Lessons Learned Building Natural Language Systems in Healthcare
David Talby
David Talby
John Snow Labs, CTO

This session reviews case studies from real-world projects that built AI systems that use Natural Language Processing (NLP) in healthcare. These case studies cover projects that deployed automated patient risk prediction, automated diagnosis, clinical guidelines, and revenue cycle optimization.

We will cover why and how NLP was used, what deep learning models and libraries were used, how transfer learning enables tuning accurate models from small datasets, and what was productized and achived. Key takeaways for attendees will include applicable best practices for NLP projects including how to build domain-specific healthcare models and using NLP as part of larger machine learning and deep learning pipelines. 

- PDT
PRO TALK (AI): Designing and Applying AI on Large Volume of IOT Telemetry Data in Azure
Rahat Yasir
Rahat Yasir
ISAAC Instrument, AI, Director of Data Science & AI

IOT devices are producing large amount of telemetry data. We need to ingest them, store them, visualize them, analyze them and build ML models on them to make those data useful. In this session, we will talk about the ways to deal with IOT data, using IOT data to train AI models, building AI models and deploying AI models to inference on real time large volume of IOT data using Azure AI tools. 

Thursday, October 27, 2022

- PDT
OPEN TALK (AI): Bringing Life and Motion to AI Explainability
Joao Nogueira
Joao Nogueira
Optum, Senior AI Engineer
Pietro Mascolo
Pietro Mascolo
Optum Ireland, Data Scientist

SHAP is a great tool to help developers and users understand black box models. To push it to the next level, we will show how to leverage on Dash, SHAP, gifs, and auto-encoders to generate interactive dashboards with animations and visual representations to understand how different AI models learn and change their minds while progressively trained with growing amounts of data.

Animations will help developers understand how frequently AI models tweak their population and local importance factors during training and how they compare across competing AI models, adding an extra layer to AI safety. Auto-encoders and LSTM will be used to generate 2-dimensional embedding representations of explainability paths at individual level, allowing developers to interactively detect algorithm decision making similarity across time and visually debug mislabeled AI predictions at each point in time.

We will show this application in the context of Chronic Kidney Disease prediction and broader Healthcare AI. 

Tuesday, November 1, 2022

- PDT
[#VIRTUAL] PRO Workshop (AI): How Route Optimisation Can Be Scaled and Optimised Using Meta Heuristics for Realistic Scenario
Sushant Burnawal
Sushant Burnawal
Publicis Sapient, Senior Associate Level 1

ECommerce platforms drive the current era, and the COVID pandemic gave rise to the need for home delivery. The end consumers have multiple options to cater for their needs, and in that case, the eCommerce platforms have to provide on-time and quality delivery to stay ahead in the market and, at the same time, boost their profit margins.

Route Optimization is one of the most critical aspects of planning and transportation. It ensures that deliveries always arrive on time and carry out with the lowest possible cost and energy consumption. However, there are a lot of variables that eCommerce platforms need to consider in a real-time scenario.

During this unfortunate COVID pandemic, eCommerce platforms deal with a massive inflow of e-commerce orders from customers scattered throughout a city, country or even across the globe. This gives rise to an enormous number of variables come into play that cannot be solved using conventional methods in a reasonable amount of time. With the recent developments in AI, machine learning and cloud data, the entire game of route optimization has begun to change. AI continuously retrieves data, learns from it, and searches for improved methods to ensure the most optimal routes for the drivers.

In the novel solution, we are trying to solve the multi-objective vehicle routing problem with optimization variables like minimizing the delivery cost, the number of vehicles and delivery time. To show this as a real-life simulation, we will dissect through the open-source library of veroviz combined with innovative scaling solutions to showcase the real-time implementation of route optimization in any part of the world. 

