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Inaugural QCon.ai 2018 - InfoQ YouTube Exclusive Videos

| by Roxana Bacila Follow 3 Followers on May 14, 2018. Estimated reading time: 9 minutes | NOTICE: QCon.ai - Applied AI for Developers Apr 15 - 17, 2019, San Francisco. Join us!

Recently, the people behind QCon (InfoQ’s conference for senior developers, architects, and leaders in software) announced a new conference called QCon.ai.

Designed as a software conference focused on the practices of artificial intelligence and machine learning, QCon.ai takes a different tack than other major software conferences in the AI/ML space do today. Rather than focusing on data scientists, QCon.ai targets how full-stack software engineers can apply machine learning and AI techniques and toolkits in their day-to-day roles.

QCon.ai is organized by the same team behind QCon San Francisco and provides a platform for innovators and early adopters to share their stories in hotbeds of software development.

The organizers filmed a selection of short videos at this event, published exclusively on the InfoQ Youtube Channel.

Here is a list of the videos published:

Machine Learning: Predicting Demand in Fashion

Ritesh Madan shows how Celect uses the historical data to build a SaaS solution that helps buyers and merchants predict the future demand of products for the upcoming season. He covers the real life problem statement, high level ML frameworks, and how the product is used by buyers and merchants.(Presentation)

Machine Learning: Predicting Demand in Fashion

Ritesh Madan shows how Celect uses the historical data to build a SaaS solution that helps buyers and merchants predict the future demand of products for the upcoming season. He covers the real life problem statement, high level ML frameworks, and how the product is used by buyers and merchants.(Presentation)

Machine Learning: Predicting Demand in Fashion

Ritesh Madan shows how Celect uses the historical data to build a SaaS solution that helps buyers and merchants predict the future demand of products for the upcoming season. He covers the real life problem statement, high level ML frameworks, and how the product is used by buyers and merchants.(Presentation)

Machine Learning: Predicting Demand in Fashion

Ritesh Madan shows how Celect uses the historical data to build a SaaS solution that helps buyers and merchants predict the future demand of products for the upcoming season. He covers the real life problem statement, high level ML frameworks, and how the product is used by buyers and merchants.(Presentation)

Machine Learning: Predicting Demand in Fashion

Ritesh Madan shows how Celect uses the historical data to build a SaaS solution that helps buyers and merchants predict the future demand of products for the upcoming season. He covers the real life problem statement, high level ML frameworks, and how the product is used by buyers and merchants.(Presentation)

Machine Learning: Predicting Demand in Fashion

Ritesh Madan shows how Celect uses the historical data to build a SaaS solution that helps buyers and merchants predict the future demand of products for the upcoming season. He covers the real life problem statement, high level ML frameworks, and how the product is used by buyers and merchants.(Presentation)

Machine Learning: Predicting Demand in Fashion

Ritesh Madan shows how Celect uses the historical data to build a SaaS solution that helps buyers and merchants predict the future demand of products for the upcoming season. He covers the real life problem statement, high level ML frameworks, and how the product is used by buyers and merchants.(Presentation)

Machine Learning: Predicting Demand in Fashion

Ritesh Madan shows how Celect uses the historical data to build a SaaS solution that helps buyers and merchants predict the future demand of products for the upcoming season. He covers the real life problem statement, high level ML frameworks, and how the product is used by buyers and merchants.(Presentation)

 

Machine Learning: Predicting Demand in Fashion

Ritesh Madan shows how Celect uses the historical data to build a SaaS solution that helps buyers and merchants predict the future demand of products for the upcoming season. He covers the real life problem statement, high level ML frameworks, and how the product is used by buyers and merchants.(Presentation)

Machine Learning: Predicting Demand in Fashion

Ritesh Madan shows how Celect uses the historical data to build a SaaS solution that helps buyers and merchants predict the future demand of products for the upcoming season. He covers the real life problem statement, high level ML frameworks, and how the product is used by buyers and merchants.(Presentation)

 

Machine Learning: Predicting Demand in Fashion

Ritesh Madan shows how Celect uses the historical data to build a SaaS solution that helps buyers and merchants predict the future demand of products for the upcoming season. He covers the real life problem statement, high level ML frameworks, and how the product is used by buyers and merchants.(Presentation)

 

 

Machine Learning: Predicting Demand in Fashion

Ritesh Madan shows how Celect uses the historical data to build a SaaS solution that helps buyers and merchants predict the future demand of products for the upcoming season. He covers the real life problem statement, high level ML frameworks, and how the product is used by buyers and merchants.(Presentation)

 

Machine Learning: Predicting Demand in Fashion

Ritesh Madan shows how Celect uses the historical data to build a SaaS solution that helps buyers and merchants predict the future demand of products for the upcoming season. He covers the real life problem statement, high level ML frameworks, and how the product is used by buyers and merchants.(Presentation)

Machine Learning: Predicting Demand in Fashion

Ritesh Madan shows how Celect uses the historical data to build a SaaS solution that helps buyers and merchants predict the future demand of products for the upcoming season. He covers the real life problem statement, high level ML frameworks, and how the product is used by buyers and merchants.(Presentation)

 

