CIO Business Intelligence

JANUARY 19, 2024

You should also have experience with pattern detection, experimentation in business optimization techniques, and time-series forecasting. and SAS Text Analytics, Time Series, Experimentation, and Optimization. The exam tests your knowledge of and ability to integrate machine learning into various tools and applications.

Data Science 126

Data Science Machine Learning Predictive Modeling Experimentation 126

CIO Business Intelligence

FEBRUARY 22, 2024

With the aim to accelerate innovation and transform its digital infrastructures and services, Ferrovial created its Digital Hub to serve as a meeting point where research and experimentation with digital strategies could, for example, provide new sources of income and improve company operations.

IT 105

IT Digital Transformation IoT Cost-Benefit 105

IBM Big Data Hub

MARCH 27, 2024

The experimental results indicate that fine-tuned LLMs exhibit significant improvements over the zero-shot classification approach. Here’s where deep learning-based foundation models for NLP can be efficient across a broad range of NLP classification tasks when availability of labelled data is insufficient or limited.

Experimentation 80

Experimentation Modeling Deep Learning Data-driven 80

Corinium

JUNE 6, 2019

It is also important to have a strong test and learn culture to encourage rapid experimentation. A playbook for this is to run multiple experiments in parallel and create ‘MVPs’ (fail/learn fast), as well as incorporate feedback mechanisms to enable an improvement loop, and scaling the ones that show the fastest path to ROI.

Insurance 250

Insurance Analytics Forecasting Deep Learning 250

IBM Big Data Hub

DECEMBER 5, 2023

According to IBM’s latest CEO study , industry leaders are increasingly focusing on AI technologies to drive revenue growth, with 42% of retail CEOs surveyed banking on AI technologies like generative AI, deep learning, and machine learning to deliver results over the next three years.

Unstructured Data 95

Unstructured Data Cost-Benefit Experimentation Machine Learning 95

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

Data Science and Beyond

DECEMBER 23, 2018

In my opinion it’s more exciting and relevant to everyday life than more hyped data science areas like deep learning. However, I’ve found it hard to apply what I’ve learned about causal inference to my work. I’ve been interested in the area of causal inference in the past few years.

Deep Learning 103

Deep Learning Experimentation Data-driven Data Science 103

Data Science and Beyond

SEPTEMBER 19, 2016

Causality and experimentation. Why you should stop worrying about deep learning and deepen your understanding of causality instead. The hardest parts of data science. You don’t need a data scientist (yet). Making Bayesian A/B testing more accessible. Purpose, ethics, and my personal path.

Data Science 66

Data Science Predictive Modeling Experimentation Deep Learning 66

Domino Data Lab

APRIL 7, 2019

A good NLP library will, for example, correctly transform free text sentences into structured features (like cost per hour and is diabetic ), that easily feed into a machine learning (ML) or deep learning (DL) pipeline (like predict monthly cost and classify high risk patients ). Image Credit: Parsa Ghaffari on the Raylien Blog.

Deep Learning 110

Deep Learning Machine Learning Experimentation Modeling 110

Cloudera

AUGUST 20, 2020

UOB used deep learning to improve detection of procurement fraud, thereby fighting financial crime. Acceptance that it will be an experiment — ML really requires a lot of experimentation, and often times you don’t know what’s going to be successful. So, the business has to accept and be willing to fail at it.

Enterprise 121

Enterprise Experimentation Uncertainty Deep Learning 121

Dataiku

MARCH 25, 2024

By leveraging advanced deep learning architectures, M-LLMs can analyze the image and question simultaneously, extracting relevant features from both modalities and synthesizing them into a cohesive understanding. Conclusion The landscape of VQA and image captioning using M-LLMs is vibrant and rapidly evolving.

Visualization 132

Visualization Modeling Experimentation Testing 132

CIO Business Intelligence

JUNE 12, 2023

Of course, he says, it’s interesting to try something experimental, but investing requires greater commitment to the business case. One way to distinguish the more serious players, he adds, could be that instead of speaking broadly about AI, to be more specific and talk about image analysis, natural language, or deep learning.

Sales 52

Sales Machine Learning Experimentation Deep Learning 52

O'Reilly on Data

APRIL 19, 2021

We’ll look at this later, but being able to reproduce experimental results is critical to any science, and it’s a well-known problem in AI. First, 82% of the respondents are using supervised learning, and 67% are using deep learning. 58% claimed to be using unsupervised learning. Bottlenecks to AI adoption.

Enterprise 356

Enterprise Modeling Risk Manufacturing 356

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

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.

