We engineer practical data-driven algorithms to implement machine learning solutions for startups by separating the AI hype from computational realities.
AI Development Services
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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.
Our AI development Capabilities
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.
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.
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.
Our AI development Capabilities
Computational
Histopathology
Classifying cell structures and recognizing similar regions in tissue samples.
Anomaly Detection and Breakdown Prediction
Modeling machine breakdown using supervised learningover high dimensional time-series data.
Targeted Extraction and AutomatedUnderstanding of Text
Identifying and extracting key concepts, questions in chatconversations/ reviews and recognizing values fordomain attributes.
Computational
Histopathology
Classifying cell structures and recognizing similar regions in tissue samples.
Anomaly Detection and Breakdown Prediction
Modeling machine breakdown using supervised learningover high dimensional time-series data.
Targeted Extraction and AutomatedUnderstanding of Text
Identifying and extracting key concepts, questions in chatconversations/ reviews and recognizing values fordomain attributes.
Computational Histopathology
Classifying cell structures and recognizing similar regions in tissue samples.
Higher prediction accuracy was achieved using an ensemble consisting of atrained deep neural network, a pre-existing application-specific predictor, and asupport vector machine classifier. While the DNN acted as a new complementarypredictor, the SVM served to determine which of the two predictorsprobabilistically gave the correct result at that moment. This is achieved bycreating a probability surface that exhibits a bi-model distribution peaking ateach of the predictors. Such techniques can be used in applications such asindoor locationing, object location predictions in images, voting based systems
Insights From Our AI Experts
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2024-02-21
An MLOps Mindset: Always Production-Ready
A lack of an ML production mindset from the beginning of a project can lead to surprises later on, especially during production time, resulting in re-modeling and delayed time-to-market.
By Abhishek Gupta, Principal Data Scientist at Talentica Software on July 27, 2023 in MLOpsmore >
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2024-02-21
An MLOps Mindset: Always Production-Ready
A lack of an ML production mindset from the beginning of a project can lead to surprises later on, especially during production time, resulting in re-modeling and delayed time-to-market.
By Abhishek Gupta, Principal Data Scientist at Talentica Software on July 27, 2023 in MLOpsmore >
-
2024-02-21
An MLOps Mindset: Always Production-Ready
A lack of an ML production mindset from the beginning of a project can lead to surprises later on, especially during production time, resulting in re-modeling and delayed time-to-market.
By Abhishek Gupta, Principal Data Scientist at Talentica Software on July 27, 2023 in MLOpsmore >
-
2024-02-21
An MLOps Mindset: Always Production-Ready
A lack of an ML production mindset from the beginning of a project can lead to surprises later on, especially during production time, resulting in re-modeling and delayed time-to-market.
By Abhishek Gupta, Principal Data Scientist at Talentica Software on July 27, 2023 in MLOpsmore >
-
2024-02-21
An MLOps Mindset: Always Production-Ready
A lack of an ML production mindset from the beginning of a project can lead to surprises later on, especially during production time, resulting in re-modeling and delayed time-to-market.
By Abhishek Gupta, Principal Data Scientist at Talentica Software on July 27, 2023 in MLOpsmore >
-
2024-02-21
An MLOps Mindset: Always Production-Ready
A lack of an ML production mindset from the beginning of a project can lead to surprises later on, especially during production time, resulting in re-modeling and delayed time-to-market.
By Abhishek Gupta, Principal Data Scientist at Talentica Software on July 27, 2023 in MLOpsmore >
Meet the Expert
● Applied mathematical optimization
● Natural Language Processing
● Machine Learning & Pattern Recognition
● Recognition algorithms for Video
AI Development FAQs
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.
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.
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.
1. Ml： Machine learning focuses on the use of data and algorithms to mimic the way humans learn, thus improving the accuracywith time.
2. NLP: it stands for naturalanguage processing,known for the combining computational linguistics, rule-based modeling ofhuman language with machine learning, statistical, and deep learning models.
3. Deep learning: t is a subset of machine learning where neural networks, algorithms based on the human brain learn from hugeamount of data. A deep learina dlaorithm can perorm a task several times each time modifving a ittle for better outcomes
4. computer vision: A feld of computer sclence that focuses on developing didital systems that can be used to process, analyseand make sense of visual data like humans do. Machines retrieve the visual information, handles it, and then interprets the
results via special software algorithms.
Scikit Learn, TensorFlow, Theano, Caffe, MxNet, Keras, PyTorch, CNTK, Auto ML, OpenNN, H20: Open Source AI Platform, Google ML Kit
An AI development team comprises of domain experts, data scientists, data engineers, product designers, data modelling experts, AI/ML solution architect and software engineers.
Awards & Recognition
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