Machine Learning: Predicting Demand in Fashion

Ritesh Madan shows how Celect uses the historical data to build a SaaS solution that helps buyers and merchants predict the future demand of products for the upcoming season. He covers the real life problem statement, high level ML frameworks, and how the product is used by buyers and merchants.(Presentation)

 

 

Machine Learning: Predicting Demand in Fashion

Ritesh Madan shows how Celect uses the historical data to build a SaaS solution that helps buyers and merchants predict the future demand of products for the upcoming season. He covers the real life problem statement, high level ML frameworks, and how the product is used by buyers and merchants.(Presentation)

 

JupyterLab: The Next Generation Jupyter Web Interface

Jason Grout gives an overview of JupyterLab, the next generation of the Jupyter Notebook. (Presentation)

 

 

 

Machine Learning: Predicting Demand in Fashion

Ritesh Madan shows how Celect uses the historical data to build a SaaS solution that helps buyers and merchants predict the future demand of products for the upcoming season. He covers the real life problem statement, high level ML frameworks, and how the product is used by buyers and merchants.(Presentation)

 

JupyterLab: The Next Generation Jupyter Web Interface

Jason Grout gives an overview of JupyterLab, the next generation of the Jupyter Notebook. (Presentation)

 

 

 

Building a Security System with Image Recognition & an Amazon DeepLens

Jeremy Edberg shows us step by step how he built a security system for his house using the Amazon DeepLens. He goes over how he built and trained the models, and the steps necessary to get the camera making inferences and sending alerts. (Presentation)

 

 

The Basics of ROS Applied to Self-Driving Cars

Anthony Navarro covers a brief overview of the ROS (Robot Operating System) architecture and how we would begin using it on our self-driving car project.(Presentation)

 

 

pDB: Abstraction for Modeling Predictive Machine Learning Problems

Balaji Rengarajan does a brief overview of modeling machine learning problems using Celect's pDB framework. He demonstrates how disparate predictive problems can be expressed using a common pDB language.(Presentation)

 

 

Building (Better) Data Pipelines with Apache Airflow

Sid Anand talks about Apache Airflow, an up-and-coming platform to programmatically author, schedule, manage, and monitor workflows. (Presentation)

 

 

Transmogrification: The Magic of Feature Engineering

Leah McGuire and Mayukh Bhaowal talk about transmogrification, where they magically and automatically engineer features based on the type of feature, data distribution and association with the response variable. (Presentation)

 

 

Continuous Delivery for AI Applications

Asif Khan talks about how to connect the workflow between the data scientists and DevOps. He explores basic continuous integration and delivery concepts and how they can be applied to deep learning models. Using a number of AWS services, he showcases how we can take the output of a deep learning model and deploy it to perform predictions in real time with low latency and high availability. (Presentation)

 

 

Serverless for Data Science

Mike Lee Williams talks about the basic idea behind serverless cloud architecture, and how to deploy a very simple web application to AWS Lambda using Zappa. He then looks in detail at PyWren, an ultra-lightweight alternative to heavy big data distributed systems such as Spark. (Presentation)

 

TensorBoard: Visualizing Learning

Machine learning models, especially deep learning ones, can be complex. Chi Zeng walks us through how to debug, monitor, and examine the decisions of a TensorFlow-based model using the TensorBoard suite of visualizations. (Presentation)

 

 

Tooling & Setup for My Neural Network

Martin Gorner talks about the toolset that he uses, both to run multiple training experiments in parallel and visualize their outcomes: Tensorflow, Google Cloud ML Engine, Tensorboard. (Presentation)

 

 

A/B Testing for Logistics: It All Depends

Jingjie Xiao explains how Instacart A/B tests changes in the logistics system where neither customers nor shoppers are independent. She also discusses how multivariate regression is used to expedite their pace of innovation. (Presentation)

 

 

PyTorch by Example

In this talk, Jendrik Joerdening talks about PyTorch, what it is, how to build neural networks with it, and compares it to other frameworks. (Presentation)

 

 

When Do You Use Machine Learning vs. a Rules Based System?

Soups Ranjan provides examples of applications where machine learning makes sense and when it doesn't, and gives examples from real-world applications in the risk domain (anti-fraud, cyber security, account takeover detection). (Presentation)

 

 

Two Effective Algorithms for Time Series Forecasting

Danny Yuan explains intuitively Fast Fourier Transformation and recurrent neural network. He explores how the concepts play critical roles in time series forecasting. Learn what the tools are, the key concepts associated with them, and why they are useful in time series. (Presentation)

 

NVIDIA Jetson

Dana Sheahen talks about the Nvidia Jetson TX2 hardware and the software that runs on the TX2 Dev kit. (Presentation)

 

Introduction to Forecasting in Machine Learning and Deep Learning

Franziska Bell provides an overview of classical, machine learning and deep learning forecasting approaches. In addition fundamental forecasting best practices will be covered. (Presentation)

 

What does it take to build a data science capability?

In this panel discussion, Charles Humble and Wes Reisz met with Stephanie Yee (StitchFix), Soups Ranjan (Coinbase), Matei Zaharia (Databricks) and Sid Anand (PayPal) to discuss what it takes to build a data science capability and what the new roles that are being defined in a data-driven company are. (Presentation)

 

 

More videos to follow each week.

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