Data Science

Turn your data assets into insights with Kloud9’s AI-First Approach

It’s imperative to drive value out of the diverse data assets in organizations. The sheer volume of these assets has made it increasingly difficult to use traditional tools and approaches to support desired outcomes. Whether it’s customer information coupled with all the behavioral signals or complex supply chain networks to ensure on-time and expected fulfillment to store networks – organizations will benefit from AI driven and actionable insights.

At Kloud9 we built a Data Science framework to rapidly benefit our clients’ desired outcomes. This framework provides a standardized and efficient way to conceptualize, build and deploy Data Science assets. This allows for unparalleled business benefits and quicker ROI.

Whether you're looking to optimize existing business processes such as eliminating stockouts in retail, improving process manufacturing QA with machine vision, improve recommendations for consumers or optimizing work force planning - Kloud9 has the experience and team to help you propel your business forward.

Kloud9’s Data Science Framework

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Kloud9’s Data Science Framework Consists Of Four Key Components

Pattern Recognition & Feature Engineering

The appearance of new concepts in systems and handling of large amounts of information has generated a novel trend in the field of Data Science – Pattern Recognition.

Pattern Recognition is the recognition of patterns and regularities in data and is closely associated with Artificial Intelligence (AI) and Machine Learning (ML). Depending on the use case and type of incoming data, different pattern recognition methods are applied. The data can range from texts, images, sentiments to even sounds.

The three common types of Pattern Recognition are:

1. Statistical Pattern Recognition

This refers to pattern recognition based on statistical historical data. It collects found patterns, processes them, and learns to generalize and apply these rules to new and similar observations.

2. Syntactic/Structural Pattern Recognition

This type of pattern recognition relies on simpler structural sub patterns. The pattern is described in terms of connections between the sub patterns.

3. Neural Pattern Recognition

Artificial Neural Networks are used in this type of pattern recognition. Kloud9 uses Pattern Recognition across Retail, Consumer Goods, Healthcare, and Manufacturing organizations enabling them to make informed business decisions.

In today’s world, Feature Engineering has become an integral part of Data Science as it increases prediction accuracy. Microsoft defines Feature Engineering as, “The process of creating new features from raw data to increase the predictive power of the learning algorithm. Engineered features should capture additional information that is not easily apparent in the original feature set”.

Feature Engineering is performed to:

  • Improve the predictive performance of a  model
  • Reduce computational or data needs
  • Improve how the results are interpreted

Kloud9 enables organizations across the Retail, Consumer Goods, Healthcare, and Manufacturing industries to engineer new features that can improve their performance thereby resulting in productivity improvements and cost savings.

Anomaly Detection

Businesses today are dealing with an abundance of data daily. Big Data sometimes gets accumulated in unmanageable datasets forcing organizations to make faster decisions in real-time. One way to process data faster and more efficiently is to detect any abnormal events, changes, or shifts in datasets.

Thus, Anomaly Detection, a technology that relies heavily on Artificial Intelligence (AI) and Machine Learning (ML) are used to identify abnormal behaviour within a given pool of collected data.

Kloud9’s Anomaly Detection use cases include:

  • Fraud detection alerts for Credit Card transactions
  • Fraud detection alerts for Gift Cards transactions
  • Anomaly detection alerts and dashboards on Omni Linux Servers
  • Store Server Anomaly Detection

Time Series Forecasting

Decision-makers across businesses are constantly facing the challenge of predicting the future as accurately as possible when making operational, tactical, and strategic decisions to derive maximum business value. The availability of time-stamped data is extremely critical for this forecasting. Studying time-series data and statistics to make forecasts and strategic decisions using data models is referred to as Time Series Forecasting.

There are two types of Time Series Forecasting:

  1. Univariate: Analyses a single data series
  2. Multi-variate: Analyses multiple series of data to explore long-term trends

Machine learning (ML) and Artificial Intelligence (AI) applied to Time Series Data is an efficient and effective way to analyze data, apply a forecasting algorithm, and derive an accurate forecast.

Time Series Forecasting has a range of practical applications in Retail & Consumer Goods, Healthcare and Manufacture, including:

  • Demand Forecasting
  • Sales Forecasting
  • Economic Forecasting
  • Disease Progression
  • Estimating Mortality Rate
  • Process and Quality Control
  • Inventory Studies
  • Workload Projections and more…

Kloud9’s data experts use Time Series Forecasting to understand business problems and have the appropriate data and forecasting capabilities to find a fitting solution to the problems.

Computer Vision & Image Recognition

As organizations continue to grow there is an increasing demand to use Artificial Intelligence (AI), Machine Learning (ML), and Neural Networks to teach computers to see defects and issues before they affect business operations. Computer Vision is a component of Artificial Intelligence (AI) that is used in industries ranging from Retail and Consumer Goods, Healthcare and Manufacturing – and the market is continuing to grow. It is expected to reach USD 48.6 billion by 2022.

Computer Vision analyses content and extracts rich information from images and video. AI-enabled Computer Vision runs on advanced algorithms that can analyze visual content in different ways based on certain input and user choices.

Computer Vision often includes Optical Character Recognition (OCR), Image Analysis, and Spatial Analysis.

Some of the established Computer Vision tasks include:

  • Image Classification
  • Object Detection
  • Object Tracking
  • Image Retrieval Based on Content

As per Gartner Glossary, “Image recognition technologies strive to identify objects, people, buildings, places, logos, and anything else that has value to consumers and enterprises”.

The inputs are in the form of images and a computer vision algorithm generates a deciphered output.

Some of the established Image Recognition tasks include:

  • Tagging Images
  • Visual Search
  • Facial Recognition
  • Image Categorization
  • AI Photo Enhancement and more…

Kloud9’s Core Data Science Capabilities

Data Scientists
Dedicated AI / ML Center of Excellence (CoE)
Use Cases
Kloud9’s Data Science practice provides customers with benefits such as faster ROI, reduced time to implement and excellence in performance. If your organization is looking to rapidly unleash the power of Data Science, then Contact Us!