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Bacancy Technology utilizes the full potential of cutting-edge technologies, like ML and AI, while offering consulting and development services to clients. Our smart solutions and services enable businesses to gain an edge over their competitors in their respective industries.
We specialize at providing Natural Language Processing (NLP), predicting analytics, Business Intelligence (BI), Robotic Process Automation (RPA), Image Processing and Analysis, Machine learning, deep learning, data mining, marketing personalization, data sciences, e-commerce AI, business consulting and other advanced AI/ML services.
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AI/ML Services for your Business
AI and ML are especially useful for those businesses which want to accelerate their growth rate and become/stay industry leaders. At Bacancy Technology, we help you in doing so. Our advanced knowledge and understanding of business implementation have helped us in becoming the best AI ML Company. Check out a few cases where our services, passion and expertise could benefit you.
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|Programming Language||Python Java C++ Objective C|
|Framework and Libraries||Mobile Core ML Cloud Image Recognition FDK TensorFlow Light Caffe2
Web and Desktop TensorFLow Open CV Caffe Natural Language Tolkit
|Platforms||Android iOS Windows Mac OS Linux|
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
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.
6 Machine Learning Platforms by Microsoft
This article briefs the architecture of the machine learning platform to the specific functions and then brings the readers to think from the perspective of requirements and finds the right way to build a machine learning platform. Finally, it is recommended that Microsoft open source platforms are privately-configured machine learning training platforms.