SEARCH (ENTER TO SEE ALL RESULTS)
Cancel Search
POPULAR TOPICS
Contributed
sponsored-post-contributed
News
Analysis
The New Stack Makers
Tutorial
Podcast
Feature
Research
Profile
The New Stack Logo
Skip to content
  • Podcasts
  • Events
  • Ebooks
    • DevOps
    • DevSecOps
    • Docker Ecosystem
    • Kubernetes Ecosystem
    • Microservices
    • Observability
    • Security
    • Serverless
    • Storage
    • All Ebooks
  • Newsletter
  • Sponsorship
  • • • •
    • Podcasts
      • TNS @Scale Series
      • TNS Analysts Round Table
      • TNS Context Weekly News
      • TNS Makers Interviews
      • All Podcasts
    • Events
    • Ebooks
      • DevOps
      • DevSecOps
      • Docker Ecosystem
      • Kubernetes Ecosystem
      • Microservices
      • Observability
      • Security
      • Serverless
      • Storage
      • All Ebooks
    • Newsletter
    • Sponsorship
Skip to content
  • Architecture
    • Cloud Native
    • Containers
    • Edge/IoT
    • Microservices
    • Networking
    • Serverless
    • Storage
  • Development
    • Development
    • Cloud Services
    • Data
    • Machine Learning
    • Security
  • Operations
    • CI/CD
    • Culture
    • DevOps
    • Kubernetes
    • Monitoring
    • Service Mesh
    • Tools
Search The New Stack
 

Machine Learning

▾ 5 MINUTE READ — CLOSE

The machine learning framework moves beyond the traditional model of computation. Instead of arriving at a definite reproducible answer through a series of calculations, machine learning — a branch of artificial intelligence — works on a series of statistical probabilities to suggest new solutions to a problem. This work is useful for such jobs as designing new materials, medical diagnosis, advanced game graphics and so many other tasks.

What Is Machine Learning?

Machine learning (ML) is part of artificial intelligence (AI) which assists various applications in predicting futuristic outcomes more accurately. Machine learning is being used in many industries and for a variety of purposes from fraud detection to business process automation. Enterprises gain a different view of operational patterns within the organization and trends in customer behavior through machine learning implementation.

Machine Learning Techniques Are Used to Train Algorithms

There are four types of algorithms and several techniques used in machine learning. These types — supervised, unsupervised, semi-supervised, and reinforcement learning — and their respective techniques are examined below:

Supervised Machine Learning

Much of the early success in machine learning has come from supervised learning, where a clearly defined data set is already available for analysis. The algorithm then determines how to arrive at given outputs and inputs. The machine learning algorithm makes predictions by analyzing the data and is corrected by the operator until its predictions are highly accurate.

In supervised machine learning, the algorithm learns through these three techniques:

Classification. The machine learning program determines what categories new observations belong to based on the analysis and observation of the available data sets.

Regression. The machine learning program estimates the relationship between variables where there is one dependent variable and a number of changing variables.

Forecasting. This involves making predictions about the future from new and existing data. It is also used to analyze trends.

Gathering recent momentum has been the Deep Learning technique, which doesn’t require all the intermediate steps that supervised learning does. Instead, the idea is to let the Deep Learning neural nets find the answers on their own.

Machine learning can be used for many different applications and industries, including operations. Find out more about Machine Learning for Operations.

Semi-Supervised Learning

Machine learning algorithms are provided with labeled and unlabeled data. Labeled data provides information about a given data set that helps the algorithm understand what the data is about. Unlabeled data has no descriptions. Through semi-supervised learning, machine learning algorithms learn to label unlabeled data.

Unsupervised Machine Learning

In an unsupervised learning process, the machine learning program is left to observe and analyze large data sets by determining the relationship between variables. There is no operator to make corrections and the algorithm solely addresses data accordingly. As it assesses more data sets, its deductions improve and become refined.

In unsupervised machine learning, the algorithm learns through these techniques:

Clustering. Based on defined criteria, the machine learning program groups similar data from a set by finding a pattern. Clustering is done when each data group needs to be segmented and analyzed.

Dimension reduction. Through predetermined criteria, this process reduces the number of available variables to find the required information.

