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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DataRobot

FEBRUARY 10, 2021

Even if all the code runs and the model seems to be spitting out reasonable answers, it’s possible for a model to encode fundamental data science mistakes that invalidate its results. As a data scientist, one of my passions is to reproduce research papers as a learning exercise. Target Leakage in a fast.ai

Modeling 98

Modeling Deep Learning Machine Learning Testing 98

O'Reilly on Data

JULY 28, 2020

Product Managers are responsible for the successful development, testing, release, and adoption of a product, and for leading the team that implements those milestones. There’s a substantial literature about ethics, data, and AI, so rather than repeat that discussion, we’ll leave you with a few resources.

Marketing 362

Marketing Experimentation Metrics Testing 362

IBM Big Data Hub

FEBRUARY 13, 2024

Generative AI represents a significant advancement in deep learning and AI development, with some suggesting it’s a move towards developing “ strong AI.” Key considerations: Tech stack: Ensure your existing technology infrastructure can handle the demands of AI models and data processing.

Enterprise 118

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O'Reilly on Data

MARCH 7, 2019

Language understanding benefits from every part of the fast-improving ABC of software: AI (freely available deep learning libraries like PyText and language models like BERT ), big data (Hadoop, Spark, and Spark NLP ), and cloud (GPU's on demand and NLP-as-a-service from all the major cloud providers). IBM Watson NLU.

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CIO Business Intelligence

JUNE 1, 2022

Like professional basketball, industrial-scale farming, national politics, and global merchandising, auto racing has become a data science. To create a productive, cost-effective analytics strategy that gets results, you need high performance hardware that’s optimized to work with the software you use.

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Manufacturing Digital Transformation Predictive Analytics Strategy 116

CIO Business Intelligence

APRIL 25, 2022

As a result, data scientists often spend too much time on IT operations tasks, like figuring out how to allocate computing resources, rather than actually creating and training data science models. Ready to evolve your analytics strategy or improve your data quality? Dell infrastructure is also part of Intel’s cnvrg.io

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CIO Business Intelligence

JUNE 2, 2022

For optimizing existing resources, Eni uses HPC5 to model, study, and ultimately improve refinement operations. . Specifically, they are interested in electric utility response to cyber and physical threats, and they are working to develop an algorithm that can be used as a tested, trusted safeguard. HPCG [TFlop/s].

Informatics 98

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DataRobot

OCTOBER 20, 2021

What Is Data Literacy? Data literacy is the ability to understand data science and AI applications critically using basic data visualization, communication, and reasoning skills. And beyond uniting diverse stakeholders, data literacy also empowers sectoral regulators to provide industry-specific guidance.

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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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Metrics Machine Learning Statistics Risk 81

IBM Big Data Hub

APRIL 18, 2024

Building an in-house team with AI, deep learning , machine learning (ML) and data science skills is a strategic move. Most importantly, no matter the strength of AI (weak or strong), data scientists, AI engineers, computer scientists and ML specialists are essential for developing and deploying these systems.

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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.

Machine Learning 87

Machine Learning Modeling Metadata Recreation/Entertainment 87

IBM Big Data Hub

OCTOBER 16, 2023

Machine learning in marketing and sales According to Forbes , marketing and sales teams prioritize AI and ML more than any other enterprise department. Marketers use ML for lead generation, data analytics, online searches and search engine optimization (SEO). Computer vision fuels self-driving cars.

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Machine Learning Marketing Forecasting Modeling 49

Insight

APRIL 9, 2019

For industries outside of tech, ML can be utilized to personalize a user’s experience, automate laborious tasks and optimize subjective decision making. The biggest categories of cost for hardware designers and manufacturers are testing, verification, and calibration of their control systems.

Manufacturing 75

Manufacturing Machine Learning Testing Software 75

Domino Data Lab

DECEMBER 1, 2019

In the remainder of this article, we shall cover how to detect data drift for models that ingest image data as their input in order to prevent their silent degradation in production. Step 4: Generate the test, train and noisy MNIST data sets. Detecting image drift. x_test = x_test.astype('float32') / 255.

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Modeling Machine Learning Deep Learning Testing 77

DataKitchen

APRIL 13, 2021

Testing and Data Observability. We have also included vendors for the specific use cases of ModelOps, MLOps, DataGovOps and DataSecOps which apply DataOps principles to machine learning, AI, data governance, and data security operations. . Genie — Distributed big data orchestration service by Netflix.

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Cloudera

SEPTEMBER 12, 2018

It was deeply gratifying to see so many organizations deploying the tools and techniques of data science and advanced analytics to solve difficult and important problems. I predict that next year’s competition will be even more amazing as we continue pushing the frontiers of data science forward. Cloud Success: .

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Machine Learning Big Data Data Science Data Warehouse 46

Insight

JANUARY 14, 2020

Keras is an open source deep learning API that was written in Python and runs on top of Tensorflow, so it’s a little more user-friendly and high-level than Tensorflow. Choosing your loss function and optimizer Finally, in the last block of code, we must compile the model that we just built.

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O'Reilly on Data

OCTOBER 19, 2021

This introduces further requirements: The scale of operations is often two orders of magnitude larger than in the earlier data-centric environments. Not only is data larger, but models—deep learning models in particular—are much larger than before. Adapted from the book Effective Data Science Infrastructure.

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Domino Data Lab

AUGUST 22, 2019

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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Cloudera

NOVEMBER 3, 2021

Thanks to pioneers like Andrew NG and Fei-Fei Li, GPUs have made headlines for performing particularly well with deep learning techniques. Today, deep learning and GPUs are practically synonymous. While deep learning is an excellent use of the processing power of a graphics card, it is not the only use.