- PDT
[#VIRTUAL] PRO Workshop (AI): Scaling ML Embedding Models to Serve a Billion Queries
Senthilkumar Gopal
Senthilkumar Gopal
eBay, Senior Engineering Manager, Search ML

This talk is aimed at providing a deeper insight into the scale, challenges and solutions formulated for powering embeddings based visual search in eBay. This talk walks the audience through the model architecture, application archite for serving the users, the workflow pipelines produced for building the embeddings to be used by Cassini, eBay's search engine and the unique challenges faced during this journey. This talk provides key insights specific to embedding handling and how to scale systems to provide real time clustering based solutions for users. 

- PDT
[#VIRTUAL] PRO Workshop (AI): Artificial General Intelligence with GPT-3 with Open AI
Cameron Vetter
Cameron Vetter
Octavian Technology Group, Principal Architect

Large Language Models (LLM) have come out of the realm of academia and research and become available to average development teams thanks to the efforts of Open AI and their competitors. Now that we have access to them what can we do with them?

This talk will explore some of the practical uses for GPT-3 made available through Open AI. We will start with a brief introduction to LLM's and transformers and how they bring us a step closer to artificial general intelligence. We will focus on real demonstrations. Each capability will start with a canned demonstration and move on to ad hoc input provided by the audience.

• Text Generation
○ Turn complex text into a simple summary
○ Create an outline of an essay
• Conversation
○ Sarcastic chat bot
• Code Generation
○ Explain Python Code
○ Translate text into programmatic commands
• Question Answering
○ Factual Answering

You will leave this talk with an understanding of Large Language Models and their practical use cases. Walk away inspired on how to apply large language models to your business today! 

- PDT
[#VIRTUAL] PRO Workshop (AI): Intro to Machine Learning with ML.NET
David Patrick
David Patrick
DSA, MCT, MCSD, MCSE, MVP

Come and get immersed into the world of machine learning with this introduction and demonstration of to ML.NET. We'll show how to create an app that can predict the type of iris flower based on features such as petal length. We'll show how to download and install ML.NET, create a data set, write the required c# code and run the finished app. 

Wednesday, November 2, 2022

- PDT
[#VIRTUAL] OPEN TALK (AI): Lessons Learned Building Natural Language Systems in Healthcare
David Talby
David Talby
John Snow Labs, CTO

This session reviews case studies from real-world projects that built AI systems that use Natural Language Processing (NLP) in healthcare. These case studies cover projects that deployed automated patient risk prediction, automated diagnosis, clinical guidelines, and revenue cycle optimization.

We will cover why and how NLP was used, what deep learning models and libraries were used, how transfer learning enables tuning accurate models from small datasets, and what was productized and achived. Key takeaways for attendees will include applicable best practices for NLP projects including how to build domain-specific healthcare models and using NLP as part of larger machine learning and deep learning pipelines. 

- PDT
[#VIRTUAL] PRO TALK (AI): Designing and Applying AI on Large Volume of IOT Telemetry Data in Azure
Rahat Yasir
Rahat Yasir
ISAAC Instrument, AI, Director of Data Science & AI

IOT devices are producing large amount of telemetry data. We need to ingest them, store them, visualize them, analyze them and build ML models on them to make those data useful. In this session, we will talk about the ways to deal with IOT data, using IOT data to train AI models, building AI models and deploying AI models to inference on real time large volume of IOT data using Azure AI tools. 

- PDT
[#VIRTUAL] PRO Workshop (AI): Deploying Machine Learning Models with Pulsar Functions
David Kjerrumgaard
David Kjerrumgaard
StreamNative, Developer Advocate

In this talk I will present a technique for deploying machine learning models to provide real-time predictions using Apache Pulsar Functions. In order to provide a prediction in real-time, the model usually receives a single data point from the caller, and is expected to provide an accurate prediction within a few milliseconds. 

Throughout this talk, I will demonstrate the steps required to deploy a fully-trained ML that predicts the delivery time for a food delivery service based upon real-time traffic information, the customer's location, and the restaurant that will be fulfilling the order. 

