O'Reilly on Data

DECEMBER 12, 2019

In addition to newer innovations, the practice borrows from model risk management, traditional model diagnostics, and software testing. Security vulnerabilities : adversarial actors can compromise the confidentiality, integrity, or availability of an ML model or the data associated with the model, creating a host of undesirable outcomes.

Machine Learning 192

Machine Learning Modeling Testing Risk Management 192

IBM Big Data Hub

JULY 6, 2023

An online hospitality company uses data science to ensure diversity in its hiring practices, improve search capabilities and determine host preferences, among other meaningful insights. The evolution of machine learning The start of machine learning, and the name itself, came about in the 1950s.

Machine Learning 56

Machine Learning Data Science Statistics Deep Learning 56

IBM Big Data Hub

APRIL 18, 2024

According to Andreessen Horowitz (link resides outside IBM.com ) , in 2023, the average spend on foundation model application programming interfaces (APIs), self-hosting and fine-tuning models across surveyed companies reached USD 7 million. This personalized approach might lead to more effective therapies with fewer side effects.

Cost-Benefit 93

Cost-Benefit Modeling Manufacturing Interactive 93

Smart Data Collective

FEBRUARY 20, 2022

With these tools, your SaaS can: Merge and improve the application code constantly Automate the development, testing, and release of software Integrate operations and developer workflows And much more. AWS also offers developers the technology to develop smart apps using machine learning and complex algorithms. Easy to use.

Cost-Benefit 114

Cost-Benefit Data Lake Software Machine Learning 114

CIO Business Intelligence

JUNE 2, 2022

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. Known as the most powerful supercomputer in academia, Frontera is hosted by the Texas Advanced Computing Center (TACC) at the University of Texas, Austin.

Informatics 98

Informatics Modeling Testing Technology 98

DataRobot Blog

MAY 26, 2022

These methods provided the benefit of being supported by rich literature on the relevant statistical tests to confirm the model’s validity—if a validator wanted to confirm that the input predictors of a regression model were indeed relevant to the response, they need only to construct a hypothesis test to validate the input.

Risk 52

Risk Modeling Metrics Business Objectives 52

CDW Research Hub

JULY 31, 2019

Level 5 and beyond : at this level, contextual assistants are able to monitor and manage a host of other assistants in order to run certain aspects of enterprise operations. Recent advances in machine learning, and more specifically its subset, deep learning, have made it possible for computers to better understand natural language.

Deep Learning 49

Deep Learning Machine Learning Testing Modeling 49

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. . Testing and Data Observability. Production Monitoring and Development Testing.

Testing 300

Testing Machine Learning Consulting Data Quality 300

O'Reilly on Data

MARCH 31, 2020

But there’s a host of new challenges when it comes to managing AI projects: more unknowns, non-deterministic outcomes, new infrastructures, new processes and new tools. A lot to learn, but worthwhile to access the unique and special value AI can create in the product space. Managing Machine Learning Projects” (AWS).

Management 332

Management Machine Learning Experimentation Metrics 332

Cloudera

SEPTEMBER 12, 2018

AbbVie’s platform uses analytics and machine learning, including natural language processing, deep learning, and unsupervised learning, to proactively identify issues and opportunities. Brian Carpenter , Co-Host, The Hot Aisle Podcast, @intheDC. Cloud Success: . FairVentures Lab (nominated by Cazena ).

Machine Learning 45

Machine Learning Big Data Data Science Data Warehouse 45

Domino Data Lab

APRIL 3, 2019

Fun fact : I co-founded an e-commerce company (realistically, a mail-order catalog hosted online) in December 1992 using one of those internetworking applications called Gopher , which was vaguely popular at the time. Does machine learning change priorities? In short, the virtuous cycle is growing. We keep feeding the monster data.

Data Governance 84

Data Governance Machine Learning Metadata Big Data 84

DataRobot Blog

FEBRUARY 2, 2023

The creation of foundation models is one of the key developments in the field of large language models that is creating a lot of excitement and interest amongst data scientists and machine learning engineers. These models are trained on massive amounts of text data using deep learning algorithms. pip install transformers==4.25.1

Modeling 52

Modeling Machine Learning Data Processing Interactive 52

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

Domino Data Lab

JANUARY 14, 2019

The notebook is hosted on Domino’s trial site. 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? To keep it simple, I choose to explain the first record in the test set for each model using SHAP and LIME. #

Testing 89

Testing Modeling Deep Learning Data Science 89

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

Cost-Benefit 98

Cost-Benefit Machine Learning Testing Interactive 98

Domino Data Lab

MAY 8, 2019

Machine learning model interpretability. At CMU I joined a panel hosted by Zachary Lipton where someone in the audience asked a question about machine learning model interpretation. Agile was originally about iterating fast on a code base and its unit tests, then getting results in front of stakeholders.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Domino Data Lab

JULY 2, 2019

Part of the back-end processing needs deep learning (graph embedding) while other parts make use of reinforcement learning. Here’s a sampler of related papers and articles if you’d like to dig in further: “ Synthesizing Programs with Deep Learning ” – Nishant Sinha (2017-03-25). “ Software writes Software?

Metadata 105

Metadata Machine Learning Data Science Data-driven 105

DataRobot Blog

DECEMBER 20, 2022

We’ll then empirically test this assumption based on an example of real estate asset assessment. Because our training dataset is multimodal and contains imagery data of residential properties in Madrid, DataRobot used machine learning models that contain deep learning based image featurizers.

Management 59

Management Machine Learning Optimization Modeling 59

Domino Data Lab

JULY 9, 2019

He advocated that an impactful ML solution does not end with Google Slides but becomes “a working API that is hosted or a GUI or some piece of working code that people can put to work” Wiggins also dove into examples of applying unsupervised, supervised, and reinforcement learning to address business problems.

Data Science 112

Data Science Machine Learning Advertising Modeling 112

DataRobot Blog

MARCH 7, 2023

DataRobot on Azure accelerates the machine learning lifecycle with advanced capabilities for rapid experimentation across new data sources and multiple problem types. Models trained in DataRobot can also be easily deployed to Azure Machine Learning, allowing users to host models easier in a secure way.

Data-driven 59

Data-driven Machine Learning Experimentation Data Lake 59

O'Reilly on Data

JANUARY 25, 2021

Both SRE and DevOps emphasize similar practices: version control (62% growth for GitHub, and 48% for Git), testing (high usage, though no year-over-year growth), continuous deployment (down 20%), monitoring (up 9%), and observability (up 128%). It’s particularly difficult if testing includes issues like fairness and bias.

Machine Learning 363

Machine Learning Software Testing Technology 363

Occam's Razor

OCTOBER 12, 2017

When you go to the interview, the hiring company will proceed to ask questions that test your competency in the listed job requirements. Test for analytics experience AND explore the level of analytical thinking the job candidate possesses. Intro to Machine Learning. Machine Learning. Deep Learning.

Marketing 136

Marketing Analytics Machine Learning Strategy 136