Cost-Benefit 88

Cost-Benefit Modeling Manufacturing Interactive 88

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. The AMPs framework also supports the promotion of models from the lab into production, a common MLOps task.

Machine Learning 82

Machine Learning Modeling Metadata Recreation/Entertainment 82

Ontotext

NOVEMBER 6, 2019

At the same time, the community there is of users can share the best practices, enabling the cross-pollination of the most successful and most fruitful results of their experimentation. It also enables Ontotext to develop specific functionality as plugins without having to fiddle with the core functionality of the database. 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. Introduction.

Deep Learning 111

Deep Learning Modeling Metrics Testing 111

O'Reilly on Data

MARCH 18, 2020

Supervised learning is the most popular ML technique among mature AI adopters, while deep learning is the most popular technique among organizations that are still evaluating AI. It seems as if the experimental AI projects of 2019 have borne fruit. Supervised learning is dominant, deep learning continues to rise.

Enterprise 309

Enterprise Deep Learning Data Governance Risk 309

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.

IT 346

IT Testing Experimentation Software 346

Rocket-Powered Data Science

JULY 6, 2021

2) MLOps became the expected norm in machine learning and data science projects. MLOps takes the modeling, algorithms, and data wrangling out of the experimental “one off” phase and moves the best models into deployment and sustained operational phase.

Data-driven 279

Data-driven Machine Learning IoT Deep Learning 279

CIO Business Intelligence

MAY 23, 2022

The traditional approach for artificial intelligence (AI) and deep learning projects has been to deploy them in the cloud. For many nascent AI projects in the prototyping and experimentation phase, the cloud works just fine.

Cost-Benefit 98

Cost-Benefit Experimentation Data Lake Deep Learning 98

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. Automated development: With AutoAI , beginners can quickly get started and more advanced data scientists can accelerate experimentation in AI development.

Machine Learning 78

Machine Learning Manufacturing Cost-Benefit Deep Learning 78

DataRobot

DECEMBER 16, 2021

Take advantage of DataRobot’s wide range of options for experimentation. Allow the platform to handle infrastructure and deep learning techniques so that you can maximize your focus on bringing value to your organization. Through the use of diverse feature types, you can observe a much broader perspective with your AI models.

Experimentation 98

Experimentation Unstructured Data Deep Learning Modeling 98

DataKitchen

APRIL 13, 2021

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. Kubeflow — The Machine Learning Toolkit for Kubernetes.

Testing 307

Testing Machine Learning Consulting Data Quality 307

CDW Research Hub

AUGUST 21, 2019

Finally, perhaps the biggest obstacle faced when adopting an AI project is the time it takes to configure the server, prepare the data, build and train the model, and deploy and infer for deep learning. The kit helps facilitate clients’ AI adoption journey from experimentation to production.

Strategy 45

Strategy Deep Learning Machine Learning Experimentation 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

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

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

Smart Data Collective

DECEMBER 24, 2020

Students who want to throw their books away may want to hold off because machine learning cannot provide a high grade every single time. Machine learning remains highly experimental and much of its functionality remains in the development stages. Conclusion.

Machine Learning 95

Machine Learning Experimentation Software Technology 95

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

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. The capability to rapidly build an AI-powered organization with industry-specific solutions and expertise.

Data-driven 59

Data-driven Machine Learning Experimentation Data Lake 59

O'Reilly on Data

SEPTEMBER 11, 2019

Deep learning,” for example, fell year over year to No. For example, even though ML and ML-related concepts —a related term, “ML models,” (No. 106, +12) also improved, year over year—are rampant, ML-related tools and techniques are not. 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

Domino Data Lab

OCTOBER 14, 2020

Experimentation and collaboration are built into the core of the platform. We needed an “evolvable architecture” which would work with the next deep learning framework or compute platform. This ability enhances the efficiency of operational management and optimizes the cost of experimentation. Why Petastorm?

Experimentation 99

Experimentation Modeling Deep Learning Testing 99

O'Reilly on Data

JANUARY 7, 2020

In fact, in our 2019 surveys, more than half of the respondents said AI (deep learning, specifically) will be part of their future projects and products—and a majority of companies are starting to adopt machine learning. To stay competitive, data scientists need to at least dabble in machine and deep learning.

Deep Learning 178

Deep Learning Machine Learning Experimentation Modeling 178

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. Intro to Machine Learning. Machine Learning. Deep Learning. This reality powers my impostor syndrome, and (yet?) All three of these courses are free: 1.

Marketing 136

Marketing Analytics Machine Learning Strategy 136