Data Science Pipelines
What is a Data Science Pipeline?
A pipeline in data science is a workflow that involves various steps to solve critical business problems or derive actionable insights for decision making. Building a data science pipeline workflow includes activities such as capturing large amounts of data, cleaning and maintaining the data, data science modeling, and data visualization and analysis.
What is a Model in Data Science?
After data preparation and exploration, data scientists fit the data into data science models using machine learning algorithms. After selecting the correct data model for the data science pipeline architecture based on data type and business objective, the model is tested for accuracy and other characteristics.
In the case of predictive modeling, computational methods are used to develop predictive models that examine current and historical datasets for underlying patterns and to calculate the probability of an outcome. Data science machine learning techniques allow analysts throughout a wide variety of disciplines to uncover actionable insights, however it is very important to monitor the health of machine learning models and validate predictions alongside actual outcomes in order to achieve the desired results for business intelligence requirements.
Read the Complete Introduction to Data Science.
OmniSci’s Data Science Pipeline Tools
OmniSci enables data scientists to render, cross-filter and explore massive datasets in a fraction of the time of mainstream data science pipeline and data visualization tools help accelerate models in data science and machine learning.
OmniSci is the only GPU-accelerated data science platform that allows faster feature engineering pipelines for machine learning model creation and the ability to "unmask the black box" by visualizing what your black box models see in the data.
Accelerate the Feature Engineering Process
Data scientists must pick data features to train their algorithms. Because OmniSci makes it easier and faster to explore big tables, data scientists train models more quickly, with better outcomes.
Shed Light on Black-box Models
Explaining why AI models make the predictions they make is a notorious challenge. Now, with OmniSci, data science and data visualization are integrated and explaining models to decision-makers, engenders greater trust and adoption of AI.
Visualize Predictions and Outcomes Together
ML models make predictions, but actual outcomes will vary and models can become less predictive over time. Data science solutions with OmniSci visually shows the difference between predictive analytics and actual insights, so you know when to retrain the model.