O'Reilly on Data
SEPTEMBER 24, 2018
Getting DataOps right is crucial to your late-stage big data projects. At Strata 2017 , I premiered a new diagram to help teams understand why teams fail and when: Early on in projects, management and developers are responsible for the success of a project. For big data, this isn't just making sure cluster processes are running.
David Menninger's Analyst Perspectives
OCTOBER 31, 2024
It was not until the addition of open table formats— specifically Apache Hudi, Apache Iceberg and Delta Lake—that data lakes truly became capable of supporting multiple business intelligence (BI) projects as well as data science and even operational applications and, in doing so, began to evolve into data lakehouses.
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Smart Data Collective
APRIL 5, 2022
Although SageMaker has become a popular hardware accelerator since it was launched in 2017, there are plenty of other overlooked hardware accelerators on the market. If you want to streamline various parts of the data science development process, then you should be aware of all of your options.
CIO Business Intelligence
AUGUST 11, 2023
How natural language processing works NLP leverages machine learning (ML) algorithms trained on unstructured data, typically text, to analyze how elements of human language are structured together to impart meaning.
datapine
NOVEMBER 19, 2019
This feature hierarchy and the filters that model significance in the data, make it possible for the layers to learn from experience. Thus, deep nets can crunch unstructured data that was previously not available for unsupervised analysis. billion in 2017 to $190.61 Regarding the future growth of AI, it is undeniable.
CIO Business Intelligence
MARCH 29, 2024
Ramping up for model-agnostic AI Rocket is as much an engineering company as it is a mortgage lender, with more than 1,000 engineers and 600 data scientists working together to build most of Rocket’s code in-house — a major advantage to its innovation efforts. This will push data into repositories best ingested by AI models.
BizAcuity
NOVEMBER 26, 2019
However, migration from SSRS 2017 has some issues with the PBI RS server. The SSRS configuration manager forgot to run a script that updates the SSRS 2017 ReportServer database so that it can actually work with the Power BI objects. SSRS is conventional and involves manual effort and time to create reports and analysis.
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