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. For machine learning systems used in consumer internet companies, models are often continuously retrained many times a day using billions of entirely new input-output pairs.

Management 332

Management Machine Learning Experimentation Metrics 332

CIO Business Intelligence

APRIL 14, 2023

In fact, it’s likely your organization has a large number of employees currently experimenting with generative AI, and as this activity moves from experimentation to real-life deployment, it’s important to be proactive before unintended consequences happen. This may include developing training videos and hosting live sessions.

Risk 122

Risk Experimentation Marketing Risk Management 122

AWS Big Data

JANUARY 9, 2024

Traditional lexical search, based on term frequency models like BM25, is widely used and effective for many search applications. Semantic search In semantic search, the search engine uses an ML model to encode text or other media (such as images and videos) from the source documents as a dense vector in a high-dimensional vector space.

Cost-Benefit 91

Cost-Benefit Visualization Modeling Machine Learning 91

AWS Big Data

MAY 2, 2023

The workflow steps are as follows: The producer DAG makes an API call to a publicly hosted API to retrieve data. Test the feature To test this feature, run the producer DAG. Removal of experimental Smart Sensors. Test the feature Upload the four sample text files from the local data folder to an S3 bucket data folder.

Testing 100

Testing Experimentation Management Metadata 100

DataRobot

JUNE 10, 2021

Traditionally, experimentation and observation was the only way to understand the physical-chemical properties of the molecule. To foster innovation in this area, AICrowd hosted a competition to predict the olfactory properties of a molecule. The best model for this dataset is a Keras-based neural network.

Metrics 52

Metrics Machine Learning Visualization Experimentation 52

Domino Data Lab

MAY 8, 2019

Paco Nathan’s latest article features several emerging threads adjacent to model interpretability. I’ve been out themespotting and this month’s article features several emerging threads adjacent to the interpretability of machine learning models. Machine learning model interpretability. Introduction. 2018-06-21).

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

AWS Big Data

JULY 26, 2023

The vector engine supports the popular distance metrics such as Euclidean, cosine similarity, and dot product, and can accommodate 16,000 dimensions, making it well-suited to support a wide range of foundational and other AI/ML models. You can choose to host your collection on a public endpoint or within a VPC.

Metadata 52

Metadata Cost-Benefit Testing Metrics 52

DataRobot Blog

JANUARY 10, 2023

Most, if not all, machine learning (ML) models in production today were born in notebooks before they were put into production. Data science teams of all sizes need a productive, collaborative method for rapid AI experimentation. A host of open-source libraries. Capabilities Beyond Classic Jupyter for End-to-end Experimentation.

Experimentation 52

Experimentation Machine Learning Data Science Modeling 52

Cloudera

JUNE 6, 2019

The typical Cloudera Enterprise Data Hub Cluster starts with a few dozen nodes in the customer’s datacenter hosting a variety of distributed services. This duplication can become such a management nightmare that they may prefer to return to the original model and simply live with noisy neighbors. Cloudera Manager (CM) 6.2

Metadata 86

Metadata Data Lake Optimization Strategy 86

Domino Data Lab

JULY 2, 2019

Paco Nathan ‘s latest article covers program synthesis, AutoPandas, model-driven data queries, and more. Using ML models to search more effectively brought the search space down to 102—which can run on modest hardware. Model-Driven Data Queries. Introduction. BTW, videos for Rev2 are up: [link]. That’s impressive.

Metadata 105

Metadata Machine Learning Data Science Data-driven 105

DataRobot Blog

MARCH 16, 2023

release, we’ve made it easy for you to rapidly prepare data, engineer new features and subsequently automate model deployment and monitoring into your Snowflake data landscape, all with limited data movement. We’ve tightened the loop between ML data prep , experimentation and testing all the way through to putting models into production.

Modeling 52

Modeling Experimentation Machine Learning Data-driven 52

Occam's Razor

JUNE 4, 2018

Not easy, but your business model has to change to survive.). Social cues (/proof) can help create a sense of urgency for a whole host of companies. Such is the case with A/B testing. 800 plusses on Google+. Ok, so maybe not Google+ (I was genuinely excited about it, I am sad it died). But you get the idea.

Strategy 124

Strategy Cost-Benefit Testing Sales 124

Occam's Razor

DECEMBER 9, 2009

The four pronged real world tested probing and loaded with politics framework to find a home for Web Analytics: 1. Who owns the power to make changes to the site (not who owns updating pages or hosting the site)? Which physical organizational model will work best for you? Are there standard tools deployed? Centralized?

Analytics 95

Analytics Data-driven Marketing Sales 95

Occam's Razor

OCTOBER 12, 2017

The tiny downside of this is that our parents likely never had to invest as much in constant education, experimentation and self-driven investment in core skills. When you go to the interview, the hiring company will proceed to ask questions that test your competency in the listed job requirements. This is normal.

Marketing 136

Marketing Analytics Machine Learning Strategy 136

DataRobot

SEPTEMBER 14, 2021

And for those that do make it past the experimental stage, it typically takes over 18 months for the value to be realized. Accurate models today become inaccurate tomorrow. Models often degrade fast in production as they encounter unexpected conditions in the real world. Challenge to keep production models fair and ethical.

Machine Learning 94

Machine Learning Modeling Experimentation Visualization 94

DataRobot Blog

FEBRUARY 9, 2022

This approach gives freedom to move its AI artifacts around, regardless of whether they are hosted on a major cloud platform or its own on-premise infrastructure. Machine learning operations (MLOps) solutions allow all models to be monitored from a central location, regardless of where they are hosted or deployed.

IT 52

IT Experimentation Machine Learning Data Processing 52

DataRobot Blog

MARCH 7, 2023

Organizations that want to prove the value of AI by developing, deploying, and managing machine learning models at scale can now do so quickly using the DataRobot AI Platform on Microsoft Azure. 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