O'Reilly Media - Data

DECEMBER 12, 2019

For all the excitement about machine learning (ML), there are serious impediments to its widespread adoption. There are several known attacks against machine learning models that can lead to altered, harmful model outcomes or to exposure of sensitive training data. [8] 2] The Security of Machine Learning. [3]

Machine Learning 191

Machine Learning Artificial Inteligence Technical Review Analysis 191

O'Reilly Media - Data

JULY 17, 2018

Although machine learning (ML) can produce fantastic results, using it in practice is complex. Machine learning workflow challenges. MLflow: An open machine learning platform. An overview of the challenges MLflow tackles and a primer on how to get started. algorithm) to see whether it improves results.

Machine Learning 180

Machine Learning Artificial Inteligence Software Review Open Source 180

Mentormate

DECEMBER 12, 2023

In a previous blog post, we introduced a five-phase framework to plan out Artificial Intelligence (AI) and Machine Learning (ML) initiatives. The Traditional Machine Learning Workflow Initiating a traditional ML project begins with collecting data. The emphasis then shifts to collecting real-time patient insights.

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Openxcell

MARCH 30, 2023

From human genome mapping to Big Data Analytics, Artificial Intelligence (AI),Machine Learning, Blockchain, Mobile digital Platforms (Digital Streets, towns and villages),Social Networks and Business, Virtual reality and so much more. What is Machine Learning? Machine Learning delivers on this need.

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O'Reilly Media - Data

AUGUST 7, 2018

New survey results highlight the ways organizations are handling machine learning's move to the mainstream. As machine learning has become more widely adopted by businesses, O’Reilly set out to survey our audience to learn more about how companies approach this work. What metrics are used to evaluate success?

Machine Learning 144

Machine Learning Artificial Inteligence Survey Company 144

O'Reilly Media - Ideas

JUNE 9, 2020

The biggest problem facing machine learning today isn’t the need for better algorithms; it isn’t the need for more computing power to train models; it isn’t even the need for more skilled practitioners. It’s getting machine learning from the researcher’s laptop to production.

Machine Learning 108

Machine Learning Artificial Inteligence Training Metrics 108

Altexsoft

JULY 23, 2020

When speaking of machine learning, we typically discuss data preparation or model building. The fusion of terms “machine learning” and “operations”, MLOps is a set of methods to automate the lifecycle of machine learning algorithms in production — from initial model training to deployment to retraining against new data.

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Artificial Inteligence Machine Learning DevOps Tools 145

Toptal

SEPTEMBER 7, 2022

Clustering in machine learning has a variety of applications, but how do you know which algorithm is best suited to your data? Here's how to amplify your data insights with comparison metrics, including the F-measure.

Metrics 98

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Perficient

DECEMBER 13, 2023

The Essence of a Metric Store: A metric store is a centralized repository designed explicitly for managing metrics definitions and data. Sitting snugly between your upstream data warehouses and downstream business applications, this innovative layer fundamentally transforms how metrics are handled.

Metrics 52

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Openxcell

APRIL 21, 2023

In today’s fast-paced world, Machine Learning is quickly changing the way various industries and our daily lives function. This engaging blog post dives into the exciting world of Machine Learning, shedding light on what it is, why it matters, its history, types, core principles, and applications.

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OpenCredo

APRIL 20, 2021

Machine learning has great potential for many businesses, but the path from a Data Scientist creating an amazing algorithm on their laptop, to that code running and adding value in production, can be arduous. Here are two typical machine learning workflows. Monitoring. Does it only do so at weekends, or near Christmas?

Machine Learning 140

Machine Learning Artificial Inteligence Software Review Open Source 140

Cloudera

FEBRUARY 16, 2022

In this example, the Machine Learning (ML) model struggles to differentiate between a chihuahua and a muffin. We will learn what it is, why it is important and how Cloudera Machine Learning (CML) is helping organisations tackle this challenge as part of the broader objective of achieving Ethical AI.

