Machine learning platforms are transforming the field of artificial intelligence. These platforms provide robust tools that enable data scientists and AI engineers to efficiently develop, train, and apply machine learning models. These platforms enable professionals to take on difficult problems, make data-driven decisions, and create innovative AI solutions by utilizing machine learning. As a result, machine learning systems are becoming an indispensable part of the artificial intelligence arsenal.
The top ten machine-learning platforms for 2024 are as follows:
Google Cloud AI Platform: With respect to developing, putting into practice, and managing machine learning models, this comprehensive and integrated platform offers a number of resources. You can use Google’s pre-trained models or create your own using TensorFlow, PyTorch, sci-kit-learn, or XGBoost. You can also utilize Google’s AutoML tools to automatically generate high-quality models with minimal coding.
Amazon SageMaker: With this fully managed service, you can easily and quickly develop, train, and implement machine learning models at any size. You can use Amazon’s built-in algorithms, or you can use your own frameworks and libraries. You may also make use of Amazon’s AutoPilot tool to automatically create and refine the best models for your data.
Microsoft Azure Machine Learning: On this cloud-based platform, you can develop, train, and apply machine learning models using a variety of tools and frameworks. You can create your own models using TensorFlow, PyTorch, sci-kit-learn, ONNX, or ML.NET, or you can use the pre-built models that Azure provides. Azure Machine Learning additionally offers features including model validation, model deployment, model management, model interpretability, data preparation, labeling, and exploration.
IBM Watson Studio: This collaborative platform allows you to design, run, and manage machine learning models using a range of tools and frameworks. You can use the pre-trained models that IBM supplies, or you can create your own models using TensorFlow, PyTorch, sci-kit-learn, Keras, or Spark MLlib. IBM Watson Studio also provides tools for data collection, transformation, analysis, and visualization in addition to model training, model deployment, and model governance.
Salesforce Einstein: This platform allows you to create and apply machine learning models for a variety of business use cases, including as analytics, customer service, sales, and marketing. You can create your own models or utilize Salesforce’s pre-built models by using a drag-and-drop interface or code-based tools. You may also utilize Salesforce’s AutoML capability to automatically build and improve models for your data.
AI: You may build, train, and apply machine-learning models on this platform by utilizing open-source tools and frameworks. You can use H2O’s algorithms, or frameworks such as TensorFlow, PyTorch, or MXNet. Furthermore, you may compare and automatically generate the optimal models based on your data with H2O’s AutoML tool. H2O.ai also provides functions including data input, data transformation, data visualization, model interpretation, deployment, monitoring, and governance.
Databricks: You can build, train, and apply machine learning models with this platform, which combines AI and unified data. You can utilize the ML Runtime offered by Databricks, or you can bring your own frameworks, such as TensorFlow, PyTorch, sci-kit-learn, or XGBoost. Additionally, you may use Databricks’ AutoML tool to automatically develop and optimize models for your data. In addition to data science, data analytics, data engineering, and data visualization, Databricks also offers model testing, deployment, management, and optimization.
DataRobot: This platform helps you develop, refine, and apply machine learning models through an interface that doesn’t require programming. You can make use of DataRobot’s algorithms or your own frameworks and libraries. Additionally, you may automatically create and improve models based on your data by using DataRobot’s AutoML capabilities. DataRobot also provides features like explainability, validation, exploration, visualization, deployment, and monitoring.
RapidMiner: This platform allows you to construct, train, and apply machine learning models. You can use it via a visual workflow designer or a code-based environment. Utilize RapidMiner’s algorithms or your own frameworks and libraries. You may also use RapidMiner’s AutoML feature to automatically build and evaluate models based on your data. Additional services offered by RapidMiner comprise data integration, transformation, analysis, and visualization in addition to model testing, deployment, maintenance, and improvement.
KNIME: On this platform, you may develop, train, and apply machine learning models using a graphical user interface or a scripting language. You are free to use KNIME’s nodes or bring your own frameworks and libraries. You may also use KNIME’s AutoML feature to automatically create and improve models using your data. KNIME additionally provides features including deployment, monitoring, interpretation, validation, manipulation, exploration, and visualization of data.
