CIO Business Intelligence

OCTOBER 13, 2023

Machine learning engineer Machine learning engineers are tasked with transforming business needs into clearly scoped machine learning projects, along with guiding the design and implementation of machine learning solutions.

Deep Learning 143

Deep Learning Machine Learning Modeling Software 143

Analytics Vidhya

JANUARY 2, 2024

Introduction In this technologically advanced era, programming languages come and go, but Python has stood the test of time, emerging as a titan in coding. Its simplicity, versatility, and robust community support have made it the go-to language for beginners and experts alike.

Testing 181

Testing Technology Analytics IT 181

Domino Data Lab

JANUARY 20, 2019

What if there was a way to quantitatively measure whether your machine learning (ML) model reflects specific domain expertise or potential bias? Testing with Concept Activation Vectors (TCAV): The Zebra. This blog post provided distilled highlights and excerpts from recent research on TCAV. Introduction.

Testing 63

Testing Modeling Machine Learning Data Science 63

Analytics Vidhya

JANUARY 2, 2024

Introduction In this technologically advanced era, programming languages come and go, but Python has stood the test of time, emerging as a titan in coding. Its simplicity, versatility, and robust community support have made it the go-to language for beginners and experts alike.

Testing 143

Testing Technology Analytics IT 143

Insight

SEPTEMBER 20, 2019

In this blog, I’ll describe how this project uses segmentation to detect lesions in an image, and classification to detect whether those lesions are benign or malignant. The box plot below shows a summary of the testing results. This shows that the model indeed learned where and what to look for in the images. on test data.

Deep Learning 53

Deep Learning Cost-Benefit Testing Modeling 53

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 56

Data Science Machine Learning Cost-Benefit Deep Learning 56

IBM Big Data Hub

JULY 11, 2023

Better machine learning (ML) algorithms, more access to data, cheaper hardware and the availability of 5G have contributed to the increasing application of AI in the healthcare industry, accelerating the pace of change. US, German and French researchers used deep learning on more than 100,000 images to identify skin cancer.

Deep Learning 57

Deep Learning Cost-Benefit Insurance Machine Learning 57

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 83

Metrics Machine Learning Risk Statistics 83

IBM Big Data Hub

FEBRUARY 13, 2024

Generative AI represents a significant advancement in deep learning and AI development, with some suggesting it’s a move towards developing “ strong AI.” Generative AI uses advanced machine learning algorithms and techniques to analyze patterns and build statistical models.

Enterprise 122

Enterprise Modeling Testing Data-driven 122

IBM Big Data Hub

SEPTEMBER 19, 2023

It uses deep learning algorithms and large neural networks trained on vast datasets of diverse existing source code. Some generative AI for code tools automatically create unit tests to help with this. Generative AI can quickly and efficiently test and debug computer code. How does generative AI code generation work?

Software 84

Software IT Visualization Deep Learning 84

IBM Big Data Hub

JULY 6, 2023

To keep up with the pace of consumer expectations, companies are relying more heavily on machine learning algorithms to make things easier. This blog post will clarify some of the ambiguity. How do artificial intelligence, machine learning, deep learning and neural networks relate to each other?

Deep Learning 59

Deep Learning Machine Learning Unstructured Data Modeling 59

Smart Data Collective

FEBRUARY 20, 2022

With these tools, your SaaS can: Merge and improve the application code constantly Automate the development, testing, and release of software Integrate operations and developer workflows And much more. AWS also offers developers the technology to develop smart apps using machine learning and complex algorithms. Conclusions.

Cost-Benefit 115

Cost-Benefit Data Lake Software Machine Learning 115

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 ). Functionality comparison cheat sheet.

Deep Learning 110

Deep Learning Machine Learning Experimentation Modeling 110

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. What is AI marketing?

Marketing 82

Marketing Technology Advertising ROI 82

Domino Data Lab

JULY 28, 2021

To model anything highly technical and computationally — machine learning, deep learning, big data analytics, and natural-language processing, to name a few — code-based tools (such as R and Python) are usually preferred. After cleaning, the data is now ready for processing.

Data Science 110

Data Science Statistics Recreation/Entertainment Predictive Analytics 110

CIO Business Intelligence

APRIL 24, 2023

How we got here The most notable enabling technologies in generative AI are deep learning, embeddings, transfer learning (all of which emerged in the early to mid-2000s), and neural net transformers (invented in 2017). Deep learning emerged in academia in the early 2000s, with broader industry adoption starting around 2010.

Enterprise 52

Enterprise Deep Learning Modeling Machine Learning 52

Insight

APRIL 9, 2019

NASA [Public domain] In this blog, I’ll discuss how I worked collaboratively with various domain experts, using reinforcement learning to develop innovative solutions in rocket engine development. The biggest categories of cost for hardware designers and manufacturers are testing, verification, and calibration of their control systems.

Manufacturing 75

Manufacturing Machine Learning Testing Software 75

Smarten

JUNE 14, 2023

While it has only recently received public attention, this deep learning model architecture was launched more than five years ago by The American Artificial Intelligence Organization (OpenAI), and recent innovations like ChatGPT are based on the latest version of GPT (GPT-4).

Optimization 52

Optimization IT Digital Transformation Consulting 52

Domino Data Lab

JULY 17, 2021

While more advanced models for speaker verification exist, this blog will form a basis of speech signal processing. Further, deep learning methods are built on the foundation of signal processing. NOTE : The scope of this blog is a machine learning approach to TISV. hours long from around 60 speakers.

