Artificial Intelligence and Machine Learning Development Services

Frequently Asked Questions

• Apart from Prediction and Classification, for What Other Projects I Can Use AI/ML?

  AI/ML Solution is not only meant for prediction/classification. Following are a few other use cases:

  • 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)
• How Much Data is Required to Build AI and ML Based Solution?

  The success of AI and ML model is based on data. For Deep Learning operations, approx 1500 units of the data point, including image, documents, review is required for any AI-based training/Testing/Validations.

• On Which Type of Data, AI/ML Can Be Applied?

  Any data which can be converted/represented into numerical representation can be worked upon in AI/ML.

  Here is the list of such types of data:

  • Tabular data
  • Text
  • Image
  • Video
  • Graphs
• What Are the Limitations of AI/ML?
  • 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 much time than actually building models. It also adds biases in data as humans are prone to error when it comes to accurately annotate 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 same results when deployed to different scenarios than the scenario used while training. So, to make it work in different situation, 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.
• How Can I Integrate My Application with AI/ML?

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

• What are the Costing Factors for AI/ML Solutions?

  Implementing AI/ML Solutions comprises two phases:

  phase-1: Building model, Training model, Testing model and Hyper tuning of the model phase-2:Deployment model in a production environment.

  For Phase-1, we need to set cloud base cluster of servers with GPU/TPU to enable machine (Although you can set one machine). It’ll take time for training in case of image, text-based data processing with one machine. To reduce training time, it's advisable to use cluster of servers for distributed service.

  following factors need to calculate in training phase

  • Costing for No. of machine for Training Phase, hourly based
  • Network I/O
  • Costing for No. of machine for Prediction Phase
• What Quality of Output In-terms of Accuracy can be Achieved by implementing AI/ML Solutions?

  AI/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 tournament. However, based on past data and other contextual data, model can predict that so and so team will have 95% chance of winning.