In order to develop ML applications, one must decide on a platform, IDE and language. There are many options available, all of which provide the implementation of Artificial Intelligence algorithms. When developing an ML algorithm, it is important to consider the language of choice, the IDE used, and the platform for development and deployment.
The language chosen should be one in which the developer is proficient. There are many options for IDEs, and the best choice will depend on the developer’s comfort level and familiarity with the existing IDEs. Various platforms are available for development and deployment, some of which are free-to-use. In some cases, a license fee may be incurred beyond a certain amount of usage. Common choices of language, IDE, and platform are listed below for reference.
Language Choice
When developing ML applications, one must choose a platform, IDE, and language. There are numerous options available, all of which provide the implementation of AI algorithms. It is important to consider the language of choice, the IDE used, and the platform for development and deployment.
Common language choices for ML development include Python, R, Matlab, Octave, Julia, C++, and C. Depending on the developer’s comfort level and familiarity with the existing IDEs, they can choose an IDE that best suits their needs. Additionally, various platforms are available for development and deployment, some of which are free-to-use. In some cases, a license fee may be incurred beyond a certain amount of usage.
IDEs
IDEs are a type of software designed to facilitate programming and development. There are many popular IDEs that support ML development, such as R Studio, Pycharm, iPython/Jupyter Notebook, Julia, Spyder, Anaconda, Rodeo, and Google–Colab. Each of these IDEs has its own set of features and benefits, so it is important to take the time to explore them and determine which one is best suited to your needs.
Platforms
IDEs are specialized software programs designed to assist with programming and development. Popular IDEs for ML development include R Studio, Pycharm, iPython/Jupyter Notebook, Julia, Spyder, Anaconda, Rodeo, and Google–Colab, each of which has its own unique features and benefits. It is important to explore the different IDEs before deciding which one is the best fit for your project.
ML applications can be deployed on a variety of platforms, including IBM, Microsoft Azure, Google Cloud, Amazon, and Mlflow. It is beneficial to sign up for these services and experiment with them to get a better understanding of their capabilities and features.