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Azure Machine Learning vs IBM Watson: Software comparison

Find out what features you can expect to see in Azure Machine Learning or IBM Watson. This will help you decide which artificial intelligence solution suits you best.

Artificial intelligence has the potential to revolutionize everything, from robotic surgeons to self-driving cars, and it is at the forefront of tech innovation. Microsoft’s Azure Machine Learning (IBM’s Watson) are two of the most well-known AI services. Both offer impressive functionality. But which one should your business choose?

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What is Azure Machine Learning?

Azure Machine Learning allows developers and data scientists to build, deploy, and train ML models using a cloud-based platform. The rich toolkit makes it easy for you to develop predictive analytics solutions. This service allows you to create predictive models with a variety ML algorithms such as classification, regression, and clustering.

What is IBM Watson?

IBM Watson Studio is a platform for data scientists and software developers to build, run, manage, and scale machine-learning capabilities that can be embedded in applications. It provides the tools and resources necessary to create cognitive services starting with business ideas and hypotheses and ending with the deployment, management, and scaling of machine-learning models.

Training and development models

Azure ML has more features than Watson for data preparation, transformation and normalization, as well as model training. You can also use it to train better models in less time than IBM Watson. It is easier to build high-performing models using the Azure ML platform than the IBM Watson platform in terms of performance and platform capabilities.

Azure ML, despite offering similar tools, is suitable for developers who want to create complex predictive models with complicated toolsets such as Python and Jupyter notebook. They can also collaborate online, even if they don’t have a costly development environment. IBM Watson, on the other hand, offers solutions to help developers with less skills. These include cognitive services like natural language processing.

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Drag-and-drop designer

Azure’s drag and drop interface makes machine learning easy. IBM may be better suited if you are looking to create advanced models, such as one that incorporates reinforcement learning and neural networks.

Both platforms are essentially identical if you have the ability to code in R or Python and are ready to learn. Azure is more focused on creating easily-trainable models with drag-and-drop tools than custom scripts. This is the key difference between them.

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IBM, on the other hand, is made for companies that want to train custom algorithms using deep learning frameworks such as TensorFlow or PyTorch. Azure is the best choice if you prefer to drag boxes around than write code.

Natural language processing

Cognitive services provide a wide range of APIs that use natural language processing and other applications. Machine learning models are used to interpret text, speech, and images.

IBM Watson Studio offers better natural language processing tools, which make it easier for business users get the most out of data. The data analysis tool is also improved, which allows you to work with large data sets and uncover insights. The IBM Watson visual recognition tools are also fantastic. These tools let you run image recognition analysis on visual assets.

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Azure offers a number of excellent cognitive services that developers can use. Their Computer Vision API, for example, can be used to classify objects in an image or video stream. This is useful if you want to create an app that detects what’s going on in a video or photo. IBM Watson is the best choice if your employees don’t have data science backgrounds and require interaction with advanced NLP technology.

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