Reinforcement Machine Learning

In reinforcement machine learning, algorithms are given specific actions, parameters, and results. The algorithm determines the rules and develops different processes for arriving at the given conclusion. The machine learning algorithm arrives at the result through trial and error. Through reinforcement, algorithms learn based on past experiences and adopt the optimal approach determined in new situations.

Top Machine Learning Technologies Are Transforming Ideas

Artificial intelligence technologies are improving outputs in machine learning. Through technology, operators can develop automated machine learning algorithms and achieve more in less time. Some new technologies train machine learning models and others such as Google’s Auto ML Zero — a new kind of automated machine learning — use simple methods to create algorithms that can be adapted to a task at hand.

RapidMiner: an automated ML tool that can significantly reduce the time needed to create predictive models for businesses across many industries with various resources and sizes.

Big ML: technology that automates the machine learning pipeline.

DataRobot: an automated ML tool leveraging regression techniques, neural networks, and gradient boosting to solve multi-class classification problems.

Auto Keras: an open-source software library designed to improve the creation of deep learning techniques

H20: technology that uses its own algorithms to create and optimize pipelines. This is achieved through exhaustive search.

TPOT: AI technology that uses genetic algorithms to optimize machine learning pipelines.

TensorFlow: an open source machine learning framework created by Google that is easy to deploy across various platforms.

Scikit-learn: an open source library developed for machine learning featuring several ML techniques.

Machine Learning Use Cases Are Expanding

Machine learning has many potential applications and is growing rapidly. The exponential growth of data contributes to the expansion and use of machine learning. Not only can machine learning be applied to client-facing applications like product recommendation, customer service, and forecasts, but it can also be used by internal teams to speed up processes and time-consuming tasks.

Here are some machine learning use cases today:

  • Fraud detection
  • Dynamic pricing
  • Product recommendation
  • Email filtering
  • Personalized marketing
  • Process automation
  • Improving sustainability and the environment
  • Proactive help desk operations

At The New Stack, we have focused our coverage of this emerging field mostly around two areas of scalable architecture. We are keeping a close eye on the emerging field of AIOps, where machine learning can influence and drive IT operations. AIOps help to automate the path from development to production, predicting the effect of deployment on production and automatically responding to changes in how the production environment is performing. Companies such as New Relic, OpsRamp, and Moogsoft have all invested heavily in this area.

Another area of machine learning we are covering closely is how Kubernetes and related cloud native technologies can expedite the machine learning lifecycle. Machine learning involves an entire IT cycle of technologies that are very early on in terms of commercialization. Data must be harvested and cleansed, models must be tested, and the most useful approaches must be pressed into production, with a feedback loop of some sort to ensure the models can be updated. Emerging workflows such as Kubeflow and Anaconda can help streamline these processes.

Discover more about machine learning below:

How Machine Learning Works: An Overview

Using Machine Learning to Automate Kubernetes Optimization

Add It Up: How Long Does a Machine Learning Deployment Take?


