Machine Learning

I'm Programmer

The post Machine Learning appeared first on I'm Programmer. Programming Funny Images Programming Jokes Machine Learning machine learning applications role of Computer Science in Machine Learning

Cautionary allegory about machine learning

I'm Programmer

The post Cautionary allegory about machine learning appeared first on I'm Programmer. Programming Funny Images Programming Jokes Cautionary allegory about machine learning Machine Learning Algorithms Machine Learning For Beginners Understanding AI Machine Learning

Primer: Kubeflow Streamlines Machine Learning with Kubernetes

The New Stack

This article is a post in a series on bringing continuous integration and deployment (CI/CD) practices to machine learning. Kubeflow was created to make it easier to develop, deploy and manage machine learning applications. Kubernetes Machine Learning Technology

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.

Machine Learning in iOS

VironIT

Fifty years ago, machine learning was still the stuff of science fiction. Machine learning is a type of artificial intelligence where computers “learn” without being explicitly programmed.

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.

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.

Operationalizing machine learning

O'Reilly Media - Data

Dinesh Nirmal explains how real-world machine learning reveals assumptions embedded in business processes that cause expensive misunderstandings. Continue reading Operationalizing machine learning

When machine learning matters

Erik Bernhardsson

I joined Spotify in 2008 to focus on machine learning and music recommendations. 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. Rarely is machine learning the fundamental enabler of a product.

Machine Learning, Microservices, and Kubernetes

The New Stack

Artificial intelligence and machine learning are expected to have a profound effect on DevOps as a way to harness the brain power of perhaps hundreds or even thousands of humans in a single system in the development and deployment pipeline. 13:30: Where does that take you in the context of machine intelligence? The post Machine Learning, Microservices, and Kubernetes appeared first on The New Stack.

Addressing Fraud with Machine Learning: How & Why

Dataiku

Yet many organizations still use more traditional modeling for fraud or anomaly detection instead of making the shift to machine learning.

Machine learning: Research & industry

O'Reilly Media - Data

Having worked in both research and industry, Mikio Braun shares insights into what's the same, what's different, and how deep learning might change the game. Continue reading Machine learning: Research & industry

From Machine Learning to Machine Reasoning

CTOvision

The conversation around Artificial Intelligence usually revolves around technology-focused topics: machine learning, conversational interfaces, autonomous agents, and other aspects of data science, math, and implementation. Artificial Intelligence CTO News Cognilytica common sense deep learning knowledge graph machine learning machine reasoning ontology

Simplifying machine learning lifecycle management

O'Reilly Media - Data

In this episode of the Data Show , I spoke with Harish Doddi , co-founder and CEO of Datatron , a startup focused on helping companies deploy and manage machine learning models. Continue reading Simplifying machine learning lifecycle management

A machine learning algorithm walked into a bar

I'm Programmer

The post A machine learning algorithm walked into a bar appeared first on I'm Programmer. Programming Funny Images Programming Jokes Machine Learning Machine learning Algorithm machine learning algorithm humor Machine learning Humor

Machine Learning with Python, Jupyter, KSQL and TensorFlow

Confluent

Building a scalable, reliable and performant machine learning (ML) infrastructure is not easy. It takes much more effort than just building an analytic model with Python and your favorite machine learning framework. with scikit-learn.

Learning from users faster using machine learning

Erik Bernhardsson

Let’s also say we try to learn as much as we can from users, both using A/B tests but also using just basic slicing and dicing of the data. How can we learn faster? And instead of using the actual target metric (what fraction of people bought widgets) we use the predicted metric, using our machine learning model. So basically we learn to replace one value with a lower variance version of itself (but with slight bias).

Integrating Machine Learning Models into Your Big Data Pipelines in Real-Time With No Coding

Cloudera Engineering

Deploying a Machine Learning Model as a REST Service. The post Integrating Machine Learning Models into Your Big Data Pipelines in Real-Time With No Coding appeared first on Cloudera Engineering Blog.

How machine learning bolsters your security operations

TechBeacon

Security, Information Security, Special Coverage: RSA Conference 2019, Machine Learning, Artificial Intelligence (AI In today's threat environment, most security operations centers (SOCs) are losing ground to adversaries. Attackers continue to up their game at a dizzying pace, while everyone else falls behind. Meahwhile, efforts to combat breaches are crippled by a severely understaffed cybersecurity industry.

