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).

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.

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

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.

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.

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.

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.

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".

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 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 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 […].

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.

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.

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.

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.

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

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".

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).

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.

How graph algorithms improve machine learning

O'Reilly Media - Ideas

A look at why graphs improve predictions and how to create a workflow to use them with existing machine learning tasks. Graph analytics vary from conventional statistical analysis by focusing and calculating metrics based on the relationships between things.

Applications of data science and machine learning in financial services

O'Reilly Media - Ideas

Chong has extensive experience using analytics and machine learning in financial services, and he has experience building data science teams in the U.S. Continue reading Applications of data science and machine learning in financial services

Becoming a machine learning company means investing in foundational technologies

O'Reilly Media - Ideas

Companies successfully adopt machine learning either by building on existing data products and services, or by modernizing existing models and algorithms. A famous example is Google’s machine translation system, which shifted from “stats focused” approaches to TensorFlow.

Make Big Data Work with Machine Learning

The New Stack

Big Data Analytics Maturity Model. Milovanov has more than 10 years of experience in building enterprise-level AI, big data and advanced analytics solutions. Speed: Real-time analytics and decision-making (Data streaming). Machine Learning Business Use-Cases.

The best predictive analytics and machine-learning conferences of 2019

TechBeacon

To keep pace with digital transformation, IT Ops is changing how it manages its ecosystem, turning to artificial intelligence (AI), analytics, and machine learning. Enterprise IT, IT Ops, Predictive Analytics, Machine Learning, Artificial Intelligence (AI), ConferencesThe great shift that has transformed application development has begun remolding IT operations.

A History of Machine Learning by Political Machines

DataRobot

I had the privilege of witnessing the growth in the use of analytics by the Democratic Party in the ‘90s and 2000s. AI Education

Predicting Taxi Fares in New York Using Machine Learning in Real-Time

Dataiku

machine learning predictive analytics data projectDo you remember the days before Uber, Lyft, or Gett? Standing in the street trying to hail a taxi waiting for the moment a free cab might drive by and spot you? These days that world seems so far away.

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.

Artificial intelligence and machine learning adoption in European enterprise

O'Reilly Media - Ideas

In a recent survey , we explored how companies were adjusting to the growing importance of machine learning and analytics, while also preparing for the explosion in the number of data sources. Machine Learning model lifecycle management. Deep Learning.

5 findings from O'Reilly's machine learning adoption survey companies should know

O'Reilly Media - Data

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.

10 Platforms for Getting Started with Machine Learning

UruIT

Most recommended development and deployment platforms for machine learning projects. Are you getting started with Machine Learning? There’s a forecasted demand for Machine Learning among all kinds of industries. Watson Machine Learning.

Machine Learning Finds Its Place in the Production Pipeline

The New Stack

Machine learning-aided artificial intelligence (AI) might one day be able to eventually emulate the intelligence of hundreds or even thousands of human brains simultaneously, in such a way that human input would be obsolete throughout the software development cycle.

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 Data Science Iron Triangle – Modern BI and Machine Learning

Cloudera

The modern BI platform provides a non-intimidating analytic platform for all data – big and small. Solutions like Cloudera Altus give enterprises the ability to perform analytics on big data in the cloud. by John Thuma, Director of Analytic Solutions, Arcadia Data ( @ AnalyticsRNA ).

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.

3 New Techniques for Data-Dimensionality Reduction in Machine Learning

The New Stack

This dataset definitely brings out the slowness of a number of machine learning algorithms. Let’s proceed now with the (re)implementation and comparison of 10 state-of-the-art dimensionality reduction techniques, all currently available and commonly used in the data analytics landscape.

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.

Data collection and data markets in the age of privacy and machine learning

O'Reilly Media - Data

Much of the focus of recent press coverage has been on algorithms and models, specifically the expanding utility of deep learning. Because large deep learning architectures are quite data hungry, the importance of data has grown even more.

How privacy-preserving techniques can lead to more robust machine learning models

O'Reilly Media - Data

The O’Reilly Data Show Podcast: Chang Liu on operations research, and the interplay between differential privacy and machine learning. What about machine learning? Continue reading How privacy-preserving techniques can lead to more robust machine learning models