Insight

MAY 8, 2019

Because data scientists generally don’t have an infrastructure background, they often struggle to build out servers and use tools to visualize their modeling and analysis. When you inevitably want your app hosted on the internet, your options, for the most part, fall into two buckets: Managed services (e.g. Check it out on GitHub.

Data Processing 72

Data Processing Testing Data Science Visualization 72

Cloudera

FEBRUARY 8, 2023

How Bad is Bad Code: The ROI of Fixing Broken Spark Code Once in a while I stumble upon Spark code that looks like it has been written by a Java developer and it never fails to make me wince because it is a missed opportunity to write elegant and efficient code: it is verbose, difficult to read, and full of distributed processing anti-patterns.

Measurement 56

Measurement Testing Cost-Benefit ROI 56

Domino Data Lab

APRIL 3, 2019

That’s a lot of priorities – especially when you group together closely related items such as data lineage and metadata management which rank nearby. Changes in system architecture get reflected as substantial changes in how we collect, use, and manage data, and therefore become drivers for DG. Introduction.

Data Governance 84

Data Governance Machine Learning Metadata Big Data 84

O'Reilly on Data

MARCH 18, 2020

Last year, when we felt interest in artificial intelligence (AI) was approaching a fever pitch, we created a survey to ask about AI adoption. When we analyzed the results , we determined the AI space was in a state of rapid change, so we eagerly commissioned a follow-up survey to help find out where AI stands right now.

Enterprise 313

Enterprise Deep Learning Data Governance Risk 313

AWS Big Data

FEBRUARY 8, 2023

One challenge you may face when performing analytics is the presence of imperfect duplicate records within the source data. Answering questions as simple as “How many unique customers do we have?” can be very challenging when the data you have available is like the following table.

Data Quality 76

Data Quality Testing Data Warehouse Unstructured Data 76

AWS Big Data

AUGUST 10, 2023

Trusted across various industries, Amazon MWAA helps organizations like Siemens , ENGIE , and Choice Hotels International enhance and scale their business workflows, while significantly improving security and reducing infrastructure management overhead. For details on how to create and use a notifier, refer to Creating a notifier.

Management 78

Management Software Testing Modeling 78

AWS Big Data

SEPTEMBER 7, 2023

Customers of all sizes and industries use Amazon Simple Storage Service (Amazon S3) to store data globally for a variety of use cases. Customers want to know how their data is being accessed, when it is being accessed, and who is accessing it. A log file may not capture all log records for requests made up to that point.

Metadata 99

Metadata Dashboards Metrics Visualization 99

Occam's Razor

FEBRUARY 11, 2008

It will soon be a year of working at Google and milestones are always a good time for introspection. I have a lot on my mind but there was one thing in particular that I wanted to share with you all: What it is has been like working at Google. Interesting, fun, surprising, insightful, inspiring, impactful, and more such words. 1: The brand. +. . #

Consulting 139

Consulting Visualization Recreation/Entertainment IT 139

AWS Big Data

FEBRUARY 21, 2023

Solutions Architect – AWS SafeGraph is a geospatial data company that curates over 41 million global points of interest (POIs) with detailed attributes, such as brand affiliation, advanced category tagging, and open hours, as well as how people interact with those places. The Spark platform is the saw.

Cost-Benefit 55

Cost-Benefit Informatics Optimization Management 55

Domino Data Lab

FEBRUARY 12, 2020

Dean Wampler provides a distilled overview of Ray, an open source system for scaling Python systems from single machines to large clusters. If your team has started using ? If you’re wondering if Ray should be part of your technical strategy for Python-based applications, especially ML and AI, this post is for you. Introduction.

Experimentation 137

Experimentation Modeling Data Science Management 137

Domino Data Lab

AUGUST 29, 2019

It also covers using Keras to construct a deep Q-learning network that learns within a simulated video game environment. From games to simulating evolution, reinforcement learning has been used as a tool to explore emergent behavior. Using Keras and OpenAI Gym to create a deep learning model and provide the environment to train.

Optimization 111

Optimization Modeling Deep Learning Metrics 111

Domino Data Lab

APRIL 6, 2021

They were expert Python programmers, but they didn’t want to spend lots of time and energy using most toolkits available. Out of this need emerged Ray , an open-source system for scaling Python (and now Java) applications from single machines to large clusters. Why Do We Need Ray? We could start as follows.

