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

JULY 28, 2020

The first step in building an AI solution is identifying the problem you want to solve, which includes defining the metrics that will demonstrate whether you’ve succeeded. It sounds simplistic to state that AI product managers should develop and ship products that improve metrics the business cares about. Agreeing on metrics.

Marketing 363

Marketing Experimentation Metrics Testing 363

Rocket-Powered Data Science

DECEMBER 15, 2018

This wisdom applies not only to life but to machine learning also. Specifically, the availability and application of labeled data (things past) for the labeling of previously unseen data (things future) is fundamental to supervised machine learning. A related problem also arises in unsupervised machine learning.

Machine Learning 171

Machine Learning Optimization Metrics Modeling 171

DataKitchen

APRIL 13, 2021

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. . Dagster / ElementL — A data orchestrator for machine learning, analytics, and ETL. . Collaboration and Sharing.

Testing 300

Testing Machine Learning Consulting Data Quality 300

DataRobot

FEBRUARY 10, 2021

Machine learning is a glass cannon. The promise and power of AI lead many researchers to gloss over the ways in which things can go wrong when building and operationalizing machine learning models. As a data scientist, one of my passions is to reproduce research papers as a learning exercise.

Modeling 98

Modeling Deep Learning Machine Learning Testing 98

O'Reilly on Data

MARCH 31, 2020

If you’re already a software product manager (PM), you have a head start on becoming a PM for artificial intelligence (AI) or machine learning (ML). AI products are automated systems that collect and learn from data to make user-facing decisions. We won’t go into the mathematics or engineering of modern machine learning here.

Management 339

Management Machine Learning Experimentation Metrics 339

Business Over Broadway

DECEMBER 20, 2018

The importance of data science and machine learning continues to grow in business and beyond. Favorite Data Science and Machine Learning Blogs, Podcasts and Newsletters – In a worldwide survey, over 16,000 data professionals were asked to indicate their favorite data science blogs, podcasts and newsletters.

Machine Learning 74

Machine Learning Data Science Deep Learning Predictive Analytics 74

Domino Data Lab

FEBRUARY 10, 2019

This blog post provides insights into why machine learning teams have challenges with managing machine learning projects. Why are Machine Learning Projects so Hard to Manage? I’ve watched lots of companies attempt to deploy machine learning?—?some Why is this?

Machine Learning 63

Machine Learning Modeling Deep Learning Metrics 63

IBM Big Data Hub

NOVEMBER 29, 2023

With the emergence of new advances and applications in machine learning models and artificial intelligence, including generative AI, generative adversarial networks, computer vision and transformers, many businesses are seeking to address their most pressing real-world data challenges using both types of synthetic data: structured and unstructured.

Metrics 80

Metrics Machine Learning Statistics Risk 80

O'Reilly on Data

JUNE 19, 2019

A look at the landscape of tools for building and deploying robust, production-ready machine learning models. Our surveys over the past couple of years have shown growing interest in machine learning (ML) among organizations from diverse industries. Why aren’t traditional software tools sufficient?

Modeling 196

Modeling Machine Learning Testing Metrics 196

Smarten

APRIL 19, 2024

Benefits include customized and optimized models, data, parameters and tuning. Anomaly Alerts KPI monitoring and Auto Insights allows business users to quickly establish KPIs and target metrics and identify the Key Influencers and variables for the target KPI.

Analytics 57

Analytics Predictive Modeling KPI Machine Learning 57

IBM Big Data Hub

AUGUST 11, 2023

Machine learning (ML), a subset of artificial intelligence (AI), is an important piece of data-driven innovation. Machine learning engineers take massive datasets and use statistical methods to create algorithms that are trained to find patterns and uncover key insights in data mining projects. What is MLOps?

Data Science 53

Data Science Machine Learning Cost-Benefit Deep Learning 53

CIO Business Intelligence

JUNE 21, 2022

In addition to quantitative ROI metrics, HPC research was also shown to save lives, lead to important public/private partnerships, and spur innovations. . Real-time big data analytics, deep learning, and modeling and simulation are newer uses of HPC that governments are embracing for a variety of applications. HPC Growth in U.S.

