Revealing the top 10 machine learning platforms of 2024 that AI enthusiasts must use

Artificial Intelligence is changing thanks to machine learning platforms. These platforms offer strong tools that let AI engineers and data scientists create, train, and implement machine learning models effectively. Professionals can use these platforms to use machine learning's potential to tackle challenging issues, make data-driven decisions, and develop creative AI solutions. Because of this, machine learning platforms are quickly evolving into a vital tool in the artificial intelligence toolbox.

Here are 10 of the best machine-learning platforms you need to know for 2024:

1. Google Cloud AI Platform:

Regarding creating, implementing, and maintaining machine learning models, this all-inclusive and integrated platform provides a range of tools and services. Using TensorFlow, PyTorch, sci-kit-learn, or XGBoost, you can build your unique models or use Google's pre-trained models. To automatically produce high-quality models with little code, you can also take advantage of Google's AutoML features.

2. Amazon SageMaker:

You can quickly and simply design, train, and deploy machine learning models at any scale with this fully managed service. You can bring your frameworks and libraries, or you can utilize the built-in algorithms on Amazon. To automatically generate and fine-tune the optimal models for your data, you may also utilize Amazon's AutoPilot tool.

3. Microsoft Azure Machine Learning:

Using a range of tools and frameworks, you can create, train, and implement machine learning models on this cloud-based platform. You can use TensorFlow, PyTorch, sci-kit-learn, ONNX, or ML.NET to develop your models, or you can use the pre-built models that Azure offers. Features including data preparation, data labeling, data exploration, model validation, model deployment, model management, and model interpretability are also provided by Azure Machine Learning.

4. IBM Watson Studio:

Using a variety of tools and frameworks, you can create, execute, and oversee machine learning models on this collaborative platform. IBM offers pre-trained models that you can utilize, or you can use TensorFlow, PyTorch, sci-kit-learn, Keras, or Spark MLlib to build your models. In addition, IBM Watson Studio offers functions like model training, model deployment, model governance, data gathering, transformation, analysis, and visualization.

5. Salesforce Einstein:

You may develop and implement machine learning models for a range of business use cases, including marketing, sales, customer support, and analytics, with this platform. Using a drag-and-drop interface or code-based tools, you can build your models or use Salesforce's pre-built models. The AutoML feature in Salesforce may also be used to automatically create and enhance models for your data.

6. Ai:

You can use open-source tools and frameworks on this platform to create, train, and implement machine-learning models. You can utilize frameworks like TensorFlow, PyTorch, or MXNet, or you can use H2O's algorithms. Additionally, you can utilize H2O's AutoML tool to compare and automatically produce the best models based on your data. Features including data intake, data transformation, data visualization, model interpretation, model deployment, model monitoring, and model governance are also offered by H2O.ai.

7. Databricks:

Using a unified data and AI platform, you may use this platform to create, train, and implement machine learning models. You can bring your frameworks, like TensorFlow, PyTorch, sci-kit-learn, or XGBoost, or use the ML Runtime provided by Databricks. Additionally, you may automatically generate and optimize models for your data by utilizing Databricks' AutoML tool. Additional services provided by Databricks include model testing, deployment, management, and optimization, as well as data science, data analytics, data engineering, and data visualization.

8. DataRobot:

Using an interface that doesn't require code, this platform assists you in creating, honing, and implementing machine learning models. You are free to use your frameworks and libraries or utilize DataRobot's algorithms. Additionally, you can utilize DataRobot's AutoML capability to automatically build and enhance models based on your data. Features including data preparation, exploration, visualization, validation, deployment, monitoring, and explainability are also offered by DataRobot.

9. RapidMiner:

Using either a code-based environment or a visual workflow designer, this platform lets you create, train, and implement machine learning models. Use your frameworks and libraries, or use RapidMiner's algorithms. RapidMiner's AutoML capability may also be used to automatically create and assess models based on your data. Other services provided by RapidMiner include model testing, deployment, maintenance, and improvement, as well as data integration, transformation, analysis, and visualization.

10. KNIME:

Using a graphical user interface or a scripting language, you may create, train, and implement machine learning models on this platform. You are welcome to bring your frameworks and libraries or use KNIME's nodes. Additionally, you may utilize KNIME's AutoML capability to automatically build and enhance models based on your data. Features including data access, manipulation, exploration, visualization, validation, deployment, monitoring, and interpretation are also offered by KNIME.