About

Upendra Singh is a highly experienced Principal Machine Learning Engineer, Data Scientist, and Big Data Engineer with over 14 years of hands-on experience. He has a strong track record of developing models and techniques that help achieve business objectives, with metrics to track progress. He has expertise in analyzing ML algorithms that can be used to solve a given problem and ranking them by their success probability. Upendra is skilled in exploring and visualizing data to gain an understanding of it, then identifying differences in data distribution that could affect performance when deploying the model in the real world. He has experience in defining data/model validation strategies, defining the pre-processing or feature engineering to be done on a given dataset(s), and performing required data engineering to achieve the same or work closely with data engineering teams to get it done. Upendra is proficient in training models and tuning their hyper-parameters, analyzing the errors of the model, and designing strategies to overcome them. He has experience in deploying models to production and integrating implemented ML Systems with existing application(s) or platforms. He is skilled in designing Machine Learning Systems to support Batch and Real-Time data processing platforms, researching and implementing appropriate ML algorithms and tools, and extending existing ML Libraries and Frameworks. Upendra performs relevant data engineering (big data if need be!) for collecting, ingesting, and transforming data for ML Experiments and analysis. Upendra has worked on various problem spaces, including Web Mining, Knowledge Graph (Creation and Augmentation), Probabilistic Deduplication, Unified Customer Profile, Time Series Forecasting, Anomaly Detection and RCA (Explainability) for IoT, Sentiment Analysis, Analyzing Marketing Campaigns, Analyze Social Media, Market basket Analysis, Customer Lifetime Value, Churn Analysis, Customer Segmentation, and AB Testing. He has used various ML/DL Techniques, including Machine Learning (Classification, Regression), Clustering, Network Analysis, Deep Learning (Tensorflow, Pytorch using Keras API), NLP using Deep Learning, Named Entity Recognition, Relationship Extraction, and Bayesian Analysis. Upendra has designed solutions deployed in production using MLOps Frameworks: MLFlow, Seldon, Data Engineering and Orchestration Frameworks: Apache Spark (Batch and Streaming), Apache Flink, Kafka, Airflow, FastAPI, Flask, and programming languages and techniques used: Scala, Python, R, and C++. He has experience in Cloud Provider Experience: AWS, GCP. Upendra has worked with various companies, including