IBM Big Data Hub

FEBRUARY 23, 2024

Machine learning (ML) and deep learning (DL) form the foundation of conversational AI development. The technology’s ability to adapt and learn from interactions further refines customer support metrics, including response time, accuracy of information provided, customer satisfaction and problem-resolution efficiency.

Enterprise 96

Enterprise Interactive Data-driven Key Performance Indicator 96

Cloudera

FEBRUARY 16, 2022

They define each stage from data ingest, feature engineering, model building, testing, deployment and validation. Figure 04: Applied Machine Learning Prototypes (AMPs). Given the complexity of some ML models, especially those based on Deep Learning (DL) Convolutional Neural Networks (CNNs), there are limits to interpretability.

Machine Learning 85

Machine Learning Modeling Metadata Recreation/Entertainment 85

IBM Big Data Hub

OCTOBER 16, 2023

ML also provides the ability to closely monitor a campaign by checking open and clickthrough rates, among other metrics. Reinforcement learning uses ML to train models to identify and respond to cyberattacks and detect intrusions. Then, it can tailor marketing materials to match those interests.

Machine Learning 49

Machine Learning Marketing Forecasting Modeling 49

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 post An In-Depth View of Data Science appeared first on Data Science Blog by Domino.

Data Science 45

Data Science Machine Learning Statistics Deep Learning 45

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. We pass 3 parameters: loss, optimizer , and metrics. Choosing your evaluation metric Lastly, metrics just refers to your choice of evaluation metric.

Metrics 58

Metrics Optimization Modeling Testing 58

DataKitchen

APRIL 13, 2021

Read the complete blog below for a more detailed description of the vendors and their capabilities. Testing and Data Observability. It orchestrates complex pipelines, toolchains, and tests across teams, locations, and data centers. Testing and Data Observability. Production Monitoring and Development Testing.

Testing 300

Testing Machine Learning Consulting Data Quality 300

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. If not, take a look at the recording where we also cover a few of the points we’ll describe in this blog post. Maybe you also attended the webinar ?

Machine Learning 98

Machine Learning Measurement Deep Learning Manufacturing 98

The Unofficial Google Data Science Blog

NOVEMBER 17, 2020

On the other hand, sophisticated machine learning models are flexible in their form but not easy to control. This blog post motivates this problem more fully, and discusses monotonic splines and lattices as a solution. Curiosities and anomalies in your training and testing data become genuine and sustained loss patterns.

Machine Learning 108

Machine Learning Deep Learning Modeling Interactive 108

Domino Data Lab

APRIL 21, 2021

deep learning) there is no guaranteed explainability. In this blog post we will show an implementation of an ML-based anomaly detection based on XGBoost. from sklearn import metrics. This is to prevent any information leakage into our test set. 2f%% of the test set." 2f%% of the test set."

Statistics 69

Statistics Machine Learning Modeling Metrics 69

Domino Data Lab

AUGUST 10, 2021

PyTorch: used for deep learning models, like natural language processing and computer vision. It’s used for developing deep learning models. Horovod: is a distributed deep learning training framework that can be used with PyTorch, TensorFlow, Keras, and other tools.

Modeling 64

Modeling IT Risk Management Deep Learning 64

Sisense

AUGUST 6, 2020

AI and machine learning (ML) are not just catchy buzzwords; they’re vital to the future of our planet and your business. An obvious mechanical answer is: use relevance as a metric. Another important method is to benchmark existing metrics. Be sure test cases represent the diversity of app users. The perfect fit.

Management 95

Management Machine Learning Key Performance Indicator Cost-Benefit 95

Smarten

APRIL 19, 2024

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

Sisense

MAY 26, 2020

While training a model for NLP, words not present in the training data commonly appear in the test data. Because of this, predictions made using test data may not be correct. Using the semantic meaning of words it already knows as a base, the model can understand the meanings of words it doesn’t know that appear in test data.

Unstructured Data 98

Unstructured Data Big Data Testing Machine Learning 98

Insight

MARCH 25, 2020

In this blog, I will share how I built Pair , a scalable web application that takes in a product image, analyzes its design features using convolutional neural network, and recommends products in other categories with similar style elements. In this project, I was curious to see if deep learning approaches?—?specifically

Deep Learning 53

Deep Learning Metrics Data Processing Interactive 53

DataRobot

NOVEMBER 9, 2021

When we convert the single channel audio signal time series into an energy spectrogram, it allows us to run state of the art deep learning architectures on the image. . Please refer to our other blog on using DataRobot Visual AI on multi-modal datasets, which also includes text features. . Image courtesy towardsAI.

Visualization 52

Visualization Unstructured Data Statistics Metrics 52

DataRobot Blog

DECEMBER 15, 2022

In the background, models are being trained in parallel for efficiency and speed—from Tree-based models to Deep Learning models (which will be chosen based on your historical data and target variable) and more. The order of the models will be based on the project’s metric—and can be changed based on your configuration.

Forecasting 81

Forecasting Modeling ROI Machine Learning 81

DataRobot Blog

FEBRUARY 2, 2023

The creation of foundation models is one of the key developments in the field of large language models that is creating a lot of excitement and interest amongst data scientists and machine learning engineers. These models are trained on massive amounts of text data using deep learning algorithms.

Modeling 52

Modeling Machine Learning Data Processing Interactive 52

Domino Data Lab

MAY 8, 2019

Other good related papers include: “ Towards A Rigorous Science of Interpretable Machine Learning ”. Finale Doshi-Velez, Been Kim (2017-02-28) ; see also the Domino blog article about TCAV. Agile was originally about iterating fast on a code base and its unit tests, then getting results in front of stakeholders. 2018-06-21).

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

DataRobot Blog

DECEMBER 20, 2022

We’ll then empirically test this assumption based on an example of real estate asset assessment. By using this model, all accuracy metrics would also comply with national valuation regulations —as defined by the Bank of Spain.

Management 59

Management Machine Learning Optimization Modeling 59

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

DataRobot Blog

MARCH 7, 2023

DataRobot on Azure accelerates the machine learning lifecycle with advanced capabilities for rapid experimentation across new data sources and multiple problem types. This generates reliable business insights and sustains AI-driven value across the enterprise.

Data-driven 59

Data-driven Machine Learning Experimentation Data Lake 59