We have strong expertise in building machine learning models which demonstrate business value. We start with exploratory analysis to make sense of the data and pick important features to predict a specific outcome (target variable within the dataset). For model evaluation, we choose the right metric based on the dataset and business outcome. Due to bias-variance trade-off, it is very important to ensure that the machine learning model is balanced in terms of the trade-off to avoid over or underfitting and help the business in predicting 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. We have demonstrated the ability of building deep learning models (using pytorch, tensorflow and opencv) using tabular data and images. 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 and so on.
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) in order to help businesses make sense of raw natural language. Moreover, we have built chatbots for businesses to make their customer service better and efficient. For chatbots, we have expertise in RASA, Google Dialogflow, Microsoft LUIS etc. In addition to this, we can also analyze sentiment from raw text across different marketing channels to help businesses in knowing 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, starting 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.