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Machine Learning Tutorial
  • Basic
    • Introduction To Machine Learning
    • What is Machine Learning?
    • Uses of Machine Learning
    • Applications of Machine Learning
    • Naive Bayes in Machine Learning
    • Dataset Labelling
    • DataSet Example
    • Deep Learning Techniques
    • Dataset ZFS
    • Careers in Machine Learning
    • What is Machine Cycle?
    • Machine Learning Feature
    • Machine Learning Programming Languages
    • What is Kernel in Machine Learning
    • Machine Learning Tools
    • Machine Learning Models
    • Machine Learning Platform
    • Machine Learning Libraries
    • Machine Learning Life Cycle
    • Machine Learning System
    • Machine Learning Datasets
    • Machine Learning Certifications
    • Machine Learning Python vs R
    • Optimization for Machine Learning
    • Types of Machine Learning
    • Machine Learning Methods
    • Machine Learning Software
    • Machine Learning Techniques
    • Machine Learning Feature Selection
    • Ensemble Methods in Machine Learning
    • Support Vector Machine in Machine Learning
    • Decision Making Techniques
    • Restricted Boltzmann Machine
    • Regularization Machine Learning
    • What is Regression?
    • What is Linear Regression?
    • Dataset for Linear Regression
    • Decision tree limitations
    • What is Decision Tree?
    • What is Random Forest
  • Algorithms
    • Machine Learning Algorithms
    • Apriori Algorithm in Machine Learning
    • Types of Machine Learning Algorithms
    • Bayes Theorem
    • AdaBoost Algorithm
    • Classification Algorithms
    • Clustering Algorithm
    • Gradient Boosting Algorithm
    • Mean Shift Algorithm
    • Hierarchical Clustering Algorithm
    • Hierarchical Clustering Agglomerative
    • What is a Greedy Algorithm?
    • What is Genetic Algorithm?
    • Random Forest Algorithm
    • Nearest Neighbors Algorithm
    • Weak Law of Large Numbers
    • Ray Tracing Algorithm
    • SVM Algorithm
    • Naive Bayes Algorithm
    • Neural Network Algorithms
    • Boosting Algorithm
    • XGBoost Algorithm
    • Pattern Searching
    • Loss Functions in Machine Learning
    • Decision Tree in Machine Learning
    • Hyperparameter Machine Learning
    • Unsupervised Machine Learning
    • K- Means Clustering Algorithm
    • KNN Algorithm
    • Monty Hall Problem
  • Supervised
    • What is Supervised Learning
    • Supervised Machine Learning
    • Supervised Machine Learning Algorithms
    • Perceptron Learning Algorithm
    • Simple Linear Regression
    • Polynomial Regression
    • Multivariate Regression
    • Regression in Machine Learning
    • Hierarchical Clustering Analysis
    • Linear Regression Analysis
    • Support Vector Regression
    • Multiple Linear Regression
    • Linear Algebra in Machine Learning
    • Statistics for Machine Learning
    • What is Regression Analysis?
    • Clustering Methods
    • Backward Elimination
    • Ensemble Techniques
    • Bagging and Boosting
    • Linear Regression Modeling
    • What is Reinforcement Learning
  • Classification
    • Kernel Methods in Machine Learning
    • Clustering in Machine Learning
    • Machine Learning Architecture
    • Automation Anywhere Architecture
    • Machine Learning C++ Library
    • Machine Learning Frameworks
    • Data Preprocessing in Machine Learning
    • Data Science Machine Learning
    • Classification of Neural Network
    • Neural Network Machine Learning
    • What is Convolutional Neural Network?
    • Single Layer Neural Network
    • Kernel Methods
    • Forward and Backward Chaining
    • Forward Chaining
    • Backward Chaining
  • Deep Learning
    • What Is Deep learning
    • Overviews Deep Learning
    • Application of Deep Learning
    • Careers in Deep Learnings
    • Deep Learning Frameworks
    • Deep Learning Model
    • Deep Learning Algorithms
    • Deep Learning Technique
    • Deep Learning Networks
    • Deep Learning Libraries
    • Deep Learning Toolbox
    • Types of Neural Networks
    • Convolutional Neural Networks
    • Create Decision Tree
    • Deep Learning for NLP
    • Caffe Deep Learning
    • Deep Learning with TensorFlow
  • RPA
    • What is RPA
    • What is Robotics?
    • Benefits of RPA
    • RPA Applications
    • Types of Robots
    • RPA Tools
    • Line Follower Robot
    • What is Blue Prism?
    • RPA vs BPM
  • Interview Questions
    • Deep Learning Interview Questions And Answer
    • Machine Learning Cheat Sheet

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Machine Learning Platform

By Abhilasha ChouguleAbhilasha Chougule

Machine Learning Platform

Introduction to Machine Learning Platform

Machine learning platforms (Microsoft Azure, IBM Watson, Amazon, H20, ai-one, etc.) are well-organized software system applications used for automating and accelerating the delivery lifecycle of prophetic applications that allow the developer to build their models effectively on the different operating systems and using online tools that can be a paid versions as well as free of cost. Moreover, these online mediums are capable of processing the huge data using techniques related to machine learning.

