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Professional Summary
As an experienced machine learning engineer with over 4.4 years of experience, I have a strong background in time-series forecasting, computer vision(segmentation and object detection), and model quantization. Moreover, I have extensive experience designing MLOps frameworks for batch and streaming inferencing in production.
Experiences
Nunam, Bangalore
Senior Machine Learning Engineer [Aug, 2019 - Present]
Designed and developed forecasting and classification models in production to understand and predict safety and health-related events in EVs while also estimating their end-of-life trajectory.
- Designed and built MLOps framework for batch and streaming inferencing to serve ML models with various levels of latency using Kubeflow, KServe, AWS Sagemaker and MLflow.
- Also developed data drift and integrity checks in post-production for easier identification for model-refreshing.
- Designed and implemented feature store across whole data and analytics stack to reduce ambiguity in data metrics and increase model training frequency.
- Developed and deployed fault identification in Li-ion battery manufacturing using Detectron2 and Segment_Anything from X-ray images, where the primary focus was to identify regions of interest with 8 different types of defects.
- Developed both object detection and segmentation models for identifying regions of interest and their subsequent classification.
- Deployed the model in production with AWS Sagemaker, MLFlow and Kubeflow.
- Designed and built a transformer-based temporal fusion model to estimate the remaining range in electric vehicles in streaming inferencing which adds non-temporal data into time-series features.
- Built and deployed ML models for forecasting and classification tasks on neural accelerators(Syntiant NDP) and micro-controllers(Raspberry Pi Pico) using TFLite and TFlite-micro with training-aware quantization. Contributed towards a new benchmark proposal in MLPerf-tiny for estimating health parameters in EVs.
Google Summer of Code
Contributor, R Project for Statistical Computing [May, 22, - Sept, 22]
Developed and published an R package for converting between Gregorian and Vedic calendar systems, with a focus on time-sequence-based analysis and forecasting. Conducted extensive research on the computational methods used to convert between calendar systems and consulted with experts in the field to ensure accuracy and reliability.
| Project: summerofcode.withgoogle.com | R-CRAN: VedicDateTime | Paper: Springer MTAP |
Medical Image Processing and Signal Processing Lab, Indian Institute of Information Technology & Management – Kerala
Project Trainee- Research [Sep,2018 - Jan,2019]
Worked on medical image enhancement methods and adaptive image segmentation for automated segmentation of MRI of brain and spine tissues for labelling in assisted MRI.
Cleantech Solar Energy Corporation Pvt Ltd, Singapore
Intern- Data Analyst [Jan,2018 - July,2018]
Worked on smart grid assistance system for solar-powered rooftop system to forecast possible power generation and also determine overall cost based on geographical location under the guidance of Dr. Asharaf S at IIITM, Kerala. This project helped the corporation to save soft costs up to 15 USD per installation annually as well as providing an efficient method to determine the disruption of energy generation. This project was part of my final semester project work and the detailed study can be traced back here: [Paper] [GitHub]
Medical Image Processing and Signal Processing Lab, Indian Institute of Information Technology & Management – Kerala
Intern- Research [May,2017 - Oct,2017]
At the Medical Image Computing and Signal Processing Lab, IIITM-K, under the guidance of Dr Joseph S Paul, I conducted extensive analysis of regularization parameters to determine the optimal parameter selection method for k-space-based image reconstruction methods (GRAPPA & SPIRiT) in parallel-MRI. This involved evaluating the impact of various regularization parameters on noise levels and developing algorithms that deliver high-quality images with minimal noise. This project was part of my minor thesis project work and the detailed study can be traced back here : [Paper] [GitHub]
Education
Indian Institute of Information Technology & Management- Kerala , Thiruvananthapuram
M. Sc., Computer Science with Specialization in Machine Intelligence [July,2016 – July,2018]
Technical Skills
- Programming Knowledge: Python, R Programming, SQL
- Deep Learning Frameworks: TensorFlow, Keras, Pytorch, Optuna, TensorflowLite, TensorflowLite-micro, Prophet, FastAI
- Database: PostgreSQL, MySQL, TimescaleDB, InfluxDB
- MLOps Tool-chain: Kubeflow, Kserve, MLflow, AWS Sagemaker, Seldon-core, Apache Airflow, Apache Kafka, FastAPI
- Cloud Platform: AWS, Google Cloud
Talks
“Managing data quality in Machine Learning” at Google Developer Group Cloud Community Day
Bangalore, 2022 [Presentation] [Poster]
In the current scenario where every ML system requires a ton of data to train, changes in the data during model refreshment or even during production will cause a performance drop, sometimes quite significantly. It has become a tremendously important task in the ML system lifecycle to periodically check quality issues in the data stream itself. There are existing libraries, open-source tools or full-fledged SaaS platforms to monitor those data quality metrics but the metric used oftentimes becomes too generic and might not be useful at all. There are simple data quality metrics, which can be developed individually and can be integrated with data quality tools/SaaS platforms to monitor them in production. In this talk, I will go through a couple of metrics for different types of data and use cases and how to use clustering and other unsupervised learning algorithms to build those metrics at the end will also try to show a demo with integrations and how it can be run in production.
“Things I learned while running neural networks on microcontroller” at PyData Global
2022 [Presentation] [Poster]
Running neural networks on production systems is quite difficult but running it on microcontrollers is different. The choice of the microcontroller, presence of a purpose-built processor, data I/O, model training and inferencing - all things change when the target deployment scenario changes from a cloud instance to a power-constraints microcontroller. In this talk, I will go through how to go about it as a novice and get a model running.
“Bessel’s Correction: Effects of (n-1) as the denominator in Standard deviation” at PyData Global
2022 [Presentation] [Poster]
When calculating standard deviation, the denominator being (n-1) for observation of n doesn’t make sense, until we dive a little deeper to understand the theory behind it. But even with the correction factor in place, the question can be asked is it really needed? How much does it influence the end result?
“Interpretable ML in production” at Google Developer Group Cloud Community Day
Bangalore, 2023 [Presentation] [Poster]
Validating an ML model with train-test accuracy metrics offers an initial understanding of viability but generating consistent inferencing with contextual business goals requires understanding how the deployed model works in different nature and how they will behave in case of soft data drift. In this talk, I will try to go through different explainability methods and how to employ them and how the choice of type of models affects or affects the interpretability in production inferencing.
“Considerations for LLMOps: Running LLMs in production” at AZConf
Chennai, 2023 [Presentation] [Poster]
With the recent explosion in development and interest in large language, vision and speech models, it has become apparent that running large models in production will be a key driver in enterprise adoption of ML. Traditional MLOps, i.e. running machine learning models in production, already has so many variabilities to address starting from data integrity, data drift and model optimization. Running a large model (language or vision) in production keeping in mind business requirements is different altogether. In this talk, I will try to explain the general framework for LLMOps and certain considerations while designing a system for inferencing a large model. A brief understanding of the current open-source tool sets will also be mentioned so that tool-chain selection is a bit easier.
“How can a learnt model unlearn something” at PyData Global
2023 [Presentation] [Poster]
In the recent past with the explosion of large language or vision models, it became inherently very costly to train models on new data. Coupled with that the various new data privacy legislations introduced or to be introduced make the “right to be forgotten” very costly and time-consuming. In this talk, we will go through the current state of research on “machine unlearning”, how a learnt model forgets something without retraining and a general demonstration of the machine unlearning framework.
Publication
Bokde, N.D., Patil, P.K., Sengupta, S. et al. VedicDateTime: An R package to implement Vedic calendar system. Multimed Tools Appl (2023). https://doi.org/10.1007/s11042-023-16553-w