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Altexsoft

MAY 12, 2022

Today, we have AI and machine learning to extract insights, inaudible to human beings, from speech, voices, snoring, music, industrial and traffic noise, and other types of acoustic signals. At the same time, keep in mind that neither of those and other audio files can be fed directly to machine learning models.

Machine Learning 122

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Altexsoft

JUNE 29, 2021

In a world fueled by disruptive technologies, no wonder businesses heavily rely on machine learning. Google, in turn, uses the Google Neural Machine Translation (GNMT) system, powered by ML, reducing error rates by up to 60 percent. The role of a machine learning engineer in the data science team.

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Artificial Inteligence Machine Learning Engineering Data Engineering 105

Cloudera

APRIL 9, 2021

Specifically, we’ll focus on training Machine Learning (ML) models to forecast ECC part production demand across all of its factories. Predictive Analytics – AI & machine learning. So let’s introduce Cloudera Machine Learning (CML) and discuss how it addresses the aforementioned silo issues.

Machine Learning 98

Machine Learning Artificial Inteligence Data Data Engineering 98

Xebia

NOVEMBER 30, 2023

In the last few years, the discipline of Machine Learning Operations (MLOps) has been received a lot of traction to get more Machine Learning (ML) solutions into productions, reduce iteration cycles, and reduce costs for engineering and maintenance. Performance metrics for ML and MLOps teams.

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Performance How To Machine Learning Artificial Inteligence 147

O'Reilly Media - Data

JUNE 5, 2018

Today, just 15% of enterprises are using machine learning, but double that number already have it on their roadmaps for the upcoming year. However, in talking with CEOs looking to implement machine learning in their organizations, there seems to be a common problem in moving machine learning from science to production.

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Artificial Inteligence Machine Learning Software Review Weak Development Team 160

Erik Bernhardsson

DECEMBER 11, 2017

Jeremy Stanley pointed out: “If you can place a dollar value on a unit of each metric, then you could use the total as a single metric”. Instead of using say “purchased a widget” as an outcome metric, try to predict based on user attributes whether the user is going to purchase the widget. Reading material. The idea is simple.

Machine Learning 127

Machine Learning Artificial Inteligence Metrics Testing 127

OverOps

AUGUST 12, 2019

To answer this question, we recently created a framework that helps organizations pinpoint critical gaps in data and metrics that are holding them back on their reliability journeys. Code Metrics. Transactions & Performance Metrics. System Metrics. Are there any blocked threads related to this failure?

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Metrics Data SDLC Machine Learning 207

Altexsoft

MAY 27, 2020

Machine learning (ML) history can be traced back to the 1950s, when the first neural networks and ML algorithms appeared. Analysis of more than 16.000 papers on data science by MIT technologies shows the exponential growth of machine learning during the last 20 years pumped by big data and deep learning advancements.

Machine Learning 89

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O'Reilly Media - Ideas

NOVEMBER 13, 2018

As the data community begins to deploy more machine learning (ML) models, I wanted to review some important considerations. We recently conducted a survey which garnered more than 11,000 respondents—our main goal was to ascertain how enterprises were using machine learning. Let’s begin by looking at the state of adoption.

Machine Learning 90

Machine Learning Artificial Inteligence Software Review Conference 90

Cloudera

DECEMBER 7, 2022

C (Cloudera is headquartered in the US, but we also recognize the superiority of the metric system). The data innovation that I was most excited to learn about though is the implementation of a human-in-the-loop (HITL) machine learning (ML) solution to assist referees in more accurately calling offsides.

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Artificial Inteligence Machine Learning Systems Review Technical Review 71

InfoBest

SEPTEMBER 19, 2021

Machine learning development. In the case of companies looking to improve their workflows and to become more digital it is usually machine learning development, a branch of A.I. Machine learning development, compared to more classic A.I., Machine learning development, compared to more classic A.I.,

Machine Learning 52

Machine Learning Artificial Inteligence Development Training 52

Erik Bernhardsson

DECEMBER 11, 2017

Jeremy Stanley pointed out: “If you can place a dollar value on a unit of each metric, then you could use the total as a single metric”. Instead of using say “purchased a widget” as an outcome metric, try to predict based on user attributes whether the user is going to purchase the widget. Reading material. The idea is simple.

