Applications Data Engineering Machine Learning 
TechCrunch
MARCH 31, 2021
We’re living in a phenomenal moment for machine learning (ML), what Sonali Sambhus , head of developer and ML platform at Square, describes as “the democratization of ML.” I’ve distilled our best practices and must-know components into five practical and easily applicable lessons. ML recruiting strategy.
O'Reilly Media - Data
APRIL 3, 2018
Strata Data London will introduce technologies and techniques; showcase use cases; and highlight the importance of ethics, privacy, and security. The growing role of data and machine learning cuts across domains and industries. Data Science and Machine Learning sessions will cover tools, techniques, and case studies.
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CIO
NOVEMBER 27, 2024
Recent research shows that 67% of enterprises are using generative AI to create new content and data based on learned patterns; 50% are using predictive AI, which employs machine learning (ML) algorithms to forecast future events; and 45% are using deep learning, a subset of ML that powers both generative and predictive models.
Cloudera
JANUARY 9, 2025
Thats why were moving from Cloudera Machine Learning to Cloudera AI. Why AI Matters More Than ML Machine learning (ML) is a crucial piece of the puzzle, but its just one piece. It means combining data engineering, model ops, governance, and collaboration in a single, streamlined environment.
O'Reilly Media - Data
AUGUST 16, 2018
In this episode of the Data Show , I spoke with Harish Doddi , co-founder and CEO of Datatron , a startup focused on helping companies deploy and manage machine learning models. Today’s data science and data engineering teams work with a variety of machine learning libraries, data ingestion, and data storage technologies.
TechCrunch
NOVEMBER 18, 2021
But with time, enterprises overcame their skepticism and moved critical applications to the cloud. DevOps fueled this shift to the cloud, as it gave decision-makers a sense of control over business-critical applications hosted outside their own data centers.
O'Reilly Media - Data
JULY 18, 2018
It’s no secret that companies place a lot of value on data and the data pipelines that produce key features. In the early phases of adopting machine learning (ML), companies focus on making sure they have sufficient amount of labeled (training) data for the applications they want to tackle.
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