Data Pipelines

Data Pipelines

Enterprise data rarely exist in the exact form or format required by data scientists for a given project. Rather, raw data must be processed through a series of transformations in order to cleanse and normalize it before it can be used for training. Once a model is put into production, this same sequence of transformations must be applied to the data to ready it for inference. Early in the exploratory phase of model development, these transformations are often applied in an ad hoc manner. However, manual transformations should give way to programmatically executed transformations very quickly, as the former are highly error-prone, not readily repeatable, and don’t scale.

SAS Visual Data Mining and Machine Learning
Solve the most complex analytical problems with a single, integrated, collaborative solution
RapidMiner Studio
One platform, does everything
IBM Cloudpak for Data
Reinvent how you work
Modern MLOps focused on speed and simplicity
Weights & Biases
With a few lines of code, save everything you need to debug, compare and reproduce your models
AI and machine learning model management and operations for enterprise data science teams
Oracle – Data Science Platform
Data science platform
Creating data science
Open-source version control system for machine learning projects
IBM Watson Studio
Build and scale trusted AI on any cloud. Automate the AI lifecycle for ModelOps