Domino Data Lab

JULY 28, 2021

Data science is a field at the convergence of statistics, computer science and business. Its value is so significant that scaling data science has become the new business imperative with organizations spending tens of millions of dollars on data, technology and talent. Data Science Techniques.

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DataKitchen

FEBRUARY 1, 2021

The list of possible issues is long, but you might hear feedback that includes: Data science/engineering/analytic teams do not deliver the insight that the business customers need. The data team takes too long to deliver analytics. Users mistrust the data itself or the team working on the data.

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Cloudera

JUNE 26, 2023

Here are some tips and tricks of the trade to prevent well-intended yet inappropriate data engineering and data science activities from cluttering or crashing the cluster. For data engineering and data science teams, CDSW is highly effective as a comprehensive platform that trains, develops, and deploys machine learning models.

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IBM Big Data Hub

NOVEMBER 29, 2023

They are already identifying and exploring several real-life use cases for synthetic data, such as: Generating synthetic tabular data to increase sample size and edge cases. You can combine this data with real datasets to improve AI model training and predictive accuracy.

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Cloudera

MAY 22, 2018

Cloudera Data Science Workbench (CDSW) makes secure, collaborative data science at scale a reality for the enterprise and accelerates the delivery of new data products. track model metrics, performance, and any model artifacts the user specifies. let the user document, test, and share the model.

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The Unofficial Google Data Science Blog

JANUARY 16, 2018

We present data from Google Cloud Platform (GCP) as an example of how we use A/B testing when users are connected. Experimentation on networks A/B testing is a standard method of measuring the effect of changes by randomizing samples into different treatment groups. Assume we have $K$ users.

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DataKitchen

JANUARY 3, 2022

For example, teams working under the VP/Directors of Data Analytics may be tasked with accessing data, building databases, integrating data, and producing reports. Data scientists derive insights from data while business analysts work closely with and tend to the data needs of business units.

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Domino Data Lab

MAY 1, 2019

This Domino Data Science Field Note provides highlights and excerpted slides from Chloe Mawer ’s “ The Ingredients of a Reproducible Machine Learning Model ” talk at a recent WiMLDS meetup. Mawer is a Principal Data Scientist at Lineage Logistics as well as an Adjunct Lecturer at Northwestern University.

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Cloudera

APRIL 9, 2021

This is part 4 in this blog series. This blog series follows the manufacturing and operations data lifecycle stages of an electric car manufacturer – typically experienced in large, data-driven manufacturing companies. The second blog dealt with creating and managing Data Enrichment pipelines.

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DataRobot

JUNE 29, 2021

Above image shows a DataRobot MLOps retraining policy set to trigger when data drift occurs. As part of the same process, it also generates and tests a whole host of new models and presents the top ones as recommended challengers. If I’ve done my job in this blog post, then you should want to learn more. Over and over.

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Sisense

AUGUST 14, 2020

By using a visual representation of code, the virus code can be detected without running the code and endangering the test system. This is because a specialized context narrows the scope of the training data used in training the dialog algorithm. SmartBear and Tricentis enhance Selenium to provide AI-driven app testing automation.

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Domino Data Lab

JULY 17, 2021

While more advanced models for speaker verification exist, this blog will form a basis of speech signal processing. NOTE : The scope of this blog is a machine learning approach to TISV. VoxCeleb1 dev test # of speakers 1,211 40 # of videos 21,819 677 # of utterances 148,642 4,874. hours long from around 60 speakers.

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Sisense

JANUARY 21, 2020

Everyone wants to get more out of their data, but how exactly to do that can leave you scratching your head. One of the simplest ways to start exploring your data is to aggregate the metrics you are interested in by their relevant dimensions. A new drug promising to reduce the risk of heart attack was tested with two groups.

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Smarten

APRIL 12, 2023

Smarten CEO, Kartik Patel says, ‘Smarten SnapShot supports the evolving role of Citizen Data Scientists with interactive tools that allow a business user to gather information, establish metrics and key performance indicators.’

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Cloudera

FEBRUARY 16, 2022

In the case of CDP Public Cloud, this includes virtual networking constructs and the data lake as provided by a combination of a Cloudera Shared Data Experience (SDX) and the underlying cloud storage. Each project consists of a declarative series of steps or operations that define the data science workflow.

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The Unofficial Google Data Science Blog

OCTOBER 31, 2016

By PATRICK RILEY For a number of years, I led the data science team for Google Search logs. We were often asked to make sense of confusing results, measure new phenomena from logged behavior, validate analyses done by others, and interpret metrics of user behavior. Something like histograms, CDFs, Q-Q plots, etc.

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The Unofficial Google Data Science Blog

JANUARY 14, 2016

by AMIR NAJMI Running live experiments on large-scale online services (LSOS) is an important aspect of data science. In this post we explore how and why we can be “ data-rich but information-poor ”. There are many reasons for the recent explosion of data and the resulting rise of data science.

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Juice Analytics

MARCH 11, 2021

However, the data was essentially stored in old copies of the paper magazine, not a format that was conducive to delivering insights to their target audience. (3) Pain Points We’ve noticed a temptation with data products to forget the cardinal rule of any product: it needs to solve a specific problem. It's a big load.

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Insight

OCTOBER 25, 2019

Image credit: [link] As the title suggests, this is a story about a question that may resonate well with many machine learning practitioners trying to build applications in the real world, where clean and annotated data on a specific problem can be sparse— How do we leverage the power of AI when we have very little data?

