Remove Automation Remove Data Platform Remove Data Science
article thumbnail

Data science vs data analytics: Unpacking the differences

IBM Journey to AI blog

Though you may encounter the terms “data science” and “data analytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts. Meanwhile, data analytics is the act of examining datasets to extract value and find answers to specific questions.

article thumbnail

How Rocket Companies modernized their data science solution on AWS

AWS Machine Learning Blog

Rockets legacy data science environment challenges Rockets previous data science solution was built around Apache Spark and combined the use of a legacy version of the Hadoop environment and vendor-provided Data Science Experience development tools.

professionals

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

The Rise and Fall of Data Science Trends: A 2018–2024 Conference Perspective

ODSC - Open Data Science

The field of data science has evolved dramatically over the past several years, driven by technological breakthroughs, industry demands, and shifting priorities within the community. Initially, organizations struggled with versioning, monitoring, and automating model updates.

article thumbnail

How Krista Software helped Zimperium speed development and reduce costs with IBM Watson

IBM Journey to AI blog

Krista Software helps Zimperium automate operations with IBM Watson Vamsi Kurukuri, VP of Site Reliability at Zimperium, developed a strategy to remove roadblocks and pain points in Zimperium’s deployment process. Once all parties approve the release, Krista then deploys it.

DevOps 214
article thumbnail

How to choose the best AI platform

IBM Journey to AI blog

AI platforms offer a wide range of capabilities that can help organizations streamline operations, make data-driven decisions, deploy AI applications effectively and achieve competitive advantages. Some AI platforms also provide advanced AI capabilities, such as natural language processing (NLP) and speech recognition.

article thumbnail

Generative AI use cases for the enterprise

IBM Journey to AI blog

Automate tedious, repetitive tasks. Key considerations: Tech stack: Ensure your existing technology infrastructure can handle the demands of AI models and data processing. Teamwork: Assemble a team with expertise in AI, data science and your industry. Best practices are evolving rapidly.

article thumbnail

Foundational models at the edge

IBM Journey to AI blog

These include data ingestion, data selection, data pre-processing, FM pre-training, model tuning to one or more downstream tasks, inference serving, and data and AI model governance and lifecycle management—all of which can be described as FMOps. IBM watsonx consists of the following: IBM watsonx.ai