Machine Learning
Your systems can learn & do wonders. Welcome, Machine Learning!
Growing volumes of data, on-demand computing power, affordable storage, and consumable AI APIs are leading to the massive adoption of ML services.
Machine learning derives insights, creates predictions from raw data to quickly solve complex, data-rich business problems. ML models learn from data iteratively and allow machines to find hidden patterns. With our experience across Data Management, DataOps, Cloud, and AI, we can help you build, train, deploy machine learning models to solve real-world business challenges. Our data scientists, BI engineers, and cloud experts create an integrated service portfolio for ML development, pipeline automation, and infra management.
Our Machine Learning Methodology
Our Machine Learning Offerings
Feature Engineering
Garbage in, garbage out! Data pre-processing and feature engineering are the main drivers of model performance in machine learning. We help you extract the right features from your raw data transforming them into suitable formats to train machine learning models. Our data scientists bring in domain expertise, isolate key information, and highlight patterns to improve predictive models.
Algorithm Design
The efficiency of a machine learning solution depends on the performance of algorithms. With a comprehensive view of various ML algorithms such as supervised, unsupervised, semi-supervised, and reinforcement learning, we help you choose the suitable algorithm considering key factors like the business case, data quality, training time, hyperparameters, and model complexity.
Model Training
Training enables the machine learning model to understand the dataset’s hidden patterns. We make the model learn enough about the training dataset’s structure to make predictions about unseen data. Our data scientists optimize algorithms, minimize loss function and safeguard against overfitting to deliver a consumable model that solves real-world problems using new data.
Model Evaluation
Evaluating guides the choice of learning model and determines how efficiently the model is making predictions. Our data scientists will evaluate your model performance with a real-world world dataset rather than a training set against the predefined metrics and KPI’s. We constantly improve the model accuracy by algorithm tuning, feature engineering, bagging and boosting methods.
MLaaS
Machine Learning as a Service (MLaaS) is a cloud-based low-code integrated development environment designed to accelerate model lifecycle without worrying about code, computing power, and hosting. We let you consume these purpose-built platforms such as AWS Sagemaker, Azure ML Studio, IBM Watson, and Google Cloud AI to jumpstart your cognitive journey effortlessly.
Applications of Machine Learning Across Industries
Finance
- Risk Analysis and Regulation
- Customer Segmentation
- Cross-selling and Up-selling
- Sales and Marketing Campaign management
- Credit-worthiness Evaluation
Retail
- Predictive Inventory Planning
- Recommendation Engines
- Up-sell and Cross-channel Marketing
- Market Segmentation and Targeting
- Customer ROI and Lifetime Value
Manufacturing
- Predictive Maintenance or Condition Monitoring
- Warranty Reserve Estimation
- Propensity to Buy
- Demand Forecasting
- Process Optimization
Logistics
- Capacity Utilization
- Route Optimization
- Demand Forecasting
- Fraudulent Invoice Detection
- Interactive Chatbot
Healthcare
- Proactive Health Management
- Healthcare Provider Sentiment Analysis
- Disease Identification and Risk Stratification
- Alerts and Diagnostics from Real-time Patient Data
E-Commerce
- Segmentation, Personalization & Targeting
- Dynamic Pricing
- Recommendation Engines
- Supply and Demand Prediction
- Sentiment Analysis
Digital Media
- Content Detection and Classification
- Object Recognition
- Recommendation Engine
- Social Analytics
- Demographic and Sentiment Analysis
Travel and Hospitality
- Demand and Price Forecasting
- Interactive Travel Assistants
- Social media Consumer Feedback and Sentiment Analysis
- Customer Segmentation
- Churn Analysis
insights
Here are our thoughts on the latest in technology, and some compelling stories of our shared success.
Case Study
Video Analytics-based In-store CX Analysis for an Offline Retailer
The solution detects the hand movements in the frame using OpenPose hand keypoint detectio...
- 80% accuracy in understanding customer behavior
- 45% improvement in Shelf Zone Analysis
- Total try-on count by Stock Keeping Unit (SKU)
- Try-on to purchase conversion by shelf position
Case Study
Prediction of Claims Denial for a Revenue Cycle Management Firm
Processed vast amounts of claims data through the intelligent ML algorithm by random fores...
- 20% reduction in claim denial
- $50K+ annual savings by avoiding rework on denied claims
- 78% accuracy level on denial prediction
- 200+ hours of effort reduced per month
Blog
Here’s how sentiment analysis can help you drive ROI.
With the recent advancements in deep learning, many algorithms have been developed that can analyze customer conversations effectively. Sentiment analysis is one such text classification tool that tells whether the sentiment behind a text is positive, negative, or neutral. Leveraging this tool, businesses can comprehend the key aspects of their products and services that customers actually care about.
Read More →
Blog
Decision Intelligence – Unlocking Success in 2021 and Beyond
In a business context, the unpredictability of the outcomes in current decision models in most cases is a result of the failure to capture the “uncertain” factors linked to these models. The introduction of machine learning algorithms into the decision-making processes can eliminate these challenges. Read on to learn more.
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