Yes, It’s completely free. We allow this free time period to evaluate the code quality, communication, on-time delivery, Agile software development process, and more of the hired trial resource. If you get satisfactory work, then you can continue with the engagement, and if not, then you can right away cancel it.

AI and ML are not only limited to prediction and classification. Following are a few other use cases where it can be used and implemented:
Image Processing (Correct image quality, Image Analysis, Image Synthesizing, Image Captioning)
Text Generation (For Q&A, Chatbot Response, Text Summarization)
Video Processing (Identifying actions and humans present in the video, Video summarization)

The success of the AI and ML models is based on data. For Deep Learning operations, approx 1500 units of the data point, including images, documents, and reviews are required for specific AI-based training/ testing and validations.

Any data which can be converted as well as represented into numerical representation can be worked used for AI and ML. Here is the list of such types of data:
Tabular data
Text
Image
Video
Graphs

Unavailability of a large number of training samples.
Labeling of Data – As deep learning and conventional machine learning algorithms are supervised, i.e., they need data and their label to capture the semantics of work to be done. It is a manual process and eats up more time than actually building models. It also adds biases in data as humans are prone to error when it comes to accurately annotating data, e.g. Annotating car parts for detecting damage. Model build with such data generally doesn’t converse with reasonable accuracy.
Adopting Generality – Ml/DL algorithms are not able to produce the same results when deployed to different scenarios than the scenario used while training. So, to make it work in a different situation, a retraining model is required.
Unable to explain what is going on inside the model and hence challenging to debug. However, various analytical tools can help with this.

A RestAPI based URL can be generated and integrated into the application, which will call underneath ML implementation by passing the required parameters.

AI and ML generated the likelihood of something as output instead of exact output. For, I.e. It is difficult to tell which team will win the EPL league. However, based on past data and other contextual data, the model can predict that so and so the team will have a 95% chance of winning.

AI and ML generated the likelihood of something as output instead of exact output. For, I.e. It is difficult to tell which team will win the EPL league. However, based on past data and other contextual data, the model can predict that so and so the team will have a 95% chance of winning.

For sure, AI and ML developers you hire from us will dedicatedly work on your project as your employees only. You will have total access and complete control over them through your convenient communication platform.