O'Reilly on Data

JANUARY 25, 2021

This study is based on title usage on O’Reilly online learning. The data includes all usage of our platform, not just content that O’Reilly has published, and certainly not just books. It’s particularly difficult if testing includes issues like fairness and bias.

Machine Learning 363

Machine Learning Software Testing Technology 363

Domino Data Lab

AUGUST 10, 2021

PyTorch: used for deep learning models, like natural language processing and computer vision. It’s used for developing deep learning models. Horovod: is a distributed deep learning training framework that can be used with PyTorch, TensorFlow, Keras, and other tools.

Modeling 64

Modeling IT Risk Management Deep Learning 64

Domino Data Lab

JULY 15, 2021

Originally created for software development, Python is used in a variety of contexts, including deep learning research and model deployment. IDEs (also sometimes referred to as notebooks) is a coding tool that makes it easier to develop and test code. Launchpad: Allows you to publish your deployed models in different formats.

Data Science 145

Data Science Visualization Modeling Enterprise 145

Domino Data Lab

MARCH 29, 2021

Some of the key benefits of using Spark for machine learning include: Distributed Learning – Parallelize compute-heavy workloads such as distributed training or hyper-parameter tuning Interactive Exploratory Analysis – Efficiently load large data sets in a distributed manner. GPU) and use bitnami/spark:2.4.6

Cost-Benefit 98

Cost-Benefit Machine Learning Testing Interactive 98

Insight

MARCH 25, 2020

After reading this, I hope you can learn how to build deep learning models using TensorFlow Keras, productionalize the model as a Streamlit app, and deploy it as a Docker container on Google Cloud Platform (GCP) using Google Kubernetes Engines (GKE). In this project, I was curious to see if deep learning approaches?—?specifically

Deep Learning 53

Deep Learning Metrics Data Processing Interactive 53

Insight

APRIL 9, 2019

As a Data Product Management Fellow at Insight , I worked with a machine learning engineering Fellow, simulation engineer, and a propulsion engineer to improve efficiency in manufacturing rocket engines. 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

Insight

MAY 7, 2019

This API also takes an S3 bucket name as input and then performs inference on all inputs in the test file. The user interacts with the platform using the APIs which start the training or batch inference in the background using Celery. The platform was tested on an Amazon p2.xlarge

Modeling 75

Modeling Testing Machine Learning Interactive 75

Cloudera

SEPTEMBER 12, 2018

Commonwealth Bank of Australia worked with Cloudera to implement a modern data platform with an AI-powered customer decisioning layer that dramatically improves how the bank interacts with its customers. Bob Gourley , Founder Crucial Point LLC and Publisher, CTOvision.com, ThreadBrief.com, @BobGourley. Cloud Success: .

Machine Learning 44

Machine Learning Big Data Data Science Data Warehouse 44

Domino Data Lab

JULY 9, 2019

A “data scientist” might build a multistage processing pipeline in Python, design a hypothesis test, perform a regression analysis over data samples with R, design and implement an algorithm in Hadoop, or communicate the results of our analyses to other members of the organization in a clear and concise fashion.

Data Science 112

Data Science Machine Learning Advertising Modeling 112

Domino Data Lab

MAY 8, 2019

For example, in the case of more recent deep learning work, a complete explanation might be possible: it might also entail an incomprehensible number of parameters. Agile was originally about iterating fast on a code base and its unit tests, then getting results in front of stakeholders. Generally, you cannot get both.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Domino Data Lab

APRIL 3, 2019

That resulted in server farms, collecting volumes of log data from customer interactions, data which was then aggregated and fed into machine learning algorithms which created data products as pre-computed results, which in turn made web apps smarter and enhanced e-commerce revenue. Agile Manifesto get published.

Data Governance 84

Data Governance Machine Learning Metadata Big Data 84