Photo by charlesdeluvio on Unsplash
(Photo : charlesdeluvio on Unsplash)

Machine Learning (ML) has become a pivotal technique for maximizing the full potential of data, enabling firms to be more inventive, productive, and responsive. Machine Learning aims to "learn" from source data and build an appropriate model by consistently predicting, optimizing, and tweaking model parameters. A new finding in machine learning is that it is able to recreate modeling complexity with substantial training datasets. This is not surprising from a research standpoint, given the ML community has focused significantly on the development of ML models, but not on implementing development-ready machine learning products. 

For example, in many commercial-based applications, machine learning engineers still handle workflows explicitly to a large extent, resulting in numerous operational challenges for the particular solution. The relevance of deploying ML and other AI state-of-the-art technologies to transform data into insights, optimize business decisions, and enhance goods and services rises as organizations collect more data.

Here, we introduce best machine learning platforms that integrate sophisticated algorithms with data, allowing you to get organizational insights and implement new solutions at broader scale.

1. Microsoft Azure 

Microsoft Azure is a ML solution creation and management service. It is intended to assist machine learning engineers and data analysts in capitalizing on their current data processing and framework creation methods. 

You can  develop a framework in Microsoft Azure Machine Learning or import one from an opensource tools including python frameworks, including PyTorch, Keras, and scikit-learn.

Features

  • It incorporates the Azure Machine Learning Studio as its UI, provides a bragging and dragging context for implementing models.

  • It includes novel algorithms for running random forest, CNN, classification and regression.

  • It provides Regular and free versions with basic features.

2. RapidMiner 

RapidMiner is a complete package for Machine Learning augmented with visual process design and automation. It implies we are exempt from coding for data science jobs. Rapidminer is among the most widely machine learning tools.

Features

  • It is a free, open-source application for textual data.

  • RapidLiner supports numerous operators or methods for interacting with data. The data can be saved in different file formats such as CSV or spreadsheet, or even in a database including Microsoft SQLServer table.

3. Qwak

Qwak is a secure production machine learning platform that eliminates the friction between the machine learning research and development phases. Qwak enables ML  engineers and data engineers to design, install, and track development models with little engineering friction. The main goal is to enable businesses to achieve ML-based production without expending interminable resources on architectures and networking.

Features

  • It facilitates the accessibility, reusability, and precision of features.

  • The Qwak-based system formalizes an ML project structure in which data, code, and features are dynamically versioned for every framework build.

4. Peltarion Platform

The Peltarion Platform is a no-code machine learning platform that enables rapid and scalable development of economically viable AI-powered solutions. Activate the AI's possibilities in your corporation by placing it in the hands of field experts and encouraging organization wide collaboration. Design, train, evaluate, and deploy your AI models via a single tool.

Features

  • Supports Business AI.

  • Bypass the need to learn new frameworks, troubleshoot code, and cope with dependency hell.

5. IBM Watson

With numerous AI tools, the IBM Watson platform is designed for both developers and end users. It provides programs and queries, forecasting, and workbook assembly tools. In addition, it enables the creation of models with the use of drag-and-drop information visualizations.

Features

  • IBM Watson provides rapid data access. It integrates to data without requiring extensive data preparation.

  • Using automation, IBM Watson swiftly recognizes what data is attempting to communicate.

  • With the use of trade-off analytics, it is possible to eliminate needless options and establish the optimal solutions among numerous goals.