CIO Business Intelligence

JUNE 12, 2023

Shoots wide of the target With generative AI, where functionality can be built into other parts, the focus is now on things like predictive analysis and energy optimization by finding deviations in the property data that Bravida collects. But maybe the next step for salespeople will be to learn it too.”

Sales 52

Sales Machine Learning Experimentation Deep Learning 52

O'Reilly on Data

MARCH 31, 2020

Because it’s so different from traditional software development, where the risks are more or less well-known and predictable, AI rewards people and companies that are willing to take intelligent risks, and that have (or can develop) an experimental culture. Managing Machine Learning Projects” (AWS). People + AI Guidebook” (Google).

Management 332

Management Machine Learning Experimentation Metrics 332

IBM Big Data Hub

APRIL 18, 2024

While leaders have some reservations about the benefits of current AI, organizations are actively investing in gen AI deployment, significantly increasing budgets, expanding use cases, and transitioning projects from experimentation to production. AGI wouldn’t just perceive its surroundings; it would understand them.

Cost-Benefit 87

Cost-Benefit Modeling Manufacturing Interactive 87

Domino Data Lab

JULY 2, 2019

SQL optimization provides helpful analogies, given how SQL queries get translated into query graphs internally , then the real smarts of a SQL engine work over that graph. Part of the back-end processing needs deep learning (graph embedding) while other parts make use of reinforcement learning. Software writes Software?

Metadata 105

Metadata Machine Learning Data Science Data-driven 105

Domino Data Lab

FEBRUARY 12, 2020

for reinforcement learning (RL), ? for model serving (experimental), are implemented with Ray internally for its scalable, distributed computing and state management benefits, while providing a domain-specific API for the purposes they serve. A research paper describing the flexible primitives internal to Ray for deep learning.

Experimentation 137

Experimentation Modeling Data Science Management 137

FineReport

DECEMBER 19, 2019

When it comes to data analysis, from database operations, data cleaning, data visualization , to machine learning, batch processing, script writing, model optimization, and deep learning, all these functions can be implemented with Python, and different libraries are provided for you to choose. From Google.

Knowledge Discovery 98

Knowledge Discovery Visualization Experimentation Reporting 98

Cloudera

FEBRUARY 16, 2022

Support for multiple sessions within a project allows data scientists, engineers and operations teams to work independently alongside each other on experimentation, pipeline development, deployment and monitoring activities in parallel. CML now supports experiment tracking using MLflow. .

Machine Learning 82

Machine Learning Modeling Metadata Recreation/Entertainment 82

Ontotext

NOVEMBER 6, 2019

By taking the open source approach, the Workbench can address a wider spectrum of use-cases, creating a higher value for clients and increasing the likelihood that specific non-generic features exist and have been developed to address the real-world problems facing the optimization of semantic data processing and management. The Plugins.

Experimentation 45

Experimentation Deep Learning Software Modeling 45

Domino Data Lab

JUNE 23, 2019

Pete Skomoroch ’s “ Product Management for AI ”session at Rev provided a “crash course” on what product managers and leaders need to know about shipping machine learning (ML) projects and how to navigate key challenges. It used deep learning to build an automated question answering system and a knowledge base based on that information.

Management 110

Management Machine Learning Experimentation Metrics 110

O'Reilly on Data

OCTOBER 19, 2021

ML apps need to be developed through cycles of experimentation: due to the constant exposure to data, we don’t learn the behavior of ML apps through logical reasoning but through empirical observation. Not only is data larger, but models—deep learning models in particular—are much larger than before. Model Operations.

IT 346

IT Testing Experimentation Software 346

IBM Big Data Hub

OCTOBER 20, 2023

Artificial intelligence platforms enable individuals to create, evaluate, implement and update machine learning (ML) and deep learning models in a more scalable way. This unified experience optimizes the process of developing and deploying ML models by streamlining workflows for increased efficiency.

Machine Learning 77

Machine Learning Manufacturing Cost-Benefit Deep Learning 77

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. They also require advanced skills in statistics, experimental design, causal inference, and so on – more than most data science teams will have.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

DataKitchen

APRIL 13, 2021

Observe, optimize, and scale enterprise data pipelines. . Metis Machine — Enterprise-scale Machine Learning and Deep Learning deployment and automation platform for rapid deployment of models into existing infrastructure and applications. Polyaxon — An open-source platform for reproducible machine learning at scale.

Testing 307

Testing Machine Learning Consulting Data Quality 307

O'Reilly on Data

SEPTEMBER 11, 2019

Deep learning,” for example, fell year over year to No. It’s more difficult to monitor, control, and optimize data flows in a data-in-motion paradigm. For example, even though ML and ML-related concepts —a related term, “ML models,” (No. 40; it peaked at Strata NY 2018 at No. Neural network” also fell slightly from 2018 (No.

IoT 20

IoT Big Data Data Warehouse Uncertainty 20

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. Optimal Starting SCOTUS Starting Points. Intro to Machine Learning. Machine Learning. Deep Learning. Raich and the use of the Commerce Clause.

Marketing 136

Marketing Analytics Machine Learning Strategy 136