- Here’s what you need to look for in a model server to build ML-powered services - Sep 15, 2020.
More applications are being infused with machine learning while MLOps processes and best practices are becoming well established. Critical to these software and systems are the servers that run the models, which should feature key capabilities to drive successful enterprise-scale productionizing of machine learning.
- [eBook] Standardizing the Machine Learning Lifecycle - Mar 15, 2019.
We explore what makes the machine learning lifecycle so challenging compared to regular software, and share the Databricks approach.
- [Webinar] Managing the Complete Machine Learning Lifecycle - Feb 28, 2019.
Join Databricks Mar 7, 2019, to learn how using MLflow can help you keep track of experiment runs and results across frameworks, execute projects remotely on to a Databricks cluster, and quickly reproduce your runs, and more. Sign up for this webinar now.
- Manage your Machine Learning Lifecycle with MLflow – Part 1 - Jul 5, 2018.
Reproducibility, good management and tracking experiments is necessary for making easy to test other’s work and analysis. In this first part we will start learning with simple examples how to record and query experiments, packaging Machine Learning models so they can be reproducible and ran on any platform using MLflow.
- KDnuggets Exclusive: Interview with Geoffrey Moore: Crossing the Chasm and Big Data - Mar 14, 2014.
KDnuggets talks with a noted author Geoffrey Moore about his "Crossing the Chasm" book, his vision for Big Data analytics, when Big Data will cross the chasm, and advice for entrepreneurs.