- Unleashing the Power of MLOps and DataOps in Data Science - Jun 29, 2021.
Organizations trying to move forward with analytics and data science initiatives -- while floating in an ocean of data -- must enhance their overall approach and culture to embrace a foundation on DataOps and MLOps. Leveraging these operational frameworks are necessary to enable the data to generate real business value.
Best Practices, Data Science, DataOps, MLOps
- DataOps: 5 things that you need to know - May 20, 2021.
DataOps (Data Operations) has assumed a critical role in the age of big data to drive definitive impact on business outcomes. This process-oriented and agile methodology synergizes the components of DevOps and the capabilities of data engineers and data scientists to support data-focused workloads in enterprises. Here is a detailed look at DataOps.
Data Engineer, Data Engineering, DataOps
- Model Experiments, Tracking and Registration using MLflow on Databricks - Jan 5, 2021.
This post covers how StreamSets can help expedite operations at some of the most crucial stages of Machine Learning Lifecycle and MLOps, and demonstrates integration with Databricks and MLflow.
Data Science, Databricks, DataOps, Experimentation, MLflow, MLOps, Modeling, StreamSets
- Managing Machine Learning Cycles: Five Learnings from comparing Data Science Experimentation/ Collaboration Tools - Jan 29, 2020.
Machine learning projects require handling different versions of data, source code, hyperparameters, and environment configuration. Numerous tools are on the market for managing this variety, and this review features important lessons learned from an ongoing evaluation of the current landscape.
Collaboration, Comet.ml, Data Operations, Data Workflow, DataOps, MLflow, MLOps, Pipeline, Reproducibility