Machine Learning 100

Machine Learning Artificial Inteligence Metrics Testing 100

CIO

APRIL 12, 2023

Real-time AI brings together streaming data and machine learning algorithms to make fast and automated decisions; examples include recommendations, fraud detection, security monitoring, and chatbots. What metrics are used to understand the business impact of real-time AI? It isn’t easy.

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Artificial Inteligence Artificial Intelligence Machine Learning Agile 345

AWS Machine Learning - AI

FEBRUARY 26, 2024

In this post, we demonstrate a few metrics for online LLM monitoring and their respective architecture for scale using AWS services such as Amazon CloudWatch and AWS Lambda. Overview of solution The first thing to consider is that different metrics require different computation considerations. The function invokes the modules.

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Artificial Inteligence AWS Lambda Metrics 106

Xebia

AUGUST 9, 2023

With the industry moving towards end-to-end ML teams to enable them to implement MLOPs practices, it is paramount to look past the model and view the entire system around your machine learning model. Demand forecasting is chosen because it’s a very tangible problem and very suitable application for machine learning.

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TechCrunch

FEBRUARY 25, 2021

The idea is to build a product with a way to connect to key business systems, pull the data and answer a very specific set of business questions, while using machine learning to provide more proactive advice. For example, ensuring you have a diverse set of candidates to choose from when you are reviewing resumes.

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Metrics Company Systems Review Machine Learning 222

KitelyTech

JUNE 9, 2022

Today’s businesses are using machine learning algorithms to improve internal and customer-facing operations, leading to better experiences for customers. Machine Learning (ML) lets companies harness the power of artificial intelligence and modern technologies to improve in ways that were not possible before.

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Artificial Inteligence Machine Learning Applications Artificial Intelligence 52

Netflix Tech

DECEMBER 10, 2020

The increasing vastness and diversity of what our members are watching make answering these questions particularly challenging using conventional methods, which draw on a limited set of comparable titles and their respective performance metrics (e.g., box office, Nielsen ratings). This challenge is also an opportunity.

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CIO

APRIL 8, 2024

Computer vision, AI, and machine learning (ML) all now play a role. Jamie Capel-Davies, head of science and technical for ITF, says metrics don’t mean much if you can’t communicate them effectively in time to make use of them. Capel-Davies’ advice: Focus on communication.

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d2iq

FEBRUARY 19, 2021

Going from a prototype to production is perilous when it comes to machine learning: most initiatives fail , and for the few models that are ever deployed, it takes many months to do so. As little as 5% of the code of production machine learning systems is the model itself. Adapted from Sculley et al.

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Artificial Inteligence Machine Learning Technical Review Software Review 103

Altexsoft

SEPTEMBER 21, 2020

Domingos referenced a common truth about complex machine learning models, where deep learning belongs. In this article, we’ll talk about the interpretability of machine learning models. But even in scenarios when a machine learning algorithm makes non-critical decisions, humans look for answers.

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Artificial Inteligence Machine Learning Healthcare Weak Development Team 119

Altexsoft

JULY 8, 2021

To help companies unlock the full potential of personalized marketing, propensity models should use the power of machine learning technologies. This post is going to shed light on propensity modeling and the role of machine learning in making it an efficient predictive tool. What is a propensity model?

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DataRobot

FEBRUARY 14, 2022

Typically this means finding out how many predictions have been made, how many requests have been made to the deployment, and other performance-related metrics. In the example below, we’re adjusting the time period for computing the Service Health metrics. Learn More About Explainable AI. Learn more.

Machine Learning 52

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CIO

MARCH 15, 2024

Martell had previously served as head of machine learning at Lyft and as head of machine intelligence at Dropbox. Among those five initiatives were improved data quality and business performance metrics, along with better digital employee management. She is scheduled to take over CDAO on April 8.

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DevOps.com

MARCH 17, 2022

The post Rafay Systems Powers AI and Machine Learning Applications at the Edge by Streamlining Operations for GPU-based Container Workloads appeared first on DevOps.com.

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