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IBM Big Data Hub

OCTOBER 16, 2023

Marketers use ML for lead generation, data analytics, online searches and search engine optimization (SEO). ML algorithms and data science are how recommendation engines at sites like Amazon, Netflix and StitchFix make recommendations based on a user’s taste, browsing and shopping cart history.

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DataKitchen

APRIL 13, 2021

Read the complete blog below for a more detailed description of the vendors and their capabilities. This is not surprising given that DataOps enables enterprise data teams to generate significant business value from their data. Testing and Data Observability. Testing and Data Observability.

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bridgei2i

SEPTEMBER 1, 2020

BRIDGEi2i’s Data Science expertise, portfolio of AI-powered solutions, and consumer industry context have helped enterprises build customer loyalty, drive incremental sales growth, get more value for marketing spend, and enhance supply chain planning and operation through AI Accelerators. These are: Watchtower.

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The Unofficial Google Data Science Blog

OCTOBER 7, 2015

A/B testing is used widely in information technology companies to guide product development and improvements. For questions as disparate as website design and UI, prediction algorithms, or user flows within apps, live traffic tests help developers understand what works well for users and the business, and what doesn’t.

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DataRobot Blog

MARCH 10, 2022

With Snowflake’s newest feature release, Snowpark , developers can now quickly build and scale data-driven pipelines and applications in their programming language of choice, taking full advantage of Snowflake’s highly performant and scalable processing engine that accelerates the traditional data engineering and machine learning life cycles.

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Insight

JANUARY 14, 2020

We pass 3 parameters: loss, optimizer , and metrics. If you’d like to read more about all of the different optimizers out there, check out this excellent Medium article and this wonderfully detailed blog post. Choosing your evaluation metric Lastly, metrics just refers to your choice of evaluation metric.

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Smarten

MAY 18, 2022

When a business sets goals and establishes metrics to determine the value of an analytical solution and a business user analytics initiative within the enterprise, the management team often fails to focus on the more subtle, but powerful, concept of efficacy.

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DataKitchen

MAY 10, 2021

And the worst part – data errors take the fun out of data science. Remember your first data science courses? You probably imagined your career would be about helping drive insights with data instead of having to sit in endless meetings discussing analytics errors and painstaking corrective actions.

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The Unofficial Google Data Science Blog

JULY 17, 2018

In this blog post we describe one of these instances — Google search deciding when to check if web pages have changed. Through this example, we discuss some of the special considerations impacting a data scientist when designing solutions to improve decision-making deep within software infrastructure.

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The Unofficial Google Data Science Blog

SEPTEMBER 23, 2015

How can we establish that there is a causal link between our idea and the outcome metric we care about? It's why estimating causal effects has been at the heart of data science. Some ideas, though, cannot be tested through randomized experiments. And witness the emergence of new data science paradigms along the way.

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datapine

OCTOBER 21, 2020

Summing up the product of all this work, the data science team developed a web-based user interface that forecasts patient loads and helps in planning resource allocation by utilizing online data visualization that reaches the goal of improving the overall patients’ care. 2) Electronic Health Records (EHRs).

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O'Reilly on Data

OCTOBER 13, 2020

In Bringing an AI Product to Market , we distinguished the debugging phase of product development from pre-deployment evaluation and testing. During testing and evaluation, application performance is important, but not critical to success. Debugging AI Products. Inference Task Speed and SLOs.

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The Unofficial Google Data Science Blog

APRIL 23, 2024

the weight given to Likes in our video recommendation algorithm) while $Y$ is a vector of outcome measures such as different metrics of user experience (e.g., This blog post discusses such a comprehensive approach that is used at Youtube. the fraction of video recommendations resulted in positive user experiences).

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The Unofficial Google Data Science Blog

DECEMBER 4, 2019

by THOMAS OLAVSON Thomas leads a team at Google called "Operations Data Science" that helps Google scale its infrastructure capacity optimally. It also owns Google’s internal time series forecasting platform described in an earlier blog post. Our team does a lot of forecasting. Our team does a lot of forecasting.

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Cloudera

AUGUST 6, 2018

Fox Foundation is testing a watch-type wearable device in Australia to continuously monitor the symptoms of patients with Parkinson’s disease. This is important because unlike diabetes or high blood pressure we don’t yet have clear metrics for Parkinson’s. The post The Value of Data for Philanthropy appeared first on Cloudera Blog.

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Cloudera

JUNE 10, 2022

For data scientists, we continue to provide new Applied Machine Learning Prototypes (AMPs), which are open source and available on GitHub. These pre-built reference examples are complete end-to-end data science projects. To learn more about video classification, check out this blog from our team at Fast Forward Labs.

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O'Reilly on Data

MAY 14, 2020

This includes tools for model development (such as the Cloudera Data Science Workbench ) and production serving infrastructure (such as Seldon and TFX ). data platform, metrics, ML/AI research, and applied ML). According to VentureBeat , fewer than 15% of Data Science projects actually make it into production.

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DataKitchen

NOVEMBER 18, 2021

Write tests that catch data errors. Build observability and transparency into your end-to-end data pipelines. We talk about systemic change, and it certainly helps to have the support of management, but data engineers should not underestimate the power of the keyboard. Automate manual processes. Implement DataOps methods.

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