2024’s Best Machine Learning Platform: Top 6 Picks

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Artificial intelligence has been a muse to many tech experts globally for as long as one can remember. Machine learning has provided the platform to turn AI into an advancing reality. Some of the best Machine learning platforms come with advanced AI tools that improve AI machines even more effectively. Modern-day AI tools can make data-driven decisions, tackle challenging issues, and develop creative AI outputs thanks to these Machine learning tools and platforms. This has also led to increased demand for ML development services.

We have curated the list of best machine learning platforms that can be used to develop excellent AI solutions.

Top 6 Machine Learning Platforms in 2024

• Amazon SageMaker
• Google Cloud AI Platform
• Microsoft Azure Machine Learning
• IBM Watson Studio
• Salesforce Einstein
• DataRobot

Let’s explore more about these machine-learning platforms to streamline your AI development journey.

Amazon SageMaker

Amazon SageMaker is an excellent tool for designing, training, and deploying machine learning models at any scale. One can easily import frameworks and libraries and utilize built-in algorithms on Amazon. It helps build well-refined AI models and is known for Amazon’s AutoPilot tool.

Solutions built with the Sage Maker tool are also convenient, as they can be easily deployed in minutes. These solutions are equipped with a broad set of capabilities and many features. They are customizable while supporting 150+ open-source models of NLP, object identification, and more.

Projects developed using the Amazon SageMaker tool are used for a variety of uses, such as :

• Extract & Analyze Data
• Churn prediction and improved retention
• Fraud detection
• Improved customer experience and performance marketing, amongst others.

Google Cloud AI Platform

Another machine learning platform making waves in 2024 is Google Cloud AI platform The platform developed by tech giant Google is a comprehensive suite that offers a holistic development environment for the entire Machine learning cycle. Each machine learning model built can handle large-scale tasks, manage a vast resource tool, make data-driven decisions, and more.

Google’s AI platform has all the components to build AI solutions. Components such as training, prediction, and data labeling services help solutions be customizable, flexible, and easily deployable. A few other features and tools of the Google Cloud AI platform set it apart and allow users to interact :

• Google Cloud Console- A console that will enable the deployment of models to the cloud and manage them on the cloud.
• The Google Cloud CLI- The command line tool that helps manage models and versions, submit jobs, and accomplish other AI Platform tasks
• REST API- provides RESTful services for managing jobs, models, and versions and for making predictions
• Vertex AI Workbench user-managed notebooks- user-managed notebooks instances let you create and manage deep learning virtual machine (VM) instances
• Deep Learning VM- a set of virtual machine images optimized for data science and machine learning tasks

Microsoft Azure Machine Learning

Next on our list is Microsoft Azure. Almost all AI developers have heard of Microsoft Azure and the features it brings. The entire machine learning suite of Microsoft Azure allows developers to train, develop, and integrate ML models into the Microsoft Cloud. Its tools, such as Tensor Flow, PyTorch, sci-kit-learn, and ONNX, offer data preparation, labeling, exploration, model validation, and deployment features provided by Azure Machine Learning.

Azure integrated tools, open-source interoperability, and industry-leading machine learning operations (MLOps) speed up time to value. This reliable AI learning platform is ideal for building responsible and ethical machine learning applications.

IBM Watson Studio

IBM Watson Studio is another excellent Machine Learning Platform used by data scientists and developers to build consistent AI models. These solutions make optimal decisions and automate all data lifecycles on the IBM cloud.

The platform also brings together many open-source frameworks like PyTorch, TensorFlow, and scikit-learn, as well as IBM and its ecosystem tools for code-based and visual data science. The studio supports various data sources and the latest automation technologies. A few other benefits include:

• AutoAI for faster experimentation
• Advanced data refinery
• Open-source notebook support
• Integrated visual tooling
• Model training and development
• Supports extensive open-source frameworks

Salesforce Einstein

Leaving Salesforce Einstein out of this list of best Machine Learning platforms would be a mistake. Salesforce Einstein is a popular ML platform integrated with a set of AI technologies by its CRM platform. Einstein can be coupled with all salesforce products known as Customer 360 and can help predict new sales opportunities and resolve service cases to create predictive journeys.

Einstein is known to boost efficiency, identify patterns in data, and give predictive insights. Some of the ways Einstein can be used are shown.

• Build custom predictions and recommendations with clicks

• Embed predictive insights into any record or in any app

• Operationalize AI by adding it to every workflow or business process

DataRobot

Last, DataRobot provides a robust platform for building and deploying intelligent AI models. It allows developers to develop and implement highly accurate machine learning models with Data Robot. With the help of the Tableau DataRobot Insights plugin, the solutions can obtain predictive insights quickly. After a model is created in DataRobot, Tableau analysts can also maximize and democratize the value of machine learning by slicing and dicing information into intelligent, actionable dashboards with a few mouse clicks. The outcomes of DataRobot machine learning models offer more profound analysis and forecasts, which facilitate improved decision-making.

The DataRobot Economic platform is known to have the following benefits:

• 83% faster operationalization of AI solutions
• 93% speedier propagation and adoption of analytics capabilities
• 75% to 80% cost savings compared to using existing resources or building new teams

Why invest in Best Machine Learning Platforms?- Conclusion

In the current dynamic scenario, companies continuously seek novel approaches to maintain their competitive edge and spur expansion. Machine learning is a revolutionary technology that has become a game-changer by enabling organizations to get valuable insights, streamline workflows, and make data-driven choices like never before by utilizing artificial intelligence and data analysis. More and more companies must hop onto this bandwagon and hire Machine Learning Developers to make the best of Machine learning and Artificial Intelligence in their applications.