- PDT
[#VIRTUAL] OPEN TALK (AI): BUILD ML ENHANCED EVENT STREAMING APPLICATIONS WITH JAVA MICROSERVICES
Timothy Spann
Timothy Spann
StreamNative, Developer Advocate

In this talk we will walk through how to build event streaming applications as functions running in with cloud native messaging via Apache Pulsar that run on near infinite scale in any cloud, docker or K8. We will show you have to deploy ML functions to transform real-time data for IoT, Streaming Analytics and many other use cases. After this talk you will be able to build Java microservices with ease and deploy them anywhere utilizing the open source unified streaming and messaging platform, Apache Pulsar. Finally, we will show you have to add dashboards with Web Sockets, no code data sinks, integrate with Apache NiFi data pipelines, SQL Reports with Apache Spark and finally continuous ETL with Apache Flink. I have built many of these applications for many organizations as part of the FLiPN Stack. Let's build next generation applications today regardless if your data is REST APIs, Sensors, Logs, NoSQL Sources, Events or Database tables. 

- PDT
[#VIRTUAL] OPEN TALK (AI): Dataset Design for Legally Clean, Technically Superior AI Development
Michael Osterrieder
Michael Osterrieder
vAIsual, Co-Founder and CEO

With data being the oil of machine learning, the adage “garbage in, garbage out” could not be truer. When I began to build the technology for photos on demand, I quickly realized that using scraped data presented both technical and legal minefields. As a result we at vAIsual decided to create our own datasets with professional, studio photography and GDPR compliant model releases. In this presentation I will explain how the lighting conditions and work flow for dataset design compliments the latest algorithms to deliver photo realistic images. 

Thursday, November 3, 2022

- PDT
[#VIRTUAL] PRO TALK (AI): Using Machine Learning to detect fraud in Payments
Tamsin Crossland
Tamsin Crossland
Icon Solutions, Senior Architect

This presentation discusses how Machine Learning can be applied to Payments to respond rapidly to known and emerging patterns of fraud, and to detect patterns of fraud that may not otherwise be identified.
It will cover techniques that have been used and are emerging in fraud detection including rule-based techniques, supervised learning and unsupervised learning. The presentation includes a demonstration using TensorFlow to detect fraud. This will illustrate the process of preparing training and test data, learning and then applying the model to generate potential fraud events.
The talk will also explore potential issues including data bias and mitigating approaches. 

- PDT
[#VIRTUAL] OPEN TALK (AI): Data-Driven Models Using Graphs for Communication Networks
Caio Vinicius Dadauto
Caio Vinicius Dadauto
Encora, Data Scientist

Behavior identification is a typical requirement for communication network issues, such as malicious call identification, DoS attacks, and fault recognition. Classical data-driven models using regular-structure data are widely explored unsuccessfully, due to the lack of expressivity of these types of data.

In his technical session, Caio Vinicius Dadauto will provide details of why graphs are suitable for communication networks and how to use them to improve the quality of machine learning models. He will give an overview of graph neural networks, graph kernels, and complex network metrics emphasizing the relevance of these graph properties to data-driven solutions in communication networks. 

- PDT
[#VIRTUAL] OPEN TALK (AI): Bringing Life and Motion to AI Explainability
Joao Nogueira
Joao Nogueira
Optum, Senior AI Engineer
Pietro Mascolo
Pietro Mascolo
Optum Ireland, Data Scientist

SHAP is a great tool to help developers and users understand black box models. To push it to the next level, we will show how to leverage on Dash, SHAP, gifs, and auto-encoders to generate interactive dashboards with animations and visual representations to understand how different AI models learn and change their minds while progressively trained with growing amounts of data.

Animations will help developers understand how frequently AI models tweak their population and local importance factors during training and how they compare across competing AI models, adding an extra layer to AI safety. Auto-encoders and LSTM will be used to generate 2-dimensional embedding representations of explainability paths at individual level, allowing developers to interactively detect algorithm decision making similarity across time and visually debug mislabeled AI predictions at each point in time.

We will show this application in the context of Chronic Kidney Disease prediction and broader Healthcare AI.