10 AI tools you can use to build and train deep learning models

In 2023, you can’t ignore the growth of AI tools as a software engineer. It is essential that you put them to your advantage as they have a major impact on the field. Here are ten resources you can use to embrace this shift and grow as a software engineer.

1. TensorFlow

TensorFlow is an open-source machine learning model development and deployment tool. It is a great resource for software engineers interested in learning about AI and machine learning. TensorFlow allows you to quickly and easily build and train deep learning models.

2. Keras

Keras is another open-source platform for developing and implementing deep learning models. It’s simple to use and understand, making it an excellent tool for beginners. Keras works seamlessly with TensorFlow, making it simple to use alongside other AI tools.

3. PyTorch

PyTorch is a famous open-source machine learning platform used by software developers. It’s well-known for its simplicity and adaptability, making it an excellent instrument for developing and deploying deep learning models.

4. Caffe

Caffe is a popular deep learning framework in the business. It is quick and efficient, making it ideal for real-time apps. Caffe is also simple to use and has a large developer group that contributes to its advancement.

5. Amazon SageMaker

Amazon SageMaker is a platform for developing and implementing machine learning models in the cloud. It’s a great tool for software engineers who want to use AI without having to handle the underlying infrastructure. SageMaker is simple to use and works well with other AWS applications.

6. Microsoft Azure Machine Learning

Microsoft Azure Machine Learning is a cloud-based platform for building and deploying machine learning models. It is an excellent tool for software engineers who want to leverage the power of AI on the Microsoft Azure cloud. Azure Machine Learning is easy to use and integrates seamlessly with other Azure services.

7. Google Cloud AI Platform

Google Cloud AI Platform is a cloud-based platform for building and deploying machine learning models. It is an excellent tool for software engineers who want to leverage the power of AI on the Google Cloud. Google Cloud AI Platform is easy to use and integrates seamlessly with other Google Cloud services.

8. H2O.ai

H2O.ai is an open-source machine learning platform that is designed to make it easy to build and deploy machine learning models. It is an excellent tool for software engineers who want to leverage the power of AI without having to write complex code. H2O.ai is easy to use and has a robust community of developers who contribute to its development.

9. IBM Watson Studio

IBM Watson Studio is a cloud-based platform for building and deploying machine learning models. It is an excellent tool for software engineers who want to leverage the power of AI on the IBM Cloud. Watson Studio is easy to use and integrates seamlessly with other IBM Cloud services.

10. OpenAI

OpenAI is an AI research institute that is dedicated to advancing artificial intelligence in a safe and beneficial way. It is an excellent resource for software engineers who want to learn about the latest developments in AI and machine learning. OpenAI provides access to cutting-edge AI research and tools that can be used to build and deploy AI applications.

Finally, as a software engineer in 2023, you can’t afford to ignore the rise of AI tools. They have a major impact on the industry, and it is critical to capitalize on them. These ten resources will assist you in embracing change and becoming a more effective software engineer.