Data Science 81

Data Science Optimization Machine Learning Modeling 81

Cloudera

AUGUST 4, 2022

This article reveals how Z-ordering works and how one can use it with Apache Impala. This can commonly happen in active archive and operational reporting use cases. This will backfire when you run queries that need to scan a large portion of the table. But what if we wanted to understand how well version 5.16

IoT 102

IoT Measurement Statistics Cost-Benefit 102

DataRobot Blog

DECEMBER 15, 2022

While AI-powered forecasting can help retailers implement sales and demand forecasting—this process is very complex, and even highly data-driven companies face key challenges: Scale: Thousands of item combinations make it difficult to manually build predictive models. The process I will present will be using the DataRobot GUI.

Forecasting 81

Forecasting Modeling ROI Machine Learning 81

Domino Data Lab

MAY 8, 2019

Several technology conferences all occurred within four fun-filled weeks: Strata SF , Google Next , CMU Summit on US-China Innovation, AI NY , and Strata UK , plus some other events. Paco Nathan’s latest article features several emerging threads adjacent to model interpretability. Introduction. back to the structure of the dataset. .

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Domino Data Lab

JANUARY 14, 2019

This blog post provides insights on how to use the SHAP and LIME Python libraries in practice and how to interpret their output, helping readers prepare to produce model explanations in their own work. In Part 2 we explore these libraries in more detail by applying them to a variety of Python models. Introduction.

Testing 89

Testing Modeling Deep Learning Data Science 89

Domino Data Lab

FEBRUARY 4, 2019

This month, let’s explore two themes in detail: take an in-depth look at Rev 2 and exploring how curiosity plays a vital, and perhaps non-intuitive, role in data science work. We’ll unpack curiosity as a core attribute of effective data science, look at how that informs process for data science (in contrast to Agile, etc.), Of course!!”

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

Domino Data Lab

AUGUST 26, 2019

This article provides an excerpt of “Tuning Hyperparameters and Pipelines” from the book, Machine Learning with Python for Everyone by Mark E. Data scientists and researchers ascertain predictive accuracy of models using different techniques, methodologies, and settings, including model parameters and hyperparameters. Introduction.

Testing 79

Testing Modeling Machine Learning Metrics 79

Domino Data Lab

AUGUST 20, 2019

Many thanks to AWP Pearson for the permission to excerpt “Manual Feature Engineering: Manipulating Data for Fun and Profit” from the book, Machine Learning with Python for Everyone by Mark E. Feature engineering is useful for data scientists when assessing tradeoff decisions regarding the impact of their ML models.

Testing 68

Testing Modeling Interactive Measurement 68

The Unofficial Google Data Science Blog

AUGUST 31, 2016

While MapReduce remains a fundamental tool, many interesting analyses require more than it can offer. Going forward, Apache Beam will become the way to express data processing pipelines, while Cloud Dataflow remains as a fully managed service for executing these pipelines.

Data Science 66

Data Science Sales Optimization Cost-Benefit 66

Domino Data Lab

AUGUST 22, 2019

The excerpt covers how to create word vectors and utilize them as an input into a deep learning model. While the field of computational linguistics, or Natural Language Processing (NLP), has been around for decades, the increased interest in and use of deep learning models has also propelled applications of NLP forward within industry.

Deep Learning 111

Deep Learning Modeling Metrics Testing 111

CIO Business Intelligence

FEBRUARY 3, 2023

Enter robotic process automation (RPA) : a smart set of tools that deploys AI and low-code options to simplify workflows and save everyone time while also adding safeguards that can prevent costly mistakes. Even the modern workplace can be boring and repetitive. What is RPA? Still, RPA isn’t automatic. But you won’t have to do much of this.

Data-driven 145

Data-driven Interactive Enterprise Statistics 145

Edwin Chen

MAY 29, 2017

How do we do this? LSTMs are behind a lot of the amazing achievements deep learning has made in the past few years, and they're a fairly simple extension to neural networks under the right view. So I'll try to present them as intuitively as possible – in such a way that you could have discovered them yourself. Neural Networks.

Modeling 94

Modeling IT Deep Learning Testing 94