Machine Learning

Build Intelligent Systems That Learn From Data

Build machine learning systems that identify patterns, predict outcomes, classify information and support better decisions.

Web30 India develops machine learning solutions around data preparation, feature engineering, model development, training, evaluation, deployment and continuous monitoring across structured and unstructured data.

Build With Machine Learning

Built on LLM

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MACHINE LEARNING TECHNOLOGY ECOSYSTEM

Explore the Machine Learning Technology Ecosystem

Machine learning covers multiple learning approaches and model families. Common applications include supervised learning for classification and regression, unsupervised learning for discovering patterns and groups, and neural-network-based approaches for more complex data and tasks.

More Than a Trained Model

Build the Machine Learning System Around Your Data

A machine learning model is only one part of a production system. Reliable results depend on the quality of the data, feature engineering, training process, evaluation methodology, deployment environment and ongoing monitoring.

Machine learning can support classification, regression, clustering, anomaly detection, recommendation, forecasting and other data-driven tasks. Model selection and evaluation should be based on the problem, available data and the business objective rather than simply choosing the most complex algorithm.

Web30 India builds machine learning systems from data preparation through production deployment, with the architecture designed around the way the model will actually be used.

Finance Technology Ecosystem Illustration

Machine Learning Development Capabilities

Engineer Models Around Real Business Problems

Supervised Learning

Develop classification and regression models using labeled datasets to predict categories, values and outcomes.

Unsupervised Learning

Identify patterns, clusters, relationships and anomalies in datasets where predefined target labels are not available.

Predictive Modeling

Build models that use historical and current data to estimate future outcomes, risks, demand or operational conditions.

Feature Engineering

Transform raw data into meaningful model features through selection, transformation, encoding, aggregation and other preprocessing techniques.

Model Development & Training

Develop and train machine learning models using appropriate algorithms, datasets, validation strategies and experiment tracking.

Model Optimization

Tune model parameters, compare algorithms and optimize performance against defined evaluation criteria. Scikit-learn, for example, provides model-selection and hyperparameter-tuning capabilities across multiple estimator families.

Machine Learning Deployment

Deploy trained models through APIs, applications, batch pipelines or integrated production systems.

MLOps & Model Monitoring

Build the operational layer required to register, deploy, monitor and retrain models as production data and model behavior change.

Machine Learning Solutions

Turn Business Data Into Working Intelligence

Predictive Analytics Platforms

Build applications that use historical and current data to generate predictions for business and operational decisions.

Recommendation Systems

Create personalized recommendation engines for products, content, services, learning and other digital experiences

Fraud & Risk Detection

Develop models that identify unusual patterns, suspicious activity and potential risk across transactional and operational data.

Demand Forecasting

Build forecasting systems for sales, inventory, supply chains, workforce planning and other demand-driven operations

Customer Intelligence

Use machine learning to understand customer behavior, segmentation, churn risk, engagement and conversion patterns.

Anomaly Detection

Identify unusual behavior in transactions, machines, systems, networks, processes or operational data.

Intelligent Decision Systems

Combine machine learning predictions with business rules, workflows and application interfaces to support operational decisions.

Enterprise Machine Learning Platforms

Build reusable ML infrastructure for data pipelines, model training, deployment, monitoring and multiple business applications.

ML Layer

Data Sources Layer

Data Engineering Layer

Feature Layer

Model Development Layer

Evaluation Layer

Deployment Layer

Monitoring Layer

Data Sources Layer

Business Applications
Databases
Transaction Systems
IoT Devices
Customer Data
External Data

Data Engineering Layer

Data Ingestion
Data Cleaning
Data Transformation
Data Validation
Data Pipelines
Data Storage

Feature Layer

Feature Engineering
Feature Selection
Feature Transformation
Feature Stores
Feature Validation

Model Development Layer

Algorithm Selection
Model Training
Experiment Tracking
Hyperparameter Tuning
Model Comparison

Evaluation Layer

Validation Data
Cross-Validation
Performance Metrics
Error Analysis
Bias & Robustness Testing

Model evaluation can use different metrics and validation strategies depending on whether the task involves classification, regression or another learning problem.

Deployment Layer

Model Registry
Model APIs
Batch Inference
Real-Time Inference
Application Integration

Monitoring Layer

Prediction Monitoring
Data Drift
Model Performance
Infrastructure Monitoring
Retraining Workflows

Retrieval-Augmented Generation

Ground Your LLM With the Right Information

LLMs have broad pretrained knowledge, but enterprise applications often need access to private, changing or domain-specific information. RAG retrieves relevant information from an external knowledge source and provides it to the model as context.

Data Ingestion

Connect documents, databases, knowledge bases and approved information sources.

Content Processing

Clean, normalize, classify and divide source information into retrieval-ready content.

Embedding Generation

Create numerical representations that allow content to be searched based on semantic similarity.

Retrieval

Use vector, keyword or hybrid search to identify relevant information for each request.

Context Assembly

Combine retrieved information with system instructions, conversation history and application rules.

LLM Generation

Send the structured context to the selected language model to generate the response.