What is a Machine Learning Platform?

A platform for automating and quicken the delivery lifecycle of prophetic applications capable of huge data processing adopting machine learning or connected procedures.

A few key ideas in this definition are:

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  • Speeding is to induce a fast and quicker resolution delivery lifecycle and additionally to hurrying up the run-time through advanced procedures like distributed and in-memory computing.
  • The bona fide task of the information analyst consists of many tedious and long tasks. Automating these tasks can eliminate project bottlenecks, allowing organizations to deliver new projects that come further quickly, updating, and get more tasks, whereas not increasing staffing.
  • The capability of a machine learning platform for users to serve and process huge amounts of data from a good sort of source.
  • These platforms focus on enabling the full lifecycle of delivering predictive applications as they dissent from PC tools and code libraries.
  • It should be integrated as they are well organized towards software system applications which are highly recommended.
  • It centralizes on assisting trading to know future outcomes like the capability of customers to shop for a given offer or reject the transaction.

Machine Learning Platforms

The field of Machine learning is growing rapidly. Therefore it is very important to choose the proper platform that leads to the success of building models using end-to-end approaches. Here is the list of machine-learning platforms.

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1. Microsoft Azure

A Microsoft Azure machine learning tool permits developers to build the models. It provides SDKs and services to quickly prep information, train, and deploy machine learning models. Improve productivity and prices with automobile scaling cipher & pipelines. Use these capabilities with open-source Python frameworks, such as PyTorch, Tensor Flow, and scikit-learn.

Features

  • It uses the Azure Machine Learning Studio as its interface, having drag & drop environment for building models.
  • It has automated programs to run decision trees, deep neural networks, classification, and regression.
  • It allows only the huge data sets to be uploaded in the Azure cloud and not the smaller data sets from either service provider.
  • It offers standard and free versions with limited features.

2. IBM Watson

IBM Watson platform is developed for both developers and users with lots of AI tools. It provides system programs and queries, prediction and assembles tools to create workbooks. In addition, it allows powerful information visualizations that are assisted with drag-drop surrounding to create models.

Features

  • Front-end interface by using SPSS Graphical Analytics.
  • The information and predictions must be stored in IBM Bluemix.
  • The services that are focused on enterprise clients help to create ML-based applications using API connectors.
  • They are chargeable, and even the free versions are available.

3. Amazon

Amazon Machine Learning platform offers ready-made and simply available prediction models for any developer, even though if they have no idea about data science. A pay-as-you-go model, requiring very little investment in hardware or software packages, has made Amazon one of the simplest ML platform providers an entrant will check in for. In addition, developers can make use of AI toolkits provided by AWS (Amazon web services), which also include Amazon Lex and Amazon Polly.

Features

  • It uses the Amazon Machine Learning sideboard and Amazon Character user Interface.
  • The information must be stocked within an associate AWS account like S3, Redshift, and RDS.
  • It works on a pay-as-you-go model, and for cardinal batch predictions, its prices as very less than ten cents.

4. ai-one

Using an ai-one platform, developers will produce intelligent assistants which will be easily deployed on nearly any software application. The tools list of resources includes developer APIs, a document library, and building agents that will be used to turn information into rule sets that support ML and AI structures.

5. Apache PredictionIO

It is an open-source stack with an open-source server for machine learning designed; on top of it, Apache PredictionIO is the simplest way to create prophetical engines that will meet any machine learning task. In addition to the event server and, therefore, the platform itself, Apache PredictionIO additionally includes a model gallery.

6. H2O

This platform was designed for programming languages like python, R & Java by H2O.ai. It conjointly offers tools needed to analyze data sets in the Apache Hadoop file systems and the cloud.H2O.ai is predicated in Mountain View, CA. It offers the free, open-source H2O OpenThis platform designed for programming languages like python, R & Java by H2O.ai. It also offers the tools required to analyze data sets in the Apache Hadoop file systems and cloud. H2O.ai is based in Mountain View, CA. and offers the free, open-source H2O Open Source Machine Learning (H2O, Sparkling Water, and H2O4GPU) and a commercial product called H2O Driverless AI. H2O.ai’s components are highly optimized and parallelized for central processing unit multicore and multinode configurations.

Conclusion

This article gives a brief introduction to machine learning platforms. Machine Learning can be a Supervised or Unsupervised technique of training machines to perform the activities bit faster and better than an average human being. When it comes to the development of machine learning models of your own, there are choices of various development languages, IDEs, and Platforms. This article gives the best platforms the user can use; it can be either cloud-based or production-based platforms.

Recommended Articles

This is a guide to the Machine Learning Platform. Here we discuss the basic concept, different Platforms of Machine learning with Features. You may also look at the following article to learn more –

  1. Machine Learning Methods
  2.  Machine Learning Methods
  3. Machine Learning Architecture
  4. Loss Functions in Machine Learning
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