- How to Make Remote Work Effective for Data Science Teams - Mar 23, 2020.
This post aims to highlight some work from home best practices, both general and data science-specific, in order to help data scientists and teams remain productive, connected and happy while working remotely.
Collaboration, Comet.ml, Data Science Team
- 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
- Solve Data Science Challenges Through Collaboration - Apr 3, 2018.
Get this eBook to learn key issues that hamper fragmented data science teams; how accelerate innovation via collaborative workspaces, and how top data science teams boosted productivity by up to 4x.
Collaboration, Data Science Team, Databricks, ebook
- Strata Data Conference, NYC – Key Trends and Highlights - Oct 12, 2017.
Strata is a conference I very much enjoyed attending. This year, I observed a few common themes that ran across much of the conference content: Data Science Collaboration, Data Ethics, and Platform Optimization.
Collaboration, Ethics, Netflix, New York City, NY, Strata
- RCloud – DevOps for Data Science - Nov 28, 2016.
After almost two decades of software development, term – DevOps was coined and officially given importance to collaboration between development and deployment of software systems. In this early stage of Data Science field, use of standardized and empirical practises like DevOps will definitely speed up its evolution.
Collaboration, Data Science, DevOps, GitHub, R, Scalability
- The Experience of Being a High-Performing Data Scientist - Nov 21, 2016.
Now in open beta, IBM Data Science Experience (DSX) delivers Machine Learning, Collaboration, and Creative capabilities in an open and integrated environment for team data science, including many productivity features for next-generation data science,
Collaboration, Data Science, IBM, IBM DSX
- Driving Data Science Productivity Without Compromising Quality - Sep 14, 2016.
How will data science teams maintain quality standards in the face of advancing automation? Attend the IBM DataFirst Launch Event on Sep 27 in NYC and learn how to drive greater productivity from your data science teams without compromising the quality of the mission-critical business assets they produce.
Citizen Data Scientist, Collaboration, Data Science, IBM, New York City, NY
- FlyElephant 2.0, Big Data High-Performance Computing Platform - Aug 1, 2016.
FlyElephant is a platform for data scientists, engineers and scientists, which provides a ready-computing infrastructure for high-performance computing and rendering.
Collaboration, Docker, FlyElephant, HPC, Open Data Platform
- Interview: Joe Otto, Alpine on Selecting the Right Big Data Vendor - Sep 10, 2014.
We discuss Big Data vendor landscape, key relevant questions, Alpine's competitive differentiation, important qualities sought in data scientists, and more.
Alpine, Collaboration, Competition, Hadoop, Interview, Joe Otto, Skills, Vendors
- Interview: Joe Otto, Alpine on Why Big Data is all about Empowerment & Collaboration - Sep 9, 2014.
We discuss the story of Alpine Data Labs, the recent recognition of Alpine, effect of YARN, major customer use cases, and challenges in consumerizing Big Data.
Advanced Analytics, Alpine, Collaboration, Hadoop, Interview, Joe Otto, Startup, Use Cases, YARN
