Smart Data Collective

FEBRUARY 21, 2021

Deep learning tech is influencing and enhancing many industries, promising to provide insights into key business operations which were not previously possible to unearth. One of the biggest applications of this technology lies with using deep learning to streamline fleet management. Route adjustments made in real time.

Deep Learning 97

Deep Learning Management Cost-Benefit Manufacturing 97

Smart Data Collective

NOVEMBER 1, 2020

Machines, artificial intelligence (AI), and unsupervised learning are reshaping the way businesses vie for a place under the sun. With that being said, let’s have a closer look at how unsupervised machine learning is omnipresent in all industries. What Is Unsupervised Machine Learning? The Bottom Line.

Machine Learning 134

Machine Learning Data mining Visualization Statistics 134

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. We covered different ways of measuring model efficiency and showed ways to visualize this and select models based on it. Below are a few examples.

Machine Learning 98

Machine Learning Measurement Deep Learning Manufacturing 98

DataRobot Blog

APRIL 21, 2022

Machine learning, and especially deep learning, has become increasingly more accurate in the past few years. In the graph below, borrowed from the same article, you can see how some of the most cutting-edge algorithms in deep learning have increased in terms of model size over time.

Deep Learning 145

Deep Learning Machine Learning Measurement Metrics 145

Cloudera

FEBRUARY 16, 2022

In this example, the Machine Learning (ML) model struggles to differentiate between a chihuahua and a muffin. blueberry spacing) is a measure of the model’s interpretability. Machine Learning Model Lineage. Machine Learning Model Visibility . Figure 04: Applied Machine Learning Prototypes (AMPs).

Machine Learning 83

Machine Learning Modeling Metadata Recreation/Entertainment 83

O'Reilly on Data

JANUARY 29, 2019

A recent survey investigated how companies are approaching their AI and ML practices, and measured the sophistication of their efforts. We found companies were planning to use deep learning over the next 12-18 months. We found companies were planning to use deep learning over the next 12-18 months.

Deep Learning 207

Deep Learning Machine Learning Data Science Metadata 207

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. Whenever you test a machine learning method, it’s helpful to have a baseline method and accuracy level against which to measure improvements.

Machine Learning 52

Machine Learning Deep Learning Modeling Measurement 52

Rocket-Powered Data Science

JULY 13, 2023

Beyond the early days of data collection, where data was acquired primarily to measure what had happened (descriptive) or why something is happening (diagnostic), data collection now drives predictive models (forecasting the future) and prescriptive models (optimizing for “a better future”). It is an essential asset.

Analytics 167

Analytics Machine Learning Data Science Deep Learning 167

O'Reilly on Data

FEBRUARY 11, 2019

Highlights and use cases from companies that are building the technologies needed to sustain their use of analytics and machine learning. In a forthcoming survey, “Evolving Data Infrastructure,” we found strong interest in machine learning (ML) among respondents across geographic regions. Deep Learning.

Big Data 207

Big Data Technology Machine Learning Deep Learning 207

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

Jen Stirrup

MAY 5, 2020

Apache Spark also allows you to do Machine Learning, streaming analytics, interactive querying, and also data visualization, as well. Azure Databricks can be used with many applications for Deep Learning, for example, which is a way of taking neural net models and stringing them together. Azure Machine Learning.

Machine Learning 89

Machine Learning Data Science Data Lake Big Data 89

DataRobot Blog

FEBRUARY 14, 2022

Because our dataset contains image data, DataRobot used models that contain deep learning based image featurizers. DataRobot uses the Population Stability Index to measure drift. Learn More About Explainable AI. Learn more. Activation Maps.

Machine Learning 52

Machine Learning Metrics Sales Modeling 52

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 327

Management Machine Learning Experimentation Metrics 327

Corinium

JUNE 6, 2019

A properly set framework will ensure quality, timeliness, scalability, consistency, and industrialization in measuring and driving the return on investment. It is also important to have a strong test and learn culture to encourage rapid experimentation. Build multiple MVPs to test conceptually and learn from early user feedback.

Insurance 250

Insurance Analytics Forecasting Deep Learning 250

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 193

Modeling Machine Learning Testing Metrics 193

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

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

Analytics Vidhya

MARCH 1, 2024

Introduction The Hamming Distance Algorithm is a fundamental tool for measuring the dissimilarity between two pieces of data, typically strings or integers. It calculates the number of positions at which the corresponding elements differ.

Measurement 207

Measurement Analytics IT Deep Learning 207

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

Smart Data Collective

JUNE 4, 2021

Probability is the measurement of the likelihood of events. Basics of Machine Learning. Machine learning is the science of building models automatically. Whereas in machine learning, the algorithm understands the data and creates the logic. In supervised learning, a variable is predicted.

Data Science 104

Data Science Statistics Deep Learning Machine Learning 104

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. AutoML tools: Automated machine learning, or autoML, supports faster model creation with low-code and no-code functionality.

