What I Learned From Tecton's apply() 2022 Conference

This long-form article dissects content from 14 sessions and lightning talks that I found most useful from attending Tecton's apply() 2022 virtual conference. These talks cover 3 major areas: industry trends, production use cases, and open-source libraries. | Continue reading


@jameskle.com | 2 years ago

What I Learned from the Open Source Data Stack Conference 2021

Building the modern stack with open-source data solutions | Continue reading


@jameskle.com | 2 years ago

Components Towards Building Production-Ready Machine Learning Systems

I recently attended the Full-Stack Deep Learning Bootcamp in the UC Berkeley campus, which is a wonderful course that teaches full-stack production deep learning. One of the lectures delivered by Sergey Karayev provided excellent coverage of model testing and deployment. In t … | Continue reading


@jameskle.com | 4 years ago

Variants of Matrix Factorization for Collaborative Filtering

In this post and those to follow, I will be walking through the creation and training of recommendation systems, as I am currently working on this topic for my Master Thesis. Part 4 looks into the nitty-gritty mathematical details of matrix factorization, arguably the most common … | Continue reading


@jameskle.com | 4 years ago

Basics of Data Cleaning

This semester, I’m taking a graduate course called  Introduction to Big Data.  It provides a broad introduction to the exploration and management of large datasets being generated and used in the modern world. In an effort to open-source this knowledge to the wider data … | Continue reading


@jameskle.com | 4 years ago

Exploring Itemset Mining

This semester, I’m taking a graduate course called  Introduction to Big Data.  It provides a broad introduction to the exploration and management of large datasets being generated and used in the modern world. In an effort to open-source this knowledge to the wider data … | Continue reading


@jameskle.com | 4 years ago

Basics of Decision Trees

This semester, I’m taking a graduate course called  Introduction to Big Data.  It provides a broad introduction to the exploration and management of large datasets being generated and used in the modern world. In an effort to open-source this knowledge to the wider data … | Continue reading


@jameskle.com | 4 years ago

Distributed Data Processing

This semester, I’m taking a graduate course called  Introduction to Big Data.  It provides a broad introduction to the exploration and management of large datasets being generated and used in the modern world. In an effort to open-source this knowledge to the wider data … | Continue reading


@jameskle.com | 4 years ago

Troubleshooting Deep Neural Networks

I recently attended the Full-Stack Deep Learning Bootcamp in the UC Berkeley campus, which is a wonderful course that teaches full-stack production deep learning. Josh Tobin delivered a great lecture on troubleshooting deep neural networks. As a courtesy of Josh’s lecture, th … | Continue reading


@jameskle.com | 4 years ago

Running an effective data science POC

What does running a POC mean in practice specifically for data science? When it comes to the evaluation of data science solutions, POCs should prove not just that a solution solves one particular, specific problem, but that a system will provide widespread value to the company: t … | Continue reading


@jameskle.com | 4 years ago

Concepts in Data Management

In this blog post, I would like to share the 5 concepts in managing data to ensure their eventual quality as inputs for your machine learning models. | Continue reading


@jameskle.com | 4 years ago

Set Up Machine Learning Projects

In this blog post, I would like to share the 5 steps that you can use to set up your Machine Learning projects up for success. | Continue reading


@jameskle.com | 4 years ago

Research Directions for Recommendation Systems

In this post and those to follow, I will be walking through the creation and training of recommendation systems, as I am currently working on this topic for Master Thesis. Part 3 addresses the limitations of using deep learning-based recommendation models by proposing a couple of … | Continue reading


@jameskle.com | 4 years ago

Categories of Deep Recommendation Systems Researchers Should Pay Attention

In this post and those to follow, I will be walking through the creation and training of recommendation systems, as I am currently working on this topic for my Master Thesis. Part 2 provides a nice review of the ongoing research initiatives with regard to the strengths, weaknesse … | Continue reading


@jameskle.com | 4 years ago

An Executive Guide to Building Recommendation System

In this post and those to follow, I will be walking through the creation and training of recommendation systems, as I am currently working on this topic for Master Thesis. Part 1 provides a high-level overview of recommendation systems, how they are built, and how they can be use … | Continue reading


@jameskle.com | 4 years ago

A Friendly Introduction to Data-Driven Marketing Business Leaders

Taking an algorithmic approach to attribution is just the beginning of driving change by moving toward a more detailed, data-driven approach in marketing. | Continue reading


@jameskle.com | 4 years ago

What I Learned from Attending All Tech Is Human NYC 2019

Let’s co-create a more thoughtful future towards technology! | Continue reading


@jameskle.com | 4 years ago

The 4 Steps To Build Out Your Machine Learning Team Productively

In this blog post, I would like to share some insights into how to think about building and managing Machine Learning teams if you are a manager, and also possibly help you get a job in Machine Learning if you are a job seeker. | Continue reading


@jameskle.com | 4 years ago

Questions You Need to Ask to Operate Deep Learning Infrastructure at Scale

In this blog post, I would like to share the 7 questions that you and your Deep Learning colleagues should ask to handle deep learning technical debt. | Continue reading


@jameskle.com | 4 years ago