Key Responsibilities: Training and Deploying ML Models, designing scalable streaming Machine Learning systems capable of handling large-scale data. Data Extraction and Analysis: (Python, Spark, Pandas, Numpy)Extract valuable insights from diverse data sources. Using machine learning tools to identify key patterns, trends, and features that can boost model performance significantly. Identifying differences in data distribution that could affect model performance in real-world situationsModel Training: (Sklearn, SparkML, Torch)Design & develop machine learning models. From feature extraction to model selection and optimization, train accurate and robust models that meet performance objectives.Data Visualization and Insights: (Plotly, Matplotlib, Seaborn, Pandas, Sklearn)Combining complex data sets, and visualizing them to enable deeper insights and to extract more meaningful features from the data to improve model performance. Stakeholder Collaboration: Work closely with stakeholders to quickly gain a deep understanding of their systems, domain, and data sources, ensuring effective alignment and seamless integration.Scalable Deployments: (Spark Streaming, Spark, Java, Python)Design & develop Scalable Low-latency machine learning systems. By leveraging my knowledge of distributed computing, and Spark Streaming, to ensure the seamless deployment of trained models in real-world environments.Standards and Best Practices: (BigQuery, Spark, Airflow, Dataproc)Encourage team members to follow best practices and processes throughout various life cycles of Data Science and MLOps.

Key Responsibilities include designing and developing Data Processing Pipelines, Microservices, Machine Learning/ Deep Learning, NLP etc. 1. Data Processing Pipelines (Java 13, RabbitMQ, MongoDB etc) Create data processing pipelines that can scale and process up to Terabytes of data through various ETL lifecycles. 2. Microservices (Java13, Spring Boot, NoSQL databases, REST)Design and develop microservices that can generate valuable analytics. Create a perfect balance between Database and In-Memory processing to process and aggregate data in an optimised manner. Create a REST framework that can request and consume this data through the REST framework. 3. Machine Learning (Python3, Tensorflow, Sklearn, Flask etc):Design, deploy & train Machine Learning/Deep Learning prototypes. Once these prototypes are ready to convert them into deployable microservices using Flask/Django 4. NLP Use state-of-the-art NLP tools and models such as Spacy, BERT etc to generate insights out of the raw texts. Create custom text processing algorithms if required.

Key Responsibilities include designing and developing Data Processing Pipelines, Microservices, Machine Learning/ Deep Learning, NLP etc. 1. Data Processing Pipelines (Java 13, RabbitMQ, MongoDB etc) Create data processing pipelines that can scale and process up to Terabytes of data through various ETL lifecycles. 2. Microservices (Java13, Spring Boot, NoSQL databases, REST)Design and develop microservices that can generate valuable analytics. Create a perfect balance between Database and In-Memory processing to process and aggregate data in an optimised manner. Create a REST framework that can request and consume this data through the REST framework. 3. Machine Learning (Python3, Tensorflow, Sklearn, Flask etc):Design, deploy & train Machine Learning/Deep Learning prototypes. Once these prototypes are ready to convert them into deployable microservices using Flask/Django 4. NLP Use state-of-the-art NLP tools and models such as Spacy, BERT etc to generate insights out of the raw texts. Create custom text processing algorithms if required.

Key Responsibilities: Develop a machine learning library for Apache Apex Understand the working of DataTorrent's Apache Apex (unified stream and batch processing engine). Develop an understanding of how Apache Hadoop and other distributed systems work. Case study on a variety of Machine Learning libraries such as Apache Mahout, H2O, Spark MLlib to understand how these libraries internally work to process data. Create an Adapter Layer between Apache Spark and Apache Apex which will enable execution of Spark ML algorithms on Apex's stream processing engine.During the internship, I gained professional training in how to use Apache Hadoop and its internal workings. Learned how to contribute to an Apache Open Source project. Developed a recommendation system for Wikipedia pages, which was a batch implementation on the streaming platformCompared accuracy and performance of five ML algorithms on Apache Apex with that on Spark.