Deep Learning 120

Deep Learning Machine Learning Data Science Cost-Benefit 120

The Unofficial Google Data Science Blog

NOVEMBER 17, 2020

For example, consider the following simple example fitting a two-dimensional function to predict if someone will pass the bar exam based just on their GPA (grades) and LSAT (a standardized test) using the public dataset (Wightman, 1998). Curiosities and anomalies in your training and testing data become genuine and sustained loss patterns.

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Machine Learning Deep Learning Modeling Interactive 108

Domino Data Lab

MARCH 16, 2021

Lately, however, there is very exciting research emerging around building concepts from first principles with the goal of optimizing the higher layers to be human-readable. Instead of optimizing for pure accuracy, the network is constructed in a way that focuses on strong definitions of high-level concepts. Saliency Maps.

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Modeling Deep Learning Testing Optimization 122

Domino Data Lab

JULY 2, 2019

In other words, using metadata about data science work to generate code. In this case, code gets generated for data preparation, where so much of the “time and labor” in data science work is concentrated. Scale the problem to handle complex data structures. However, AutoPandas has a bolder agenda.

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Metadata Machine Learning Data Science Data-driven 105

Cloudera

FEBRUARY 1, 2022

At Cloudera, we provide our customers with an extensive library of prebuilt data science projects (complete with out of the box models and apps) called Applied ML Prototypes (AMPs) to help them move the starting point of their project closer to the finish line. Take a test drive to try AMPs for yourself. Launch the AMP.

Deep Learning 114

Deep Learning Machine Learning Modeling Data Science 114

DataRobot Blog

SEPTEMBER 13, 2022

Data scientists drive business outcomes. Many implement machine learning and artificial intelligence to tackle challenges in the age of Big Data. They develop and continuously optimize AI/ML models , collaborating with stakeholders across the enterprise to inform decisions that drive strategic business value.

Forecasting 52

Forecasting Data-driven Machine Learning Optimization 52

Domino Data Lab

MAY 8, 2019

The lens of reductionism and an overemphasis on engineering becomes an Achilles heel for data science work. Instead, consider a “full stack” tracing from the point of data collection all the way out through inference. Machine learning model interpretability. back to the structure of the dataset.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Domino Data Lab

APRIL 21, 2021

deep learning) there is no guaranteed explainability. We will go through a typical ML pipeline, where we do data ingestion, exploratory data analysis, feature engineering, model training and evaluation. This is to prevent any information leakage into our test set. 2f%% of the test set." Model training.

Statistics 69

Statistics Machine Learning Modeling Metrics 69

IBM Big Data Hub

JANUARY 10, 2024

Artificial intelligence (AI) refers to the convergent fields of computer and data science focused on building machines with human intelligence to perform tasks that would previously have required a human being. For example, learning, reasoning, problem-solving, perception, language understanding and more.

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Unstructured Data Deep Learning Cost-Benefit Machine Learning 76

BizAcuity

MARCH 31, 2022

you already have a data strategy in place, then it is easier to identify and analyze where AI would be the most useful for your business.Analytics Insight has an informative blog on the wide range of use-cases of AI in prominent industries. Integrating IoT and route optimization are two other important places that use AI.

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Domino Data Lab

JULY 9, 2019

Chris Wiggins , Chief Data Scientist at The New York Times, presented “Data Science at the New York Times” at Rev. Wiggins also indicated that data science, data engineering, and data analysis are different groups at The New York Times. Data science. Session Summary.

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Domino Data Lab

JANUARY 14, 2019

Next, we load the Boston Housing data, the same dataset we used in Part 1. Let’s build the models that we’ll use to test SHAP and LIME. xgb_model = xgb.train({'objective':'reg:linear'}, xgb.DMatrix(X_train, label=y_train)) # GBT from scikit-learn? X,y = shap.datasets.boston()?X_train,X_test,y_train,y_test random_state=0).

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Testing Modeling Deep Learning Data Science 89

Domino Data Lab

MARCH 3, 2019

Paco Nathan covers recent research on data infrastructure as well as adoption of machine learning and AI in the enterprise. Welcome back to our monthly series about data science! This month, the theme is not specifically about conference summaries; rather, it’s about a set of follow-up surveys from Strata Data attendees.

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

Domino Data Lab

MARCH 29, 2021

Maintaining the cluster and the underlying infrastructure configuration can be a complex and time-consuming task Lack of GPU acceleration – Complex machine workloads, especially the ones involving Deep Learning, benefit from GPU architectures that are well adapted for vector and matrix operations. GPU) and use bitnami/spark:2.4.6

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Data Science 101

NOVEMBER 13, 2023

I had the opportunity to beta test a bunch of their offerings and became a JQL, Jira and Confluence expert which helped me out tremendously PM at a Fintech (anti-money laundering, DS, cyber security) company – this is where I found my love for complex data analytics, building dashboards, queries and spreading my “intrinsic motivation” ideals.

B2B 59

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IBM Big Data Hub

AUGUST 1, 2023

It’s the underlying engine that gives generative models the enhanced reasoning and deep learning capabilities that traditional machine learning models lack. Dev Developers can write, test and document faster using AI tools that generate custom snippets of code.

Modeling 55

Modeling Cost-Benefit Data Lake Machine Learning 55

Alation

APRIL 14, 2022

A useful feature for exposing patterns in the data. Supports the ability to interact with the actual data and perform analysis on it. Automatic sampling to test transformation. Similar to a data warehouse schema, this prep tool automates the development of the recipe to match. Visual Profiling. Scheduling.

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Metadata Data Governance Modeling Data-driven 59