Subject archive for "data-science," page 3

Pipes stacked together to form a circular structure
Data Science

Designing a Best-in-Class MLOps Pipeline

Today, one of the biggest challenges facing data scientists is taking models from development to production in an efficient and reproducible way. In this way, machine learning (ML) pipelines seek to identify the steps involved in this process. Once the steps are defined, they can be automated and orchestrated, streamlining the data science lifecycle.

By Damaso Sanoja10 min read

Data Science

Building Robust Models with Cross-Validation in Python

So, you have a machine learning model! Congratulations! Now what? Well, as I always remind colleagues, there is no such thing as a perfect model, only good enough ones. With that in mind, the natural thing to do is to ensure that the machine learning model you have trained is robust and can generalise well to unseen data. On the one hand, we need to ensure that our model is not under- or overfitting, and on the other one, we need to optimise any hyperparameters present in our model. In order words, we are interested in model validation and selection.

By Dr J Rogel-Salazar16 min read

financial services icons in a data science environment
Data Science

Deep Learning & Machine Learning Applications in Financial Services

In the last few years, machine learning and deep learning have become a core part of fields like healthcare, retail, banking, and insurance. This list can go on indefinitely—there's almost no field where machine learning (ML) is not applied to improve the overall efficiency and accuracy of systems. As the world's economy has rapidly grown, so has the need to do things automatically.

By Gourav Singh Bais39 min read

Data Science

7 Top Innovators Share Insights, Trends and Career Advice in 'The Data Science Innovator’s Playbook'

Who’s doing the most innovative things in data science? Where is the profession going? And most importantly, what can you learn from some of the brightest in the business?

By Lisa Stapleton6 min read

Perspective

Rocketing Confidence in Data Science, Poll Finds: Are Better Tools the Reason?

Businesses are increasingly betting big on data science for ambitious near-term growth, just one more indication that the rapidly rising profession is making itself a huge force for innovation in fields as diverse as healthcare & pharma, defense, insurance, and financial services. Nearly half of respondents in a recent poll said that their company’s leadership expects data science efforts to produce double-digit revenue growth. A similar survey in 2021 put that same figure at only 25%, indicating growing expectations for the young profession.

By Lisa Stapleton4 min read

Comma separated values containing integers
Perspective

The Case for Reproducible Data Science

Reproducibility is a cornerstone of the scientific method and ensures that tests and experiments can be reproduced by different teams using the same method. In the context of data science, reproducibility means that everything needed to recreate the model and its results such as data, tools, libraries, frameworks, programming languages and operating systems, have been captured, so with little effort the identical results are produced regardless of how much time has passed since the original project.

By Sundeep Teki8 min read

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