Response Evaluation

Measure groundedness, relevance, completeness and correctness to improve the retrieval and generation pipeline.

MLOps

Move Models From Experiments Into Production

A machine learning project does not end when a model achieves a good evaluation score. Production systems need repeatable training, model versioning, deployment controls and monitoring. A modern ML lifecycle commonly includes scoping, data preparation, training, evaluation, registration, deployment, monitoring and retraining.

Data & Experiment Management

Track datasets, features, experiments and model configurations across development cycles.

Model Registry

Maintain controlled versions of trained models and their associated metadata.

Deployment Pipelines

Automate model packaging, testing, staging and production deployment.

Inference Infrastructure

Serve predictions through APIs, batch processing or embedded application workflows.

Model Monitoring

Monitor prediction behavior, input data and production performance.

Drift Detection

Identify changes in production data or model behavior that may require investigation or retraining.

Retraining Workflows

Create controlled processes for updating models as new data becomes available.

Machine Learning Security

Protect the Data, Models and Decisions

Machine learning systems can handle sensitive business and customer information, making controls around data, models, access and operational behavior an important part of production architecture.

01 // DATA CONTROLS

Data Access Controls

Control who can access datasets, features, training environments and prediction outputs.

02 // PROTECTION

Data Protection

Protect sensitive information throughout ingestion, storage, training and inference.

03 // SECURITY

Model Security

Control access to trained models, model artifacts, endpoints and deployment infrastructure.

04 // INTEGRITY

Feature & Pipeline Integrity

Validate data transformations and feature pipelines so unexpected changes do not silently affect model behavior.

05 // CONTROLS

Prediction Controls

Apply business rules and validation around model outputs before they influence downstream workflows

06 // AUDITABILITY

Monitoring & Auditability

Track model versions, predictions, deployments, data changes and relevant operational events.

Machine Learning Use Cases

Where Machine Learning Fits Into the Business

Financial Risk & Fraud

Identify suspicious transactions, assess risk and support financial decision workflows.

Retail & Commerce

Predict demand, personalize experiences, recommend products and identify customer behavior patterns.

Manufacturing

Predict equipment failures, identify production anomalies and support quality inspection.

Healthcare

Support risk prediction, patient analytics, operational forecasting and other controlled healthcare applications.

Logistics & Supply Chain

Forecast demand, optimize operations, estimate delivery conditions and identify supply chain anomalies.

Banking & Financial Services

Build credit risk models, fraud detection systems, customer intelligence and forecasting applications.

Media & Entertainment

Personalize content discovery, understand audiences and predict engagement patterns.

Travel & Hospitality

Support demand forecasting, customer segmentation, recommendations and operational intelligence.

Machine Learning Integrations

Connect Models With the Data and Systems They Need

Enterprise Data Platforms

Connect data warehouses, databases, data lakes and operational data systems.

Business Applications

Integrate CRM, ERP, HRMS, finance, commerce and operational platforms.

IoT & Connected Devices

Process sensor and telemetry data for monitoring, prediction, anomaly detection and maintenance applications.

Integration Core

ML Integrations

Analytics Platforms

Connect machine learning outputs with dashboards, reporting systems and business intelligence platforms.

Cloud Infrastructure

Deploy training and inference workloads across AWS, Microsoft Azure and Google Cloud environments.

APIs & Application Systems

Expose trained models through APIs and application services for real-time or workflow-based predictions.

From Raw Data to Production Prediction

A Controlled Path From Data to Decision

Data Collection

Bring together relevant historical, transactional, operational or real-time data.

Data Preparation

Clean, transform, validate and structure the data for model development.

Feature Engineering

Create meaningful model features and select the information most relevant to the learning problem.

Model Training

Train candidate models using appropriate datasets, algorithms and experiment configurations.

Evaluation & Selection

Compare models using appropriate metrics, validation methods and business requirements.

Deployment & Monitoring

Deploy the selected model, monitor production behavior and establish processes for maintenance and retraining.

Our Machine Learning Development Approach

From Business Problem to Production Model

01 // DISCOVERY

Problem & Data Discovery

Define the business problem, prediction objective, available data, constraints and expected outcome.

02 // ASSESSMENT

Data Assessment

Evaluate data quality, completeness, structure, availability and suitability for the proposed model.

03 // DESIGN

Feature & Model Design

Design the feature pipeline and identify suitable algorithms and modeling approaches.

04 // EXPERIMENTATION

Training & Experimentation

Train candidate models, track experiments and compare alternative approaches.

05 // VALIDATION

Evaluation & Validation

Evaluate models against defined metrics and validate their behavior using appropriate test data.

06 // INTEGRATION

Production Integration

Connect the selected model with APIs, applications, workflows and enterprise systems.

07 // DEPLOYMENT

Deployment

Deploy the model through real-time APIs, batch inference or other suitable production architecture.

08 // OPTIMIZATION

Monitoring & Optimization

Monitor data, predictions and model performance, then retrain or optimize when production conditions change.

scikit-learn

XGBoost

PyTorch

TensorFlow

Pandas

Apache Spark

PostgreSQL

MySql

Docker

Kubernetes

AWS

Node.js

React

Next.js