Deep automation in machine learning

O'Reilly Media - Ideas

In a previous post , we talked about applications of machine learning (ML) to software development, which included a tour through sample tools in data science and for managing data infrastructure. But this process only applies to a single machine learning platform: Spark.

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".

The State of the Octoverse: machine learning

Github

In our 2018 Octoverse report, we noticed machine learning and data science were popular topics on GitHub. We decided to dig a little deeper into the state of machine learning and data science on GitHub. Popular machine learning and data science packages.

Why it’s hard to design fair machine learning models

O'Reilly Media - Data

They recently wrote a survey paper, “A Critical Review of Fair Machine Learning,” where they carefully examined the standard statistical tools used to check for fairness in machine learning models. Continue reading Why it’s hard to design fair machine learning models

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.

How will the GDPR impact machine learning?

O'Reilly Media - Data

Answers to the three most commonly asked questions about maintaining GDPR-compliant machine learning programs. But there’s perhaps no more important—or uncertain—question than how the regulation will impact machine learning (ML), in particular.

Preserving privacy and security in machine learning

O'Reilly Media - Data

Ben Lorica offers an overview of recent tools for building privacy-preserving and secure machine learning products and services. Continue reading Preserving privacy and security in 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.

What machine learning engineers need to know

O'Reilly Media - Data

This conversation grew out of a recent email thread the three of us had on machine learning engineers , a new job role that LinkedIn recently pegged as the fastest growing job in the U.S. Continue reading What machine learning engineers need to know

How Machine Learning Pipelines Work and What Needs Improving

The New Stack

This article is a post in a series on bringing continuous integration and deployment (CI/CD) practices to machine learning. Whether you’re developing a machine learning system or running the model in production, there’s an increasingly large number of data processing workflows.

The Possibilities of AI and Machine Learning for Cybersecurity

The New Stack

A type of AI-powered security programs remaining engaged in its routine tasks of keeping checks and balances can suddenly be exposed by a more advanced hacking program based on machine learning. Google also uses Deep Learning for its plethora of platforms and applications.

An Introduction to the Machine Learning Platform as a Service

The New Stack

This article is the first post in a series on bringing continuous integration and deployment (CI/CD) practices to machine learning. Machine-Learning-Platform-as-a-Service (ML PaaS) is one of the fastest growing services in the public cloud.

Using machine learning to improve dialog flow in conversational applications

O'Reilly Media - Data

Continue reading Using machine learning to improve dialog flow in conversational applications The O’Reilly Data Show Podcast: Alan Nichol on building a suite of open source tools for chatbot developers.

DataRobot and Qlik Partnership Helps Democratize Machine Learning

DataRobot

Implementing machine learning models into analytics tools used to be time-consuming and technically challenging. With the DataRobot and Qlik partnership , this is no longer the case.

How machine learning impacts information security

O'Reilly Media - Ideas

They list important changes to the information landscape and offer suggestions on how to alleviate some of the new risks introduced by the rise of machine learning and AI. Continue reading How machine learning impacts information security

Managing risk in machine learning models

O'Reilly Media - Data

Burt recently co-authored a white paper on managing risk in machine learning models , and I wanted to sit down with them to discuss some of the proposals they put forward to organizations that are deploying machine learning.

Privacy in the age of machine learning

O'Reilly Media - Data

Ben Lorica explores emerging security best practices for business intelligence, machine learning, and mobile computing products. Continue reading Privacy in the age of machine learning

5 Best Machine Learning Frameworks for Web Development

Apiumhub

Machine learning is a branch of computer science that uses statistical methods to give computers the ability to self-improve without direct human supervision. Machine learning frameworks have changed the way web development companies utilize data. Machine learning algorithms can process large volumes of unstructured information, and turn them into actionable insights and predictions. 5 Best Machine Learning Frameworks for Web Development.

Humans and the machine: Machine learning in context

O'Reilly Media - Data

Continue reading Humans and the machine: Machine learning in context Jean-François Puget explains why human context should be embraced as a guide to building better and smarter systems.

When Holt-Winters Is Better Than Machine Learning

The New Stack

Machine Learning (ML) gets a lot of hype, but its classical predecessors are still immensely powerful, especially in the time-series space. Taken from “Statistical and Machine Learning forecasting methods: Concerns and Ways forward.”. A list of learning resources.