Artificial Intelligence & Machine Learning

Artificial Intelligence & Machine Learning

Artificial Intelligence & Machine Learning

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

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

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.

Machine Learning Operations

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.

Can machine learning be self-taught?

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.

AI technologies have resulted in process automation and process discovery. In this digital age, making data-driven decision-making is essential for every enterprise for digital transformation. AI and machine learning can extract meaning from data when the answers are clearly defined. Moreover, they can also transform exponentially dynamic unregulated and regulated data into insights, behaviors, values, and profitability improvements. Similarly, advanced machine learning and big data can assist AI to take a focal point.  

How Does ML & AI Help Your Business?

Machine learning and AI can help your business decrease equipment breakdown through preventative maintenance. Furthermore, it can help you increase profits by analyzing business performance in detail. The predictive model can predict purchases, revenue and probability, and several variables for better decision-making. With our machine learning consulting services, you as an enterprise can leverage customer data to create valuable customer profiles and thus enhance brand loyalty.

Machine Learning Solution:

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 important features to predict a specific outcome (target outcome within the dataset. For model evaluation, we choose the right metric based on the dataset and business outcome. Due to the 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 undercutting and help the business in predicting the right outcome to demonstrate maximum business value.   Through Deep Learning, we have demonstrated the ability to build 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.    Our Machine Learning Roadmap:

You can gain a competitive edge over your rivals by adopting state-of-the-art data practices. Moreover, we can create end-to-end machine learning solutions for your specific business needs by using intricate statical methods and various ML models and algorithms such as Deep Learning.

We have expertise in Machine Learning, Computer Vision, Natural Language Processing (NLP), Deep Learning, and Chatbots. We adopt a holistic approach to developing robust AI & ML solutions that entail the following phases: 1. Learn: Develop an understanding of the business model and problems faced. 2. Educate: Explain AI technology and develop a common ground of possibilities for the business. 3. Ideate: Find solutions and prioritize them for the short-term and long-term. 4. Develop: Formulate an action plan to transform the business with AI. 5. Implement: Ensure the organization embraces the technology by providing a thorough change management plan.