Remove Deep Learning Remove Experimentation Remove Visualization
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Interview with: Sankar Narayanan, Chief Practice Officer at Fractal Analytics

Corinium

It is also important to have a strong test and learn culture to encourage rapid experimentation. A playbook for this is to run multiple experiments in parallel and create ‘MVPs’ (fail/learn fast), as well as incorporate feedback mechanisms to enable an improvement loop, and scaling the ones that show the fastest path to ROI.

Insurance 250
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Deep Learning Illustrated: Building Natural Language Processing Models

Domino Data Lab

Many thanks to Addison-Wesley Professional for providing the permissions to excerpt “Natural Language Processing” from the book, Deep Learning Illustrated by Krohn , Beyleveld , and Bassens. The excerpt covers how to create word vectors and utilize them as an input into a deep learning model. Introduction.

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The DataOps Vendor Landscape, 2021

DataKitchen

DataOps needs a directed graph-based workflow that contains all the data access, integration, model and visualization steps in the data analytic production process. Polyaxon — An open-source platform for reproducible machine learning at scale. Kubeflow — The Machine Learning Toolkit for Kubernetes. Meta-Orchestration .

Testing 300
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Enterprise Data Science Workflows with AMPs and Streamlit

Cloudera

Here in the virtual Fast Forward Lab at Cloudera , we do a lot of experimentation to support our applied machine learning research, and Cloudera Machine Learning product development. We believe the best way to learn what a technology is capable of is to build things with it.

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AI Adoption in the Enterprise 2021

O'Reilly on Data

We’ll look at this later, but being able to reproduce experimental results is critical to any science, and it’s a well-known problem in AI. First, 82% of the respondents are using supervised learning, and 67% are using deep learning. 58% claimed to be using unsupervised learning. Bottlenecks to AI adoption.

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Demystifying Multimodal LLMs

Dataiku

Moreover, M-LLMs adeptly answer questions about visual content, aiding in tasks like image recognition and scene understanding. Additionally, we’ll explore their proficiency in tasks such as generating descriptive captions for images and answering questions about visual content.

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12 data science certifications that will pay off

CIO Business Intelligence

You should also have experience with pattern detection, experimentation in business optimization techniques, and time-series forecasting. and SAS Text Analytics, Time Series, Experimentation, and Optimization. The exam tests your knowledge of and ability to integrate machine learning into various tools and applications.