Choosing the right framework is a question every data scientist deals with. Data Science is a relatively new field so there is no general agreement on the best frameworks, tools, and languages to use. However, some machine learning frameworks are used more widely among data scientists for training algorithms for tasks like image recognition, prediction, and recommendation etc.
Frameworks greatly help data scientists in their work and do not even require extensive programming experience. They focus on the big picture and help in data mining and analysis. Instead of getting overwhelmed over the tedious details of a code, frameworks allow programmers to see the whole picture allowing them to put more effort into high-level features and functioning. Thus frameworks help build better algorithms.
To select the right framework, it is important to consider some pertinent questions like Will it be used for deep learning or classical machine learning algorithms? and What programming language would be the right fit for developing AI models? For example, a few good machine learning framework options for deep learning include TensorFlow, MXNet, and Caffe. As for programming languages, Python and R are two examples of high ranking programming languages for machine learning.
After considering these questions, one can arrive at the appropriate framework for the AI model in consideration. Nevertheless, without going into specifics some frameworks are more popular than others and some of the top ones (in no particular below) are listed below.
- Pytorch & Torch
- AWS Deep Learning AMI
- Google Cloud ML Engine
- Microsoft Cognitive Toolkit/CNTK