Data Platform Data Science ETL 
AWS Machine Learning Blog
FEBRUARY 21, 2025
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.
ODSC - Open Data Science
MARCH 12, 2025
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. Data Engineerings SteadyGrowth 20182021: Data engineering was often mentioned but overshadowed by modeling advancements.
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AI News
SEPTEMBER 26, 2023
AI and machine learning (ML) models are incredibly effective at doing this but are complex to build and require data science expertise. HT: When companies rely on managing data in a customer data platform (CDP) in tandem with AI, they can create strong, personalised campaigns that reach and inspire their customers.
IBM Journey to AI blog
JANUARY 5, 2023
The right data architecture can help your organization improve data quality because it provides the framework that determines how data is collected, transported, stored, secured, used and shared for business intelligence and data science use cases.
IBM Journey to AI blog
JULY 17, 2023
By supporting open-source frameworks and tools for code-based, automated and visual data science capabilities — all in a secure, trusted studio environment — we’re already seeing excitement from companies ready to use both foundation models and machine learning to accomplish key tasks.
Marktechpost
JULY 17, 2023
IBM merged the critical capabilities of the vendor into its more contemporary Watson Studio running on the IBM Cloud Pak for Data platform as it continues to innovate. The platform makes collaborative data science better for corporate users and simplifies predictive analytics for professional data scientists.
TransOrg Analytics
AUGUST 9, 2024
In the realm of data management and analytics, businesses face a myriad of options to store, manage, and utilize their data effectively. Understanding their differences, advantages, and ideal use cases is crucial for making informed decisions about your data strategy. Cons: Costly: Can be expensive to implement and maintain.
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