Go Data Driven BLOG!
Real time analytics: Divolte + Kafka + Druid + Superset
DeepCS - Berlin Buzzwords 2019
Fairness in AI - Dutch Data Science Week 2019
How to build your first image classifier using PyTorch
Data Science Podcast Recommendations
The Analytics Translator Part 3: Characteristics of an Analytics Translator
The Analytics Translator Part 2: The Problems an Analytics Translator Solves
An Analytics Translator enables the execution of your company’s AI strategy. Data engineers are good at developing robust applications. Data scientists are good at distilling intelligence from data. Business teams know their specific processes, habits, and work-arounds like no other. Still, there is a gap between data experts and business. Translators bridge it by solving three business problems for you.
GoDataDriven Open Source Contribution for May and June 2019
Deploying Apache Airflow on Azure Kubernetes Service
The Analytics Translator Part 1: Delivering Business Value with Data and AI
Many organizations have not seen return on their investment after developing their data and AI capabilities. It’s imperative to account for all of the phases of an AI solution life-cycle. Find the right business problems to solve in the Ideation phase, discover if there is a viable business model during an Experimentation phase, and scale up in an Industrialization phase.