We have strong expertise in building machine-learning models which demonstrate business value. We start with an exploratory analysis to make sense of the data and pick essential features to predict a specific outcome (target variable within the dataset). Then, we choose the right metric based on the dataset and business outcome for model evaluation. Due to the bias-variance trade-off, it is crucial to ensure that the machine learning model is balanced in terms of the interchange to avoid over or underfitting and help the business predict the right outcome to demonstrate maximum business value. Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks. Using tabular data and images, we have demonstrated the ability to build deep learning models (using PyTorch, TensorFlow, and OpenCV). Moreover, we have strong expertise in building computer vision models using deep learning and transfer learning for problems like object detection, facial recognition, image classification, etc.
Natural language processing (NLP) is the ability of a computer program to understand human language as it is spoken and written -- referred to as natural language. We have expertise in building NLP models (using PyTorch and TensorFlow) to help businesses make sense of plain natural language. Moreover, we have built chatbots for firms to make their customer service better and more efficient. For chatbots, we have expertise in RASA, Google Dialogflow, Microsoft LUIS, etc. In addition, we can also analyze sentiment from raw text across different marketing channels to help businesses know what kind of content works and which products are being liked the most. MLOps is a set of practices that combines Machine Learning, DevOps, and Data Engineering, which aims to deploy and maintain ML systems in production reliably and efficiently. It applies to the entire Machine Learning Lifecycle, from data pipeline to model generation, orchestration, and deployment, to health, diagnostics, governance, and business metrics. Imagine an ML model deployed in production. MLOps takes care of retraining and deployment of the model with new incoming data, manages model versions and related artifacts, and monitors model health in production. It helps to increase automation and improve the quality of production ML. MLOps is a seamless integration of your development cycle and your operations that enables your data scientists to work through the lens of business interest and create better agile ML products.
Yes, machine learning can be self-taught. However, it is recommended to hire data scientists to train and manage ML models.
Machine learning is a branch of Artificial Intelligence. It automates systems by using data to discover trends and patterns without any human intervention. This automation can refine organization-wide processes and analyze data.