Deep Learning 92

Deep Learning Big Data ROI Software 92

DataRobot

OCTOBER 20, 2021

In order to scale responsible AI, organizations should implement these fundamental building blocks of data literacy: The data science and machine learning workflow: Learning about the steps required to create predictions from raw data helps stakeholders develop an understanding of AI project implementation.

Machine Learning 52

Machine Learning Metrics Risk Deep Learning 52

Domino Data Lab

AUGUST 1, 2021

The interest in interpretation of machine learning has been rapidly accelerating in the last decade. This can be attributed to the popularity that machine learning algorithms, and more specifically deep learning, has been gaining in various domains. Methods for explaining Deep Learning.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

IBM Big Data Hub

SEPTEMBER 6, 2023

AI marketing is the process of using AI capabilities like data collection, data-driven analysis, natural language processing (NLP) and machine learning (ML) to deliver customer insights and automate critical marketing decisions. AI can help marketers create and optimize content to meet the new standards. What is AI marketing?

Marketing 78

Marketing Technology Advertising ROI 78

O'Reilly on Data

JANUARY 25, 2021

We’re not pretending the frameworks themselves are comparable—Spring is primarily for backend and middleware development (though it includes a web framework); React and Angular are for frontend development; and scikit-learn and PyTorch are machine learning libraries. AI, Machine Learning, and Data.

Machine Learning 363

Machine Learning Software Testing Technology 363

CIO Business Intelligence

JUNE 1, 2022

and artificial intelligence (AI) and machine learning (ML) technologies. . Aside from monitoring components over time, sensors also capture aerodynamics, tire pressure, handling in different types of terrain, and many other metrics. which are virtual models of objects, systems, or processes ? Just starting out with analytics?

Manufacturing 98

Manufacturing Digital Transformation Predictive Analytics Strategy 98

IBM Big Data Hub

AUGUST 28, 2023

Text mining —also called text data mining—is an advanced discipline within data science that uses natural language processing (NLP) , artificial intelligence (AI) and machine learning models, and data mining techniques to derive pertinent qualitative information from unstructured text data.

Data mining 53

Data mining Machine Learning Deep Learning Marketing 53

CIO Business Intelligence

JANUARY 4, 2023

Workflow Optimization from Edge to Core/Cloud and Back : Integration with edge devices as well as integration with different HPC systems is currently designed in-house or otherwise customized. Advances in Artificial Intelligence and Machine Learning (AI/ML): AI/ML will continue growing as an important workload in HPC.

Cost-Benefit 59

Cost-Benefit Machine Learning Modeling Uncertainty 59

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. Be aware that machine learning often involves working on something that isn’t guaranteed to work.

Management 110

Management Machine Learning Experimentation Metrics 110

IBM Big Data Hub

FEBRUARY 23, 2024

Machine learning (ML) and deep learning (DL) form the foundation of conversational AI development. It signifies a shift in human-digital interaction, offering enterprises innovative ways to engage with their audience, optimize operations, and further personalize their customer experience. billion by 2030.

Enterprise 95

Enterprise Interactive Data-driven Key Performance Indicator 95

AWS Big Data

OCTOBER 26, 2023

Some recent examples of performance optimizations driven by fleet telemetry include: String query optimizations – By analyzing how Amazon Redshift processed different data types in the Redshift fleet, we found that optimizing string-heavy queries would bring significant benefit to our customers’ workloads.

Data Warehouse 95

Data Warehouse Cost-Benefit Dashboards Optimization 95

Domino Data Lab

JULY 28, 2021

Communication cannot be emphasized enough, for it is this trait that ensures results are effectively translated from the white board to impact on business metrics. In fact, deep learning was first described theoretically in 1943. The most commonly used techniques today are under the umbrella of machine learning.

Data Science 45

Data Science Machine Learning Statistics Deep Learning 45

Domino Data Lab

SEPTEMBER 9, 2019

Data science teams in industry must work with lots of text, one of the top four categories of data used in machine learning. You could cluster (k=2) on NPS scores (a customer evaluation metric) then replace the Democrat/Republican dimension with the top two components from the clustering. for a, b in pairs:?. print(a, b, lic[a].similarity(lic[b])).

