Data Scientists spend their days working in Jupyter notebooks, which are then passed to an implementation team to prepare for production. This post guides you through that process, emphasizing iterative refinement. I will be using the scikit-learn and XGBoost libraries, but other ML libraries could be swapped in. While scikit-learn offers a comprehensive library of … Read More
A client recently started building a new machine learning (ML) architecture with a feature store as one of the key pieces. The feature store was already burning through a lot of money on AWS Elasticache and it wasn’t even scaled up in production yet! The project was in danger of being shelved without serious cost … Read More
Podcast: Play in new window | Download (Duration: 56:16 — 43.4MB) | Embed
Ken Rimple and Sujan Kapadia bring a serious smorgasbord of topics to the livestream this week: spanning from machine learning doodles, data viz with Nivo, the OSI model, and more.
Podcast: Play in new window | Download (Duration: 45:56 — 41.0MB) | Embed
Today Sujan takes the reins as host, and welcomes long-time Charioteer, Rich Freedman! Sujan and Rich cover dev news, then delve into machine learning, TensorFlow and TensorFlow Lite, and some Arduino work Rich has been doing recently.
Podcast: Play in new window | Download (Duration: 29:46 — 25.8MB) | Embed
Time for another DevNews with Ken Rimple and Sujan Kapadia! This week’s news items include machine learning with K, Lambdas for Excel, NodeJS race conditions, and some controversy bubbling around certain Chromium features.
Podcast: Play in new window | Download (Duration: 38:49 — 33.1MB) | Embed
Ken Rimple and Sujan Kapadia bring this week’s dev news to the table: spatial indexing with CockroachDB, TinyML, the Maven daemon, and more.
Check out our YouTube playlist to watch all the talks from Emerging Technologies for the Enterprise 2020. Abstract Machine learning and IoT have become commonplace words in the enterprise workplace. Even in our homes we have ML-enabled IoT devices (we’re looking at you, Amazon Alexa). But what does it look like to combine ML and … Read More
Check out our YouTube playlist to watch all the talks from Emerging Technologies for the Enterprise 2020. Abstract Last year at Philly ETE, Jonathan explained five lessons from his last five years in distributed databases. This time, he offers his predictions on the future: what impact will databases experience from machine learning, graph algorithms, hardware … Read More
Abstract Getting started with machine learning on the mobile platform can be daunting. From learning how to implement low-level models and use frameworks, to optimizing and adapting those models for mobile, it can be overwhelming. With ML Kit, regardless of one’s machine learning expertise, one can build compelling machine learning functionality into their mobile apps. … Read More
Abstract What exactly is a Neural Network? How do you go from experimenting with a deep learning model in the notebook of your choice to deploying your model to production? What is the secret sauce to a successful feature or platform that utilizes “AI” to accomplish the business goals or needs of your platform? And … Read More
