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Not-to-Miss Top 7 Considerations while selecting an AI Platform

February 10, 2024

Not-to-Miss Top 7 Considerations while selecting an AI Platform

With artificial intelligence becoming commonplace; it is indispensable to target the creation, evaluation, implementation, and update of deep learning, AI, and ML models in a scalable manner. McKinsey Global Institute estimates that in the manufacturing sector, emerging technologies that use AI will reach USD 3.7 trillion by 2025.

From image recognition to text-to-speech features to programming language support to templates for optimization. These AI platforms imitate human intelligence in digital form and transform complex tasks into intuitive processes. Let us take you through Artificial Intelligence Platforms and how it works magic.

What is an Artificial Intelligence Platform?

The Artificial Intelligence Platform is an integrated set of technologies that allow people to develop, test, deploy, and refresh machine learning and deep learning models. AI platforms are needed as they offer a wide range of capabilities and help organizations of all sizes to streamline operations; make data-driven decisions, deploy AI applications effectively, and achieve a competitive edge.

How can you use an AI Platform?

You can submit your input data for the AI platform to train using a built-in algorithm (beta). If the built-in algorithms do not fit your use case, you can submit your training application to run on the AI Platform or build a customer container with your training application and its dependencies to run on the AI Platform.

Top 5 AI Platforms of 2024:

  • 1: KERAS

    Best for deep learning model flexibility; Keras is a high-level neural networks API that is built on top of TensorFlow, Theano, and Microsoft cognitive toolkit. It supports multiple backends and is a user-friendly API that speeds up the prototyping process. It offers comprehensive documentation and active community support.


    Best for Automated Machine Learning; DataRobot is a pioneering AI platform that is designed to democratize data science. It offers tools for model validation, deployment, and monitoring. It fosters built-in feature engineering, ensuring better model performance.

  • 3: PYTORCH

    Best for dynamic neural network graphs; PyTorch supports dynamic computation graphs, provides pre-trained models and datasets, and effective integration with Python libraries.

  • 4: OPENAI

    Best for research-driven AI models; OpenAI is a renowned organization that guides the boundaries of AI through research and development. It has made its models accessible via APIs, ensuring developers can embed these powerful tools into a variety of applications and platforms.

  • 5: APORIA

    Best for monitoring machine learning in production, Aporia is an advanced platform tailored to monitor machine learning models once they are deployed into production spaces. It allows users to track model drift, anomalies, and performance degradation over time. You can also make use of custom alerting to notify stakeholders when the model’s behavior deviates from the expected.

How to choose the best AI Platform in 2024?


    Look for an AI Platform that applies automation strategically in ways that make it convenient for your team to amplify the automation of proven AI ML models with consistent performance. It must be capable enough to recalibrate automation as necessitated; based on the accuracy of outcomes.


    Look for an AI platform that enables your IT administrators to control and govern the software supply chain, giving them the tools they need to manage user access and provide details for audits.

  • 3: SCALE

    Look for an AI platform that can deploy as you like- online or offline; and can handle your organization’s requirements, and use cases, and stay put as you scale your AI applications. It must be able to possess confidence that they can quickly identify and mitigate security risks.


    Look for an AI platform with maintainers and authors who are experts in the tools and techniques that your team would use to take machine learning models from the build stages to high-performing production deployments.

  • 5: SUPPORT

    Look for an AI platform that provides the support your team needs, from onboarding support to resources for learning, to just-in-time support when you need it. It offers reliable bug reporting and tracking, engaging training resources, and ongoing support.


    Look for an AI platform that allows you easy access to tools such as Jupyter Notebook, Keras, Matplotlib, Scikit-learn, and TensorFlow. It centralizes workflows on these tools.


    Look for an AI Platform that maintains strong connections to the open-source community and its contributors. It assists in supporting innovation and robust security with vulnerability curation by their team and the ecosystem.

    Let’s look at the key benefits of an AI Platform:

    • Enhanced automation
    • More scalability
    • Better integration
    • Spike in Security
    • Improved governance
    • Technical support

Final Word:

No doubt; there is a plethora of AI platforms available in the global space today. Selecting the best-suited AI platform calls for an informed decision. If you are an aspiring AI professional, planning to build a thriving AI career shortly; it is essential to gain mastery of these popular AI tools and AI platforms. The most trusted and convenient way of earning the said skill is through a popular global AI certification from a global vendor. With the popular use cases of AI platforms ranging from healthcare to retail, financial services, and beyond. It is deemed critical to comprehend these skills, tools, and platforms to deal with the staggering rise of Artificial intelligence ahead. Make the move now!