Machine Learning 73

Machine Learning Manufacturing Cost-Benefit Deep Learning 73

O'Reilly on Data

JULY 28, 2020

It’s often difficult for businesses without a mature data or machine learning practice to define and agree on metrics. These measures are commonly referred to as guardrail metrics , and they ensure that the product analytics aren’t giving decision-makers the wrong signal about what’s actually important to the business.

Marketing 361

Marketing Experimentation Metrics Testing 361

Data Science and Beyond

JANUARY 11, 2020

My story seems to reflect that: From my first steps in sentiment analysis and topic modelling, through building recommender systems while dabbling in Kaggle competitions and deep learning a few years ago, and to my present-day interest in causal inference. What can one do to remain relevant in such an environment?

Software 103

Software Data Science Machine Learning Deep Learning 103

Laminar Security

DECEMBER 13, 2023

However, the advent of AI and machine learning (ML) has revolutionized this process. Machine learning algorithms can be trained to recognize patterns in the data and classify data accordingly. One trend is the increasing use of deep learning algorithms for these processes.

Data-driven 119

Data-driven Machine Learning Risk Deep Learning 119

Smart Data Collective

JULY 1, 2023

Image annotation is the act of labeling images for AI and machine learning models. The resulting structured data is then used to train a machine learning algorithm. There are a lot of image annotation techniques that can make the process more efficient with deep learning.

Metrics 64

Metrics Machine Learning Uncertainty Deep Learning 64

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 82

Metrics Machine Learning Risk Statistics 82

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 54

Data Science Machine Learning Cost-Benefit Deep Learning 54

Analytics Vidhya

FEBRUARY 7, 2024

The average, also known as the arithmetic mean, is a measure of central tendency that provides insight into the overall value of a dataset. Introduction When working with lists in Python, finding the average values within the list is often necessary.

Measurement 143

Measurement Analytics Deep Learning Machine Learning 143

O'Reilly on Data

JUNE 18, 2019

Machine learning solutions for data integration, cleaning, and data generation are beginning to emerge. “AI In this post, we shed some light on various efforts toward generating data for machine learning (ML) models. Machine learning applications rely on three main components: models, data, and compute.

Machine Learning 255

Machine Learning Data Quality Statistics Modeling 255

Cloudera

NOVEMBER 19, 2020

The Association of Certified Fraud Examiners reports the use of artificial intelligence and machine learning in anti-fraud programs is expected to almost triple in the next two years. Machine learning algorithms enable fraud detection systems to distinguish between legitimate and fraudulent behaviors.

Machine Learning 117

Machine Learning Data-driven Modeling Deep Learning 117

CIO Business Intelligence

MARCH 22, 2023

Enter the new class ML data scientists require large quantities of data to train machine learning models. The new class often uses advanced techniques such as deep learning, natural language processing, and computer vision to analyze and extract insights from the data.

Machine Learning 96

Machine Learning Cost-Benefit Modeling Risk 96

O'Reilly on Data

MARCH 20, 2019

Apply fair and private models, white-hat and forensic model debugging, and common sense to protect machine learning models from malicious actors. Like many others, I’ve known for some time that machine learning models themselves could pose security risks. Data poisoning attacks. Watermark attacks.

Modeling 219

Modeling Machine Learning Predictive Modeling Consulting 219

CIO Business Intelligence

SEPTEMBER 7, 2022

Dubbed Cropin Cloud, the suite comes with the ability to ingest and process data, run machine learning models for quick analysis and decision making, and several applications specific to the industry’s needs. The suite, according to the company, consists of three layers: Cropin Apps, the Cropin Data Hub and Cropin Intelligence.

B2B 80

B2B Contextual Data IoT Deep Learning 80

O'Reilly on Data

MARCH 7, 2019

Language understanding benefits from every part of the fast-improving ABC of software: AI (freely available deep learning libraries like PyText and language models like BERT ), big data (Hadoop, Spark, and Spark NLP ), and cloud (GPU's on demand and NLP-as-a-service from all the major cloud providers). negation) detection.

Deep Learning 224

Deep Learning Testing Machine Learning Modeling 224

CIO Business Intelligence

APRIL 11, 2022

Because the safety and security that Doral residents experience are key to their quality of life, the initiative emphasizes security measures, which have intense data gathering and processing workloads. In just three years, Doral has implemented 40 percent of the smart city technology measures identified by the National League of Cities.

IoT 145

IoT Measurement Cost-Benefit Deep Learning 145

CIO Business Intelligence

JUNE 7, 2022

Predictive analytics applies techniques such as statistical modeling, forecasting, and machine learning to the output of descriptive and diagnostic analytics to make predictions about future outcomes. In business, predictive analytics uses machine learning, business rules, and algorithms.

Data Analytics 123

Data Analytics Diagnostic Analytics Management Analytics 123

CIO Business Intelligence

JUNE 22, 2023

It’s already being used to help improve operational processes, strengthen customer service, measure employee experience, and bolster cybersecurity efforts, among other applications. AI is quickly becoming an essential part of daily work.

IT 101

IT Machine Learning Deep Learning Technology 101