Deep Learning 110

Deep Learning Machine Learning Data Science Visualization 110

Domino Data Lab

AUGUST 29, 2019

This article provides an excerpt “Deep Reinforcement Learning” from the book, Deep Learning Illustrated by Krohn , Beyleveld , and Bassens. The article includes an overview of reinforcement learning theory with focus on the deep Q-learning. Essential Theory of Reinforcement Learning.

Optimization 111

Optimization Modeling Deep Learning Metrics 111

Domino Data Lab

APRIL 21, 2021

In this article, we’ll discuss the challenge organizations face around fraud detection, how machine learning can be used to identify and spot anomalies that the human eye might not catch. deep learning) there is no guaranteed explainability. from sklearn import metrics. from datetime import datetime.

Statistics 69

Statistics Machine Learning Modeling Metrics 69

IBM Big Data Hub

DECEMBER 20, 2023

Machine learning (ML) technologies can drive decision-making in virtually all industries, from healthcare to human resources to finance and in myriad use cases, like computer vision , large language models (LLMs), speech recognition, self-driving cars and more. What is machine learning?

Machine Learning 74

Machine Learning Modeling Deep Learning Predictive Modeling 74

Domino Data Lab

MAY 8, 2019

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. Other good related papers include: “ Towards A Rigorous Science of Interpretable Machine Learning ”. Not yet, if ever.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Domino Data Lab

MARCH 3, 2019

Paco Nathan covers recent research on data infrastructure as well as adoption of machine learning and AI in the enterprise. O’Reilly Media published our analysis as free mini-books: The State of Machine Learning Adoption in the Enterprise (Aug 2018). What metrics are used to evaluate success? Introduction.

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

DataRobot Blog

APRIL 22, 2022

In our previous post , we talked about how red AI means adding computational power to “buy” more accurate models in machine learning , and especially in deep learning. This view actually delivers four out of the five efficiency metrics that we discussed in the previous blog post.

Machine Learning 98

Machine Learning Measurement Deep Learning Manufacturing 98

Insight

JANUARY 14, 2020

Keras is an open source deep learning API that was written in Python and runs on top of Tensorflow, so it’s a little more user-friendly and high-level than Tensorflow. Choosing your loss function and optimizer Finally, in the last block of code, we must compile the model that we just built.

Metrics 58

Metrics Optimization Modeling Testing 58

Domino Data Lab

JULY 22, 2019

why data governance, in the context of machine learning is no longer a “dry topic” and how the WSJ’s “global reckoning on data governance” is potentially connected to “premiums on leveraging data science teams for novel business cases”. But for most enterprise, using machine learning…not really.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

Edwin Chen

OCTOBER 6, 2014

Click-through rate may be your initial hope…but after a bit of thought, it's not clear that it's the best metric after all. Metrics like CTR, or even number of favorites and retweets, will probably optimize for showing quick one-liners and pictures of funny cats. Amazon, and Moving Beyond Log-Based Metrics.

Metrics 79

Metrics Measurement Machine Learning Testing 79

Insight

MARCH 28, 2019

Like Derek, the first step Yanxia took was to classify each review’s sentences into topic categories, but she opted to use a machine learning-based approach to identify 14 latent topics. Yanxia then used a combination of quantitative and qualitative metrics to interpret each topic group identified by LDA.

Dashboards 70

Dashboards Visualization Data Science Machine Learning 70

DataRobot Blog

DECEMBER 20, 2022

In this context, an augmented intelligence approach around the data will be increasingly more critical for asset managers, investors, and real estate developers to ensure a better understanding of the real estate assets and take better decisions aimed at optimizing both the Net Asset Value and the Net Operating Income.

Management 59

Management Machine Learning Optimization Modeling 59

IBM Big Data Hub

NOVEMBER 14, 2023

Internal data monetization initiatives measure improvement in process design, task guidance and optimization of data used in the organization’s product or service offerings. Realize: Instrument the data product services to enable adherence to risk and compliance controls with event and metrics data integrated to financial management.

Strategy 104

Strategy Cost-Benefit Data-driven Measurement 104