IBM Big Data Hub

APRIL 18, 2024

While these large language model (LLM) technologies might seem like it sometimes, it’s important to understand that they are not the thinking machines promised by science fiction. Most experts categorize it as a powerful, but narrow AI model. A key trend is the adoption of multiple models in production.

Cost-Benefit 95

Cost-Benefit Modeling Manufacturing Interactive 95

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. The company also lets AI make 3D models to follow a construction or streamline internal training. “We

Sales 52

Sales Machine Learning Experimentation Deep Learning 52

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

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

AUGUST 22, 2019

Many thanks to Addison-Wesley Professional for providing the permissions to excerpt “Natural Language Processing” from the book, Deep Learning Illustrated by Krohn , Beyleveld , and Bassens. The excerpt covers how to create word vectors and utilize them as an input into a deep learning model.

Deep Learning 111

Deep Learning Modeling Metrics Testing 111

O'Reilly on Data

OCTOBER 19, 2021

Let’s start by considering the job of a non-ML software engineer: writing traditional software deals with well-defined, narrowly-scoped inputs, which the engineer can exhaustively and cleanly model in the code. Not only is data larger, but models—deep learning models in particular—are much larger than before.

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 78

Machine Learning Manufacturing Cost-Benefit Deep Learning 78

DataKitchen

APRIL 13, 2021

DataOps needs a directed graph-based workflow that contains all the data access, integration, model and visualization steps in the data analytic production process. Observe, optimize, and scale enterprise data pipelines. . ModelOp — Governs, monitors, and orchestrates models across the enterprise. Meta-Orchestration .

Testing 307

Testing Machine Learning Consulting Data Quality 307

Domino Data Lab

JULY 2, 2019

Paco Nathan ‘s latest article covers program synthesis, AutoPandas, model-driven data queries, and more. 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. Introduction. BTW, videos for Rev2 are up: [link].

Metadata 105

Metadata Machine Learning Data Science Data-driven 105

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

Domino Data Lab

FEBRUARY 12, 2020

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. Motivations for Ray: Training a Reinforcement Learning (RL) Model. for hyper parameter tuning, and ? annotation.

Experimentation 137

Experimentation Modeling Data Science Management 137

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.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

O'Reilly on Data

SEPTEMBER 11, 2019

2 in frequency in proposal topics; a related term, “models,” is No. An ML-related topic, “models,” was No. For example, even though ML and ML-related concepts —a related term, “ML models,” (No. Deep learning,” for example, fell year over year to No. If anything, this focus has shifted to the ML or predictive model.

IoT 20

IoT Big Data Data Warehouse Uncertainty 20

Domino Data Lab

OCTOBER 14, 2020

We’ve developed a model-driven software platform, called Climate FieldView , that captures, visualizes, and analyzes a vast array of data for farmers and provides new insight and personalized recommendations to maximize crop yield. For example, our models can show farmers how to increase their production while using less fertilizer.

Experimentation 99

Experimentation Modeling Deep Learning Testing 99

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