Capabilities
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Machine Learning & Pattern Recognition
Building a solution involving machine learning is much more than the model. It is a complex mix of data structures, model training, model integration and architecture. We engage in end-to-end delivery of a machine learning solution tailored to bring product features to life.
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Natural Language Processing
There are many NLP APIs and services available today. Some of these services could give 80% accuracy on extraction tasks involving generic data. However, to solve really hard problems involving natural language understanding, especially with proprietary and small data sets, we need to skillfully use machine learning techniques along with traditional NLP algorithms.
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Computer Vision & Image Processing
Deep learning techniques have given a fillip to computer vision and image processing solutions. However, training models for proprietary and domain-specific data sets is a challenge. We find innovative ways to transform the domain-specific part of a problem into a generic computational problem in order to deliver practical solutions.
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Mathematical Optimization
Optimization algorithms are the foundation of modern-day machine learning. However, there is a rich history dating back to many decades. We strive to use these fundamental algorithms to deliver solutions to problems involving allocation, balancing, routing.
What We Have Done
Insights
Solving a Network of Sensors Problem using Gradient Descent
In this research report, we highlight a problem formulation involving multiple sensors that collectively determine “characteristics” of targets in an environment. We show how the formulation can be solved with Lagrangian relaxation.
Handling Categorical Features in Machine Learning
You can’t fit categorical variables into a regression equation in their raw form in most of the ML Libraries. If it is not included in the modeling, then you do not get an accurate model. It’s crucial to learn the methods of dealing with such variables.
Meet the
Expert
Dr.Ravindra Guntur
Data Scientist- Applied mathematical optimization
- Natural Language Processing
- Machine Learning & Pattern Recognition
- Recognition algorithms for Video
AI FAQs
What is AI?
Artificial intelligence (AI) is the science of building smart machines capable of solving complex tasks. AI’s major thrust lies in the development of computer functions linked with human intelligence like reasoning, learning, and problem solving.
How is ML different from AI?
AI refers to a system that solves tasks that complex decision making. It basically mimics the human intelligence.
On the other hand, machine learning is a subset of AI and refers to an AI system that can self-learn using an algorithm and lots of data to make accurate predictions.
What are the use case of AI?
- AI in healthcare:
- It can help doctors by precise and quick diagnosis of diseases using patient samples, medical history etc.
- AI can help vaccine R&D teams in quickly rolling out new effective vaccines.
- AI in banking and finance:
- It can analyse large volumes of data, detect fraud, and can perform predictive tasks too.
- AI-powered apps can also offer financial advices and guidance based on a customer’s spending pattern.
- AI in insurance:
- AI can help both insurers and insured by predicting the most appropriate premiums based on risk factors and history of insured.
- AI can help by detecting fraud insurance claims or adherence issue.
Read our blog that extensively talks about use cases of AI industry wise
What are the top AI technologies in demand?
- ML: Machine Learning focuses on the use of data and algorithms to mimic the way humans learn, thus improving the accuracy with time.
- NLP: It stands for natural language processing, known for the combining computational linguistics, rule-based modelling of human language with machine learning, statistical, and deep learning models.
- Deep learning: It is a subset of machine learning where neural networks, algorithms based on the human brain learn from huge amount of data. A deep learning algorithm can perform a task several times each time modifying a little for better outcomes.
- Computer vision: A field of computer science that focuses on developing digital systems that can be used to process, analyse, and make sense of visual data like humans do. Machines retrieve the visual information, handles it, and then interprets the results via special software algorithms.
Check out our article that explains the AI technologies in detail.
What are the top tools and platforms used in AI development projects?
Scikit Learn, TensorFlow, Theano, Caffe, MxNet, Keras, PyTorch, CNTK, Auto ML, OpenNN, H20: Open Source AI Platform, Google ML Kit
What an AI development team looks like?
An AI development team comprises of domain experts, data scientists, data engineers, product designers, data modelling experts, AI/ML solution architect and software engineers.
What are the steps involved in an Artificial Intelligence development project?
Before you start AI development project, check out the prerequisites given below:
- Do you have the labelled data?
- Do you a strong data pipeline to assist model training?
- Have you selected the right model?
Now, let’s focus on the steps involved in an AI development project:
- Data acquisition: It involves data collection, data pipeline creation, data validation and data exploration.
- Model development: It involves feature engineering, training and evaluation.
- Deployment: It involves integration, testing and validation.
- Monitoring: Keep a watch on how AI models perform in production.
If you are interested in knowing in detail about the prerequisites and AI implementation, read our blog on all you need to know about AI implementation
How long does it take for an Artificial Intelligence Development project to go live?
For an AI project to go live, it can take from few months to a year, totally depending on the scope and complexity of the AI project. It is advised not to underestimate the time it takes to prepare the data before a data science engineer builds an AI algorithm.
What are the common mistakes to avoid while developing AI solutions?
- Unclear goals and KPIs
- Failing to adopt AI early leading to tech issues during implementation.
- Developing isolated POCs that fails to work in production environment.
- Insufficient data to build data pipelines
- Insufficient skills and experience.