Are Your Machine Learning Models Wrong?

Cloudera Engineering

In these circumstances, concerns arise about the accuracy of Machine Learning (ML) models, how recovery will be different for the UK and EU relative to the rest of the world, and what financial institutions should do to address these concerns. Business Machine Learning

20 Machine Learning/Artificial Intelligence Influencers To Follow In 2020

Hacker Earth Developers Blog

Machine Learning (ML) is emerging as one of the hottest fields today. The Machine Learning market is ever-growing, predicted to scale up at a CAGR of 43.8% Consequently, there has been a significant increase in the number of Machine Learning enthusiasts across the globe.

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20 Machine Learning/Artificial Intelligence Influencers To Follow In 2020

Hacker Earth Developers Blog

Machine Learning (ML) is emerging as one of the hottest fields today. The Machine Learning market is ever-growing, predicted to scale up at a CAGR of 43.8% Consequently, there has been a significant increase in the number of Machine Learning enthusiasts across the globe.

Putting Machine Learning Models into Production

Cloudera Engineering

The key focus areas (detailed in the diagram below) are usually managed by machine learning engineers after the data scientists have done their work. To make this more concrete, I will use an example of telco customer churn (the “Hello World” of enterprise machine learning).

Bringing AIOps to Machine Learning & Analytics

Cloudera

We learned a lot about data center automation based on real-time application and diagnostic feedback using applied machine learning. Witnessing these challenges, we focused on solving them through machine learning applied to workload and cluster optimization.

R vs Python for Machine Learning

The Crazy Programmer

There are so many things to learn before to choose which language is good for Machine Learning. Don’t worry guys through this article we will discuss R vs Python for Machine Learning. R vs Python for Machine Learning. Deep Learning.

When machine learning matters

Erik Bernhardsson

I joined Spotify in 2008 to focus on machine learning and music recommendations. So instead, I switched gears and ran the “Analytics team” for 2 years. In the majority of all products, machine learning will not be a key differentiator in the first five years. Most machine learning is sprinkles on the top. I lead the tech team at a startup and we are nowhere near using any kind of sophisticated machine learning, two years into the process.

Machine Learning and Predictive Analytics Are Reshaping Manufacturing

DevOps.com

The post Machine Learning and Predictive Analytics Are Reshaping Manufacturing appeared first on DevOps.com. Blogs DevOps Practice Enterprise DevOps inventory management Predictive Analytics predictive maintenance process optimization supply chain

Machine Learning and Deep Learning: How Does Machine Learning Work?

G2 Crowd Software

anytime soon, but machine learning and deep learning are gaining a large amount of traction, and are becoming borderline essential in the business world. So what is machine learning and why is it so crucial for enterprise businesses today? Supervised learning.

How OpenXcell is adapting to TensorFlow to building advanced machine learning models

Openxcell

Tensorflow for Machine Learning helps engineers effectively to assemble and send ML-fueled applications. With the help of TensorFlow.js, you can create new machine learning models, and it can be deployed to the existing models through JavaScript.

Machine learning on encrypted data

O'Reilly Media - Ideas

The O’Reilly Data Show Podcast: Alon Kaufman on the interplay between machine learning, encryption, and security. As I noted, the main motivation for improving data liquidity is the growing importance of machine learning.

Managing risk in machine learning

O'Reilly Media - Ideas

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.

Introducing MLOps And SDX for Models in Cloudera Machine Learning

Cloudera Engineering

It seems everyone is talking about machine learning (ML) these days — and ML’s use in products and services we consume everyday continues to be increasingly ubiquitous. In addition, the regulatory landscape for data and machine learning are evolving quickly.

AI and Machine Learning in Test Automation

LaunchDarkly

Oren explained the differences between AI and automation, problems with existing test automation solutions, how AI/machine learning can be used to address software testing problems, and more. And you’ll learn out of that. It can learn and help create the test for you.

The challenges you’ll face deploying machine learning models (and how to solve them)

Cloudera Engineering

billion into machine learning application development ( Statistica ). Yet, only 35% of organizations report having analytical models fully deployed in production ( IDC ). . Disjointed software and approaches to production Machine Learning (MLOps).

Build Repeatable ML Workflows with Azure Machine Learning Pipelines

The New Stack

Machine learning (ML) involves a complex workflow of data preparation, transformation, training, tuning, evaluation, deployment, and inference. For training the model and hyperparameter tuning, a fleet of GPU-enabled virtual machines is deployed to speed up the process.

Chasing the Elusive Machine Learning Platform

CTOvision

The “Data Science Platform” and “Machine Learning Platform” are at the front lines of [.]. Artificial Intelligence Big Data and Analytics data science Machine learning ml platformIf you have been following the breathless hype of AI and ML over these past few years, you might have noticed the increasing pace at which vendors are scrambling to roll out “platforms” that service the data science and ML communities.

OverOps Employs Machine Learning to Measure Application Reliability

DevOps.com

OverOps is adding a series of dashboards to its analytics platform that employ machine learning algorithms to identify which software application anomalies in a pre-production environment are likely to have the most impact on a production environment.

