We solve the hard problem
Ganit Labs is a full-service data science and data engineering service company that utilizes machine learning (ML), deep learning (DL), statistics, neural networks to design custom solutions that provide efficient, rapid decision-making capabilities and predictive modeling solutions to clients.
These artificial intelligence (AI) techniques, along with other advanced statistical methods, utilize available data sets to train algorithms that provide complex human-like decision-making abilities. We partner with companies who are looking to innovate and adapt in a fast-paced and data-driven world, and make improvements to their businesses, consequently impacting every stakeholder.
Our AI solutions are fully customized, based on the specific needs of our partners. From conceptualization, design, development, testing to deployment in the production environment, we offer the entire suite of services that allow end-to-end lifecycle for seamless and successful AI implementation.
Experience & Expertise: Senior leadership with 20 years experience and PhDs in ML, 30 years in technology service, 10+ years in financial service on Wall Street, 12 years in ML consulting, 15 years in health science research, 12 years in mechanical engineering
Focus: Our only focus is data science and any cross-functional capabilities that aid in data science
Quality & Improvement: Betting long-term success with razor-focus on quality of work, continuous improvement and adoption of new techniques and cutting-edge methodologies
Domain Knowledge: Diversity and depth of knowledge is valued by our clients
Lean & Flexible: Nimble organizational structure allows adaptability to customer needs
We are only as good as our people
We have a strong management and diversified data team with expertise in data science, machine learning, data engineering, cloud technology, computer science and statistics. The depth of our domain knowledge is derived from team members with backgrounds in academia, mechanical engineering, health science research, business and finance.
Our team consists of industry veterans from IBM, Wall Street and PhDs in Machine Learning, and Computer Science from Carnegie Mellon University and Stanford University. We look forward to introducing ourselves to you.
What is Machine Learning?
Machine Learning (ML) is unique cross-functional area within Artificial Intelligence (AI) that utilizes principles from mathematics, calculus, statistics and computer science to develop algorithms that learn from data sets and experiences from real-world phenomenon and consequently, utilize that intelligence to make realistic predictions, provide prescriptions or descriptions about the phenomenon. The algorithms also adapt over time to new data and experiences to improve efficacy over time. Businesses are adopting ML in applications that include sales predictions, fault or fraud detections, machine failure predictions, image and speech recognitions, medical diagnosis, driverless cars, and so on.
ML models are typically of three types:
- Supervised Learning: Algorithm uses training data and feedback from humans to learn the relationship of given inputs to a given output (eg, how the inputs “time of year” and “interest rates” predict housing prices)
- Unsupervised Learning: Algorithm explores input data without being given an explicit output variable (eg, explores customer demographic data to identify patterns, detect anomalies in a streaming sensor data)
- Reinforced Learning: Algorithm learns to perform a task simply by trying to maximize rewards it receives for its actions (eg, maximizes points it receives for increasing returns of an investment portfolio)