The New Stack Newsletter Sign-Up
A newsletter digest of the week’s most important stories & analyses.
Do you also want to be notified of the following?
We don’t sell or share your email. By continuing, you agree to our Terms of Use and Privacy Policy.
Cloud Services / Machine Learning
Tutorial: Create Training and Inferencing Pipelines with Azure ML Designer
15 May 2020 8:31am, by Janakiram MSV
Data Science / Machine Learning / Software Development / Contributed
How Solving the Multi-Armed Bandit Problem Can Move Machine Learning Forward
13 May 2020 3:00am, by Dattaraj Rao
Machine Learning / Contributed
Demystifying Deep Learning and Artificial Intelligence
8 May 2020 8:53am, by Levon Paradzhanyan
Cloud Services / Kubernetes / Machine Learning
Build Repeatable ML Workflows with Azure Machine Learning Pipelines
8 May 2020 8:27am, by Janakiram MSV
DevOps / Machine Learning / Security / Contributed
AIOps Readiness in 5 Steps
6 May 2020 9:34am, by Paul Scully
Data Science / Machine Learning / Observability
Databand: Observability for Data Pipelines
27 Apr 2020 12:17pm, by Susan Hall
Cloud Native Ecosystem / Data Science / Frontend Development / Machine Learning
The 2020s Will Be Defined by Scale-Out Data
22 Apr 2020 5:00pm, by Richard MacManus
CI/CD / DevOps / Machine Learning
Could AIOps Play a Role in the Future of IT Operations?
22 Apr 2020 7:41am, by Jennifer Riggins
Data Science / Machine Learning / Contributed
Demystifying Machine Learning: How ML Discovers New Information
22 Apr 2020 3:00am, by Levon Paradzhanyan
Machine Learning / Networking / Observability
New Relic’s Ambitious Plan to Apply AI and ML to Incident Response
16 Apr 2020 1:37pm, by B. Cameron Gain
Data Science / Machine Learning / Software Development
Data Sharing Key to Success for COVID-19 Data Models
16 Apr 2020 7:26am, by Lawrence E Hecht
Data Science / Machine Learning / Contributed
Machine Learning for Twitter Sentiment Analysis
14 Apr 2020 6:00am, by Arawan Gajajiva
DevOps / DevOps Tools / Machine Learning / Sponsored / Contributed
Forrester’s Surprising Discovery About Robotic Process Automation
30 Mar 2020 11:07am, by Wayne Ariola
Data Science / Machine Learning / Contributed
Primer: Demystifying Data Science
26 Mar 2020 8:33am, by Levon Paradzhanyan
Machine Learning / Security / Technology / Contributed
Machine Learning: Cutting Through the Hype
20 Mar 2020 12:00pm, by Dan Perkins
Machine Learning
AI Powers a Potential App for At-Home Coronavirus Risk Assessment
20 Mar 2020 8:53am, by Kimberley Mok
Kubernetes / Machine Learning
Run:AI Brings Dynamic GPU Virtualization to Kubernetes
17 Mar 2020 10:53am, by Mike Melanson
Machine Learning
Deep Learning ‘Capsule Neural Network’ Uses Analog Tech to Predict Extreme Weather
13 Mar 2020 12:00pm, by Kimberley Mok
Machine Learning
This Anti-Ferromagnetic Device May Solve Computers’ ‘Memory Bottleneck’ Problem
6 Mar 2020 10:34am, by Kimberley Mok
Kubernetes / Machine Learning
Kubeflow 1.0 Brings a Production-Ready Machine Learning Toolset to Kubernetes
5 Mar 2020 9:43am, by Mike Melanson
CI/CD / DevOps Tools / Machine Learning / Sponsored / Contributed
How ‘Low-Code’ Can Take Over Robotic Process Automation
2 Mar 2020 9:51am, by Jason Bloomberg
Creative Commons photo of RISC-V prototype by Derrick Coetzee via Wikipedia
Culture / Machine Learning
Open Source Hardware: The Rise of RISC-V
1 Mar 2020 6:00am, by David Cassel
Machine Learning
Tutorial: Use the Amazon SageMaker Python SDK to Train AutoML Models with Autopilot
28 Feb 2020 8:23am, by Janakiram MSV
Machine Learning / Software Development / Contributed
Can AI Find Bugs in Your Code?
27 Feb 2020 3:00am, by Michael Shpilt
Cloud Services / Machine Learning
Train and Deploy Machine Models with Amazon SageMaker Autopilot
26 Feb 2020 6:00am, by Janakiram MSV
Machine Learning
AI Can Analyze and Deconstruct Animal Behaviors Better than Humans
21 Feb 2020 9:58am, by Kimberley Mok
Pagination Previous Button
11 12 13 14 15 16 17 18 19 20
Pagination Next Button
Architecture
  • Cloud Native
  • Containers
  • Edge/IoT
  • Microservices
  • Networking
  • Serverless
  • Storage
Development
  • Cloud Services
  • Data
  • Development
  • Machine Learning
  • Security
Operations
  • CI/CD
  • Culture
  • DevOps
  • Kubernetes
  • Monitoring
  • Service Mesh
  • Tools
The New Stack
  • Ebooks
  • Podcasts
  • Events
  • Newsletter
  • About / Contact
  • Sponsors
  • Sponsorship
  • Disclosures
  • Contributions

© 2022 The New Stack. All rights reserved.

Privacy Policy. Terms of Use.