Domino Data Lab

APRIL 7, 2019

A good NLP library will, for example, correctly transform free text sentences into structured features (like cost per hour and is diabetic ), that easily feed into a machine learning (ML) or deep learning (DL) pipeline (like predict monthly cost and classify high risk patients ). Image Credit: Parsa Ghaffari on the Raylien Blog.

Deep Learning 110

Deep Learning Machine Learning Experimentation Modeling 110

Data Science and Beyond

SEPTEMBER 19, 2016

Causality and experimentation. Making Bayesian A/B testing more accessible. Why you should stop worrying about deep learning and deepen your understanding of causality instead. The hardest parts of data science. You don’t need a data scientist (yet). Purpose, ethics, and my personal path.

Data Science 66

Data Science Predictive Modeling Experimentation Deep Learning 66

DataRobot

DECEMBER 16, 2021

Take advantage of DataRobot’s wide range of options for experimentation. Allow the platform to handle infrastructure and deep learning techniques so that you can maximize your focus on bringing value to your organization. Through the use of diverse feature types, you can observe a much broader perspective with your AI models.

Unstructured Data 98

Unstructured Data Experimentation Deep Learning Modeling 98

Domino Data Lab

JUNE 23, 2019

Pete Skomoroch ’s “ Product Management for AI ”session at Rev provided a “crash course” on what product managers and leaders need to know about shipping machine learning (ML) projects and how to navigate key challenges. Be aware that machine learning often involves working on something that isn’t guaranteed to work. Transcript.

Management 110

Management Machine Learning Experimentation Metrics 110

Dataiku

MARCH 25, 2024

By leveraging advanced deep learning architectures, M-LLMs can analyze the image and question simultaneously, extracting relevant features from both modalities and synthesizing them into a cohesive understanding. In our tests, we observed significant progress in both VQA and image captioning tasks.

Visualization 132

Visualization Modeling Experimentation Testing 132

Domino Data Lab

JULY 2, 2019

Part of the back-end processing needs deep learning (graph embedding) while other parts make use of reinforcement learning. Here’s a sampler of related papers and articles if you’d like to dig in further: “ Synthesizing Programs with Deep Learning ” – Nishant Sinha (2017-03-25). “ Software writes Software?

Metadata 105

Metadata Data Science Machine Learning Data-driven 105

CIO Business Intelligence

AUGUST 21, 2024

The flashpoint moment is that rather than being based on rules, statistics, and thresholds, now these systems are being imbued with the power of deep learning and deep reinforcement learning brought about by neural networks,” Mattmann says. Adding smarter AI also adds risk, of course.

Risk 136

Risk Insurance Cost-Benefit Software 136

DataRobot Blog

MARCH 7, 2023

DataRobot on Azure accelerates the machine learning lifecycle with advanced capabilities for rapid experimentation across new data sources and multiple problem types. The capability to rapidly build an AI-powered organization with industry-specific solutions and expertise.

Data-driven 59

Data-driven Machine Learning Experimentation Data Lake 59

Cloudera

FEBRUARY 16, 2022

They define each stage from data ingest, feature engineering, model building, testing, deployment and validation. Figure 04: Applied Machine Learning Prototypes (AMPs). Given the complexity of some ML models, especially those based on Deep Learning (DL) Convolutional Neural Networks (CNNs), there are limits to interpretability.

Machine Learning 85

Machine Learning Modeling Metadata Recreation/Entertainment 85

Domino Data Lab

MAY 8, 2019

For example, in the case of more recent deep learning work, a complete explanation might be possible: it might also entail an incomprehensible number of parameters. They also require advanced skills in statistics, experimental design, causal inference, and so on – more than most data science teams will have. Agile to the core.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

IBM Big Data Hub

APRIL 18, 2024

While leaders have some reservations about the benefits of current AI, organizations are actively investing in gen AI deployment, significantly increasing budgets, expanding use cases, and transitioning projects from experimentation to production. This personalized approach might lead to more effective therapies with fewer side effects.

Cost-Benefit 89

Cost-Benefit Manufacturing Modeling Interactive 89

Domino Data Lab

OCTOBER 14, 2020

Experimentation and collaboration are built into the core of the platform. We needed an “evolvable architecture” which would work with the next deep learning framework or compute platform. This ability enhances the efficiency of operational management and optimizes the cost of experimentation. Why Petastorm?

Experimentation 99

Experimentation Deep Learning Modeling Testing 99

Occam's Razor

OCTOBER 12, 2017

The tiny downside of this is that our parents likely never had to invest as much in constant education, experimentation and self-driven investment in core skills. When you go to the interview, the hiring company will proceed to ask questions that test your competency in the listed job requirements. Intro to Machine Learning.

Marketing 138

Marketing Analytics Machine Learning Strategy 138