The Unofficial Google Data Science Blog

NOVEMBER 17, 2020

by TAMAN NARAYAN & SEN ZHAO A data scientist is often in possession of domain knowledge which she cannot easily apply to the structure of the model. On the one hand, basic statistical models (e.g. On the other hand, sophisticated machine learning models are flexible in their form but not easy to control.

Machine Learning 108

Machine Learning Deep Learning Modeling Interactive 108

CIO Business Intelligence

JANUARY 23, 2023

As OpenAI’s exclusive cloud provider it will see additional revenue for its Azure services, as one of OpenAI’s biggest costs is providing the computing capacity to train and run its AI models. The risk there — aside from making the internet useless to humans — is that it will pollute the very resource needed to train better AIs.

Enterprise 130

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Corinium

JUNE 6, 2019

Regulations and compliance requirements, especially around pricing, risk selection, etc., Beyond that, we recommend setting up the appropriate data management and engineering framework including infrastructure, harmonization, governance, toolset strategy, automation, and operating model. In addition, the traditional challenges remain.

Insurance 250

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CIO Business Intelligence

JUNE 7, 2022

Those numbers represent the projected growth of chatbot interactions among banking customers between 2019 to 2023 and the cost savings from 862 hours less of work by support personnel, according to research by Juniper Research. In business, when a trend is forecast to grow by more than 3000% and generate cost savings of $7.3

Unstructured Data 87

Unstructured Data Deep Learning Insurance Interactive 87

CIO Business Intelligence

APRIL 15, 2022

Derek Driggs, a machine learning researcher at the University of Cambridge, together with his colleagues, published a paper in Nature Machine Intelligence that explored the use of deep learning models for diagnosing the virus. The algorithm learned to identify children, not high-risk patients.

Analytics 144

Analytics Machine Learning Insurance Testing 144

Domino Data Lab

OCTOBER 9, 2019

In some cases, their work is vital when millions of lives may be at risk due to natural disasters. We had working sessions about how to retrofit the saildrones with low-power ML on edge devices, using transfer learning with deep learning models to detect and avoid hazards from sea ice. AI CA 2019 highlights.

Deep Learning 69

Deep Learning Metadata Machine Learning Data Science 69

O'Reilly on Data

MARCH 31, 2020

Instead of writing code with hard-coded algorithms and rules that always behave in a predictable manner, ML engineers collect a large number of examples of input and output pairs and use them as training data for their models. The model is produced by code, but it isn’t code; it’s an artifact of the code and the training data.

Management 327

Management Machine Learning Experimentation Metrics 327

O'Reilly on Data

JANUARY 25, 2021

Year-over-year (YOY) growth compares January through September 2020 with the same months of 2019. Let’s look at the data, starting at the highest level: O’Reilly online learning itself. O’Reilly Online Learning. Usage of O’Reilly online learning grew steadily in 2020, with 24% growth since 2019.

Machine Learning 362

Machine Learning Software Testing Technology 362

Domino Data Lab

MAY 8, 2019

Paco Nathan’s latest article features several emerging threads adjacent to model interpretability. I’ve been out themespotting and this month’s article features several emerging threads adjacent to the interpretability of machine learning models. Machine learning model interpretability. Introduction.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Domino Data Lab

JULY 2, 2019

Paco Nathan ‘s latest article covers program synthesis, AutoPandas, model-driven data queries, and more. See also: Caroline Lemieux’s slides for that NeurIPS talk, and Rohan Bavishi’s video from the RISE Summer Retreat 2019. Introduction. Welcome back to our monthly burst of themespotting and conference summaries.

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Metadata Machine Learning Data Science Data-driven 105

Domino Data Lab

MARCH 3, 2019

O’Reilly Media published our analysis as free mini-books: The State of Machine Learning Adoption in the Enterprise (Aug 2018). Evolving Data Infrastructure: Tools and Best Practices for Advanced Analytics and AI (Jan 2019). AI Adoption in the Enterprise: How Companies Are Planning and Prioritizing AI Projects in Practice (Feb 2019).

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

BizAcuity

MARCH 31, 2022

of organizations who participated in an executive survey back in 2019 claimed they are going to be investing in big data and AI. 85% of AI (marketing) projects fail due to risk, confusion, and lack of upskilling among marketing teams. (Source: Gartner Research). Source: TCS). Source: Gartner Research). AI in Supply chain and Logistics.

Data Analytics 52

Data Analytics Analytics Prescriptive Analytics Predictive Analytics 52

O'Reilly on Data

SEPTEMBER 11, 2019

Our call for speakers for Strata NY 2019 solicited contributions on the themes of data science and ML; data engineering and architecture; streaming and the Internet of Things (IoT); business analytics and data visualization; and automation, security, and data privacy. 2 in frequency in proposal topics; a related term, “models,” is No.

IoT 20

IoT Big Data Data Warehouse Uncertainty 20

Smart Data Collective

OCTOBER 14, 2019

Ludwig is a tool that allows people to build data-based deep learning models to make predictions. In September 2019, Google decided to make it’s Differential Privacy Library available as an open-source tool. Here are some open-source options to consider. Kubernetes.

Data Science 67

Data Science Machine Learning Big Data Interactive 67

Domino Data Lab

APRIL 3, 2019

Data governance shows up as the fourth-most-popular kind of solution that enterprise teams were adopting or evaluating during 2019. Plus, the more mature machine learning (ML) practices place greater emphasis on these kinds of solutions than the less experienced organizations. Newer work in machine learning (e.g.,

Data Governance 84

Data Governance Machine Learning Metadata Big Data 84