Dataiku

FEBRUARY 23, 2022

In January, Dataiku’s Léo Dreyfus-Schmidt and Reda Affane presented our annual webinar on up-and-coming machine learning (ML) trends — this year, with a spin on grounding those research trends in reality with actual use cases from our data science team.

Data Science 59

Data Science Machine Learning Optimization Modeling 59

Dataiku

MARCH 2, 2022

To kick off every year, we host a technical webinar with members of our AI Labs research team in order to hone in on some of the hot ML trends we’ll likely see more of in the coming year. To take that to a new level, this year’s webinar combined the research insights with practical data science projects that back up the trends.

Optimization 59

Optimization Data Science Modeling Data Processing 59

AWS Big Data

MAY 1, 2023

Real-time anomaly detection describes a use case to detect and flag unexpected behavior in streaming data as it occurs. In typical setups, we want to be able to run the RCF algorithm on input data with large throughput, and streaming data processing frameworks can help with that.

Data Analytics 69

Data Analytics Analytics Software Machine Learning 69

Cloudera

DECEMBER 3, 2021

In part 1 of this blog post, we discussed the need to be mindful of data bias and the resulting consequences when certain parameters are skewed. Surely there are ways to comb through the data to minimise the risks from spiralling out of control. An AI system trained on data has no context outside of that data.

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Uncertainty Measurement Metrics Risk 99

Domino Data Lab

MARCH 11, 2021

Machine learning (ML) frameworks are interfaces that allow data scientists and developers to build and deploy machine learning models faster and easier. Using these tools, businesses can scale their machine learning efforts while maintaining an efficient ML lifecycle. How to choose the right ML Framework.

Machine Learning 143

Machine Learning Deep Learning Insurance Modeling 143

IBM Big Data Hub

SEPTEMBER 20, 2022

Organizations are increasingly depending upon artificial intelligence (AI) and Machine Learning (ML) to assist humans in decision making. But these organizations need to be able to trust their AI/ML models before they can be operationalized and used in crucial business processes. How a data fabric enables trustworthy AI.

Metadata 58

Metadata Machine Learning Data-driven Modeling 58

Alation

APRIL 14, 2022

Moving data to the cloud can bring immense operational benefits. However, the sheer volume and complexity of today’s enterprise data can cause downstream headaches for data users. Semantics, context, and how data is tracked and used mean even more as you stretch to reach post-migration goals. Data pipeline orchestration.

Metadata 59

Metadata Data Governance Modeling Data-driven 59