When machine learning matters

Erik Bernhardsson

I joined Spotify in 2008 to focus on machine learning and music recommendations. So instead, I switched gears and ran the “Analytics team” for 2 years. In the majority of all products, machine learning will not be a key differentiator in the first five years.

How Machine Learning Can Strengthen Insider Threat Detection

CTOvision

Using Machine Learning in Oracle Analytics Cloud to Predict HR Attrition

Apps Associates

Artificial Intelligence (AI) and Machine Learning (ML) have become popular mainstream topics. This was true for many years but it is beginning … Continue reading "Using Machine Learning in Oracle Analytics Cloud to Predict HR Attrition".

Using machine learning and analytics to attract and retain employees

O'Reilly Media - Ideas

In this episode of the Data Show , I spoke with Maryam Jahanshahi , research scientist at TapRecruit, a startup that uses machine learning and analytics to help companies recruit more effectively.

Machine Learning for Twitter Sentiment Analysis

The New Stack

Organizations have modernized their business intelligence architecture by moving analytics workloads into the cloud, opening doors for leveraging other cloud services to gain deeper insights from data. The regular introduction of new cloud services has made machine learning a hot topic.

Demystifying Machine Learning: How ML Discovers New Information

The New Stack

This post is part of a series by Levon Paradzhanyan that demystifies data science, machine learning, deep learning, and artificial intelligence down while explaining how they all tie into one another. Machine Learning versus Data Science. Unsupervised Learning.

Are Self-Service Machine Learning Models the Future of AI Integration?

DevOps.com

DevOps teams seeking to step up their mojo in developing cutting-edge artificial intelligence (AI) features are facing a big skills bottleneck when it comes to data analytics and machine learning modeling.

Using machine learning to monitor and optimize chatbots

O'Reilly Media - Data

As in the case of other machine learning applications , when companies start deploying many more chatbots, automated tools for monitoring and diagnostics become essential. Continue reading Using machine learning to monitor and optimize chatbots

Machine Learning and AI Underpin Predictive Analytics to Achieve Clinical Breakthroughs

Cloudera

Despite advances made in EHRs of late, they, unfortunately, do not provide advanced analytics or intelligent search for that matter. Together in tandem with MetiStream, a healthcare analytics software company, Cloudera addresses many of these challenges.

10 Ways Machine Learning Is Revolutionizing Sales

CTOvision

Artificial Intelligence (AI) and machine learning show the potential to reduce the most time-consuming, manual tasks that keep sales teams away from spending more time with customers. Automating account-based marketing support with predictive analytics and supporting account-centered research, forecasting, reporting, and recommending which customers to upsell first are all techniques freeing sales teams from manually […].

Is AI and Machine learning impacting Enterprise Mobility?

OTS Solutions

Is AI and Machine learning impacting Enterprise Mobility? As a result, developers have shifted gear and are now using the latest technologies, including machine learning and Artificial Intelligence (AI) to develop mobile apps. Data Mining and Predictive Analytics.

How Kubernetes Could Orchestrate Machine Learning Pipelines

The New Stack

This article is a post in a series on bringing continuous integration and deployment (CI/CD) practices to machine learning. As a scalable orchestration platform, Kubernetes is proving a good match for machine learning deployment — in the cloud or on your own infrastructure.

How companies around the world apply machine learning

O'Reilly Media - Data

The growing role of data and machine learning cuts across domains and industries. Companies continue to use data to improve decision-making (business intelligence and analytics) and for automation (machine learning and AI).

Application of advanced analytics and machine learning in the banking industry

Hacker Earth Developers Blog

Banks have always been custodian of customer data, but they lack the technological and analytical capability to derive value from the data. On the other hand, fintech companies have the analytical capabilities and, thanks to payments services directives, they now have access to valuable data.

How to take machine learning from exploration to implementation

O'Reilly Media - Data

Interest in machine learning (ML) has been growing steadily , and many companies and organizations are aware of the potential impact these tools and technologies can have on their underlying operations and processes. Machine Learning in the enterprise". Deep Learning".

Machine learning in production: Human error is inevitable, here’s how to prepare.

Cloudera Engineering

You have machine learning capabilities up and running in your organization. Machine learning and AI are projected to create $2.6 When it comes to making machine learning operational for your business – and sustaining it – there are many factors to consider.

New Server Hits the Machine-Learning Track

Dell EMC

The new Dell EMC DSS 8440 server accelerates machine learning and other compute-intensive workloads with the power of up to 10 GPUs and high-speed I/O with local storage.

Machine Learning and Real-Time Analytics in Apache Kafka Applications

Confluent

The relationship between Apache Kafka® and machine learning (ML) is an interesting one that I’ve written about quite a bit in How to Build and Deploy Scalable Machine Learning in […].

New Quantum Machine Learning Algorithm Could Crush Big Data

CTOvision

Read Zayan Guedim explain how a new hybrid system combines classical machine learning techniques with quantum computation to optimize the management of the power grid on Edgylabs blog : Strategically deployed across the grid, Phasor Measurement Units (PMUs) are sensor devices that measure currents and voltages at a particular time. Big Data and Analytics CTO News