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Modeling Testing Machine Learning Measurement 64

Insight

OCTOBER 25, 2019

Image credit: [link] As the title suggests, this is a story about a question that may resonate well with many machine learning practitioners trying to build applications in the real world, where clean and annotated data on a specific problem can be sparse— How do we leverage the power of AI when we have very little data?

Snapshot 57

Snapshot Testing Deep Learning Visualization 57

Cloudera

MARCH 13, 2019

A Data Scientist : Organizations who show how they improved analytics, delivered new actionable intelligence, or designed systems for distributed deep learning and artificial intelligence to the organization’s business and customers. appeared first on Cloudera Blog. The post Introducing the 2019 Data Heroes – EMEA!

Data Architecture 69

Data Architecture Deep Learning Machine Learning Big Data 69

Cloudera

MAY 19, 2021

In the previous blog post in this series, we walked through the steps for leveraging Deep Learning in your Cloudera Machine Learning (CML) projects. As a machine learning problem, it is a classification task with tabular data, a perfect fit for RAPIDS. Introduction. Simple Exploration and Model.

Machine Learning 136

Machine Learning Data Science Data Lake Modeling 136

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

DataRobot Blog

MAY 26, 2022

Validating Modern Machine Learning (ML) Methods Prior to Productionization. Validating Machine Learning Models. This may be accomplished through a wide variety of tests, to develop a deeper introspection into how the model behaves.

Risk 52

Risk Modeling Metrics Business Objectives 52

IBM Big Data Hub

FEBRUARY 14, 2024

Using machine learning (ML), AI can understand what customers are saying as well as their tone—and can direct them to customer service agents when needed. AI platforms can use machine learning and deep learning to spot suspicious or anomalous transactions.

Cost-Benefit 85

Cost-Benefit Insurance Unstructured Data Machine Learning 85

Domino Data Lab

JULY 28, 2021

Today, the most advanced techniques used in data science are grouped under the term Artificial Intelligence (AI) Due to their information-acquiring nature, machine learning, deep learning, natural language processing (NLP) and computer vision are all considered branches within the field of AI. The Data Science Toolkit.

Data Science 45

Data Science Machine Learning Statistics Deep Learning 45

IBM Big Data Hub

APRIL 18, 2024

While AGI promises machine autonomy far beyond gen AI, even the most advanced systems still require human expertise to function effectively. Building an in-house team with AI, deep learning , machine learning (ML) and data science skills is a strategic move. What are the types of AGI?

Cost-Benefit 93

Cost-Benefit Modeling Manufacturing Interactive 93

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

Cloudera

APRIL 27, 2023

In the blog we will cover how Ray can be used in Cloudera Machine Learning’s open-by-design architecture to bring fast distributed AI compute to CDP. Its innovative architecture enables seamless integration with ML and deep learning libraries like TensorFlow and PyTorch. This is the power of being open by design.

Machine Learning 76

Machine Learning Dashboards Deep Learning Testing 76

Insight

MAY 7, 2019

Here is another great blog post on BERT by a former Insight Fellow. BERT uses a deep bi-directional model using transformers. This API also takes an S3 bucket name as input and then performs inference on all inputs in the test file. The platform was tested on an Amazon p2.xlarge Batch Inference ?—?This

Modeling 75

Modeling Testing Machine Learning Interactive 75

CDW Research Hub

JULY 31, 2019

Recent advances in machine learning, and more specifically its subset, deep learning, have made it possible for computers to better understand natural language. Rasa core is the main framework of the stack the provides conversation or dialogue management backed by machine learning. What’s next?

Deep Learning 49

Deep Learning Machine Learning Testing Modeling 49

IBM Big Data Hub

FEBRUARY 23, 2024

Machine learning (ML) and deep learning (DL) form the foundation of conversational AI development. Thoroughly testing the AI system before full deployment is critical. Experience the power of watsonx Assistant The post Conversational AI use cases for enterprises appeared first on IBM Blog.

Enterprise 100

Enterprise Interactive Data-driven Key Performance Indicator 100

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

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Testing Machine Learning Consulting Data Quality 300

Cloudera

SEPTEMBER 12, 2018

The week is typically filled with exciting announcements from Cloudera and many partners and others in the data management, machine learning and analytics industry. Enterprise Machine Learning: . It’s a big week for us, as many Clouderans descend on New York for the Strata Data Conference. Cloud Success: .

Machine Learning 45

Machine Learning Big Data Data Science Data Warehouse 45

Domino Data Lab

AUGUST 9, 2021

With the right tools, your data science teams can focus on what they do best – testing, developing and deploying new models while driving forward-thinking innovation. Before selecting a tool, you should first know your end goal – machine learning or deep learning. This is no exaggeration by any means.

Modeling 111

Modeling Deep Learning Machine Learning Statistics 111

Cloudera

SEPTEMBER 11, 2020

Due to COVID-19, retailers world-wide had their e-commerce capabilities put to the test with entire countries being in lockdown for weeks, even months, due to the pandemic. . What they have learned is that often their legacy Machine Learning models (e.g. Data and analytics.

Manufacturing 86

Manufacturing Forecasting Digital Transformation Sales 86

DataRobot Blog

MARCH 9, 2022

With that said, recent advances in deep learning methods have allowed models to improve to a point that is quickly approaching human precision on this difficult task. The IMDB Movie Reviews Dataset provides 50,000 highly polarized movie reviews with a 50-50 train/test split. Sentiment analysis datasets. It provides 1.6

Machine Learning 52

Machine Learning Deep Learning Modeling Measurement 52