Machine Learning without code
As the demand for machine learning and artificial intelligence goes up, leading tech giants realized the need to give developers access to tools to build and deploy models. From the industrial perspective, there aren’t enough skilled programmers and data scientists within the industry to develop these systems. Tech giants are now open sourcing their platforms and developer tools to lower the barrier for entry in AI/ML. Also the fields using ML and AI are expanding at vast rates, now Healthcare, Automobile, Agriculture almost all the areas are leveraging the use of machine learning and people in these fields are dependent on data scientists for converting their ideas to reality.
Engineers in areas other than computers face problem of not knowing How to code, so their is a need to know some tools to let people start in their projects. Writing code can unlock more and different tools and capabilities, but it is not required, and it does not need to come first.
You only need to know your data.
Here are few tools which doesn’t have requirement of knowledge to code.
Google’s solution for complex machine learning is Cloud AutoML, a point-and-click method for generating machine learning models without any coding background. Google offers pre-trained neural networks available through APIs that can accomplish certain tasks, but that’s only useful if you need specifically what that model does. Google does all the complicated operations behind the scenes, so the client doesn’t require to comprehend anything about the complexities of neural network design. AutoML uses a simple graphical interface, enabling the user to drag in a collection of images. Then, the platform needs to know how to represent those images. Google does its charm, and users end up with a model running in the cloud that can recognize the specified courses in photos.
This tool is capable of producing world-class imminent modelling capabilities with automated machine learning, transforming machine learning and AI projects in minutes or days instead of months without having to hire and instruct a data science team. The tool develops and deploys predictive patterns employing traditional methods without any former programming knowledge. It provides numerous cutting-edge open source machine learning prototypes to discover the most authentic model for user data.
This feature brings data science and predictive modelling within the reach of organisations and helps them achieve ML at scale.
Uber’s AI Lab continues with open-sourcing deep learning framework with there newest release which is called Ludwig, a toolbox build on top of TensorFlow that allows users to create and train models without writing code.
Finding the right model architecture and hyperparameters for your model is a difficult aspect of the deep learning pipeline. As a data scientist, you can spend hours experimenting with different hyperparameters and architectures to find the perfect fit for your specific problem. This procedure isn’t only time consuming, code-intensive but also requires knowledge of all the algorithms used and state-of-the-art techniques, which are used to squeeze out the last percent of performance.
WEKA is short for Waikato Environment for Knowledge Analysis. It is an open source Java software that has a collection of machine learning algorithms for data mining and data exploration tasks. It is a very powerful tool for understanding and visualizing machine learning algorithms on your local machine. It contains tools for data preparation, classification, regression, clustering, and visualization.