The year 2022 is ready to use Machine learning that will be useful for industrial workings, economic and social.
The Machine Learning industry is developing at a quick rate and gives sufficient scope to companies to bring vital change.
Throughout recent years, there have been new searches in machine learning. Many organizations have been able to accomplish the business objectives. For developing machine learning there are some platforms as like below:
IBM Watson Machine Learning
IBM Watson Machine Learning useful to Developers and Data Scientists for machine-learning deployment. Watson Machine Learning simplifies Businesses.
Watson Machine learning features
- One-Click Deployment
- Deploy any model at Scale
- Model Operations
- Auto AI: AI lifecycle management
- Integrated UI end to end
- Dynamic retaining
R Studio
RStudio is an integrated development environment (IDE) for R, which is a programming language for graphics and computing. It has two formats: RStudio Server runs on a remote server and R Studio Desktop is used for desktop applications.
It has a syntax-highlighting editor for supporting direct code execution.
RStudio IDE Features
- Execute R code from the source editor.
- Code completion, Syntax highlighting, and smart indentation.
- Jump to function definitions.
Alteryx
Alteryx is a data analytics platform designed for business data analysts. It provides share options, centralized analytics deployment, and is securely hosted to encourage effective collaboration. Analysts store analytics workflows or apps in a private environment. It also adds version controls.
- Build data models with drag-and-drop workflows
- Share and Create data insights via reports and apps
- Blend, analyze and prepare data via repeatable workflows
- Visualize data models and blends with Alteryx Designer
- Connect, access & blend business data from multiple sources
- Share analytic apps within private teams or publicly
- Use interactive visualizations to confirm model results
- Drag and Drop predictive tools based on the R language
- Output spatial analytics to mappings & visualizations
- Add security and governance with Alteryx Server
Anaconda Enterprise
Anaconda Machine learning (ML) is a subset of artificial intelligence in which data scientists use statistical models and algorithms and for outcomes and to perform specific tasks. Machine Learning models may “learn from” data sets to improve their image.
Anaconda Enterprise has not fixed pricing information product or service. That is common for software service providers and sellers. To obtain the current pricing of it contact anaconda enterprise.
To install and download Anaconda is the speediest way to start with machine learning or data science. You have to consider miniconda, when you don’t have the disk space for what comes with anaconda.
Features of various Frameworks
Machine/Deep Learning Services
- Artificial Neural Networks
- Computer Vision
- Natural Language Generation
- Natural Language Processing
Model Development
- Drag and Drop
- Language Support
- Model Training
- Pre-Built Algorithms
Deployment
- Managed Service
- Scalability
- Application
IBM SPSS Modeler
IBM SPSS Modeler is a platform from IBM. It gives intelligence to everyday business problems. It provides a wide range of entity analytics, text analytics, automated modeling, social network analytics, decision optimization, data preparation, and decision management.
It has three editions as like below:
-
IBM SPSS Modeler Professional :
It gives automated modeling, advanced algorithm, automated preparation, and modeling to uncover hidden patterns in structured data and to build predictive models.
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IBM SPSS Modeler Premium :
It gives social network analysis, text analytics, automated modeling.
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IBM SPSS Modeler Gold :
It can build and deploy models into the business processes.
IBM SPSS Modeler Features
- Natural Language Processing to sentiments in text and to extract concepts.
- No need for programming for interaction.
- Integration to Infosphere, Congos, and Netezza.
- Data Preparation and automated modeling capabilities.
- You can access all unstructured and structured from disparate sources.
Datbricks
Databricks Unified Analytics offers Apache Spark-based unified analytics platform. It also offers a cloud that combines data engineering and data science functions. Amazon Web Services includes features for performance, operationalization, and real-time enablement.
A Data Science Workspace enables users to build models and explore data. It permits one-click access to preconfigured Machine Learning environments with popular frameworks.
With Data bricks as your Unified Data Analytics Platform, you can quickly clean and prepare data at a massive scale with no limitations. The platform permits you to deploy and train ML models for all of your artificial intelligence applications. The top advantages with a Databricks Platform are:
- Make big data simple
- Innovate with big data
- Unifying data engineering and data science
Google Cloud AI Platform
Google Cloud AI offers many products for a variety of uses. It has a products list like Google BioQuery, Google Cloud AutoML, and Google Tensor Flow. It also offers machine learning stacks in space. It is fully managed by interoperable models.
- Data Labeling Service
- Validation via AI Explanations
- AutoML
- MLOps via the Pipeline tool
- Cloud model deployment with Prediction
Data Robot
Data Robot gives an end-to-end process for building, deploying, and maintaining AI. It has products like MLOps, Automated Machine Learning, and Automated Time Series. Those are also called fully integrated tools of Data Robot.
The integrated tool can be deployed in multiple ways to match business needs and IT requirements. It also offers an enterprise AI platform. The products are powered by open-source algorithms. It is fully managed by AI services.
Azure Machine Learning
The Azure Machine Learning service allows data scientists and developers to build, train, and deploy machine learning models. It has products like Azure HD Insight, Azure machine learning studio, Machine learning server, Azure Data Factory, Azure Data Bricks, and Power BI.
It has features like expansive MLops capabilities that integrate with DevOps processes. It protects data with confidential computing and differential privacy. Azure Machine Learning supports open-source frameworks and languages like Kubeflow, MLflow, Python, R, ONNX, Tensor Flow, and PyTorch.
Azure Machine learning platform is a secure, trusted platform and designed for responsible Machine Learning app.
- Open and Interoperable
- End-to-end MLOps
- ML for all skills
- State-of-the-art Responsible ML
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