Artificial Intelligence and Machine Learning Solutions
We provide artificial intelligence solutions to help organization automate processes and leverage from machine learning tools.
We use Artificial Intelligence algorithms to help machines learn themselves from the data they are exposed to and arrive at conclusions, simulating human intelligence. Our data scientist have profound knowledge and experience in designing, implementing and integrating artificial intelligence applications specific to customers business.
Some of the expertise that we have include:
- Chat Bots
- Machine learning solutions
- Deep learning & neural networks
- Natural language processing
- Predictive modeling
- Reinforcement learning
- Anomaly detection
Most of the organizations don’t have their data science team, which is true for not just startups but also for large organizations. Our motive is to fill this gap by providing technical expertise and know-how to your project from the day one. We provide a team of best data scientist who would help you overcome challenges faced by you in data planning.
The lifecycle of a data science project is different from software development project. Unlike a software development project, there is no single universal workflow process for all data science projects. The data scientists working on Artificial Intelligence software have to determine which workflow best fits the business requirements. The lifecycle of a data science project at Prolitus involves jumping back and forth among various interdependent data science tasks and steps using a variety of data science programming tools. Prolitus data science process begins with asking business questions that guide the overall workflow of the data science project.
- Data Acquisition - In the first stage, we identify the person who knows what data to acquire and at what time, based on the question to be answered.
- Data Preparation – Prolitus data scientists understand the importance of data preparation process, (also known as data cleaning process) and closely identify various data quality issues.
- Hypothesis and Modelling – To identify which machine learning model fit the best with the business needs, different machine learning techniques are applied to the data.
- Evaluation and Interpretation – In this stage, we measure machine learning model performances, compare using validations and then test sets to identify the best model based on model accuracy.
- Deployment - Machine learning models might need to be re-coded before deployment if the programming language of the production environment (such as Java) is different from the test environment (such as Python). Then, the machine learning models are first deployed in a test environment before actually deploying them into production.
- Operations – Prolitus data scientists accumulate their learnings from different data science projects to speed up similar data science projects in the future.
- Optimization - In the final phase, we retrain the machine learning model in production, whenever there are new data sources coming in and take necessary steps to keep up with the performance of the machine learning model.
Prolitus has always been at the forefront of investing in latest technologies, and Artificial Intelligence is no exception. We have a pool of in-house coders and data scientists who are committed to developing Artificial Intelligence tools such as Chat-Bots. We are developing a solution for Healthcare Industry, that would give suggestions about possible diseases, diagnosis, and which specialist to consult, based on the symptoms of the patients.