Our Data Science Lifecycle includes these steps:
DS step1
DS step1

1

Step

Data Acquisition

Collected data from various sources in a variety of formats, ranging from unstructured data to structured XML documents

DS step2
DS step2

2

Step

Data Analysis

This involves applying analytics techniques to identify key predictors and descriptors, which can then be used in an algorithm to identify leading trends.

DS step3
DS step3

3

Step

Data Transformation

Analyzed data can be transformed in accordance with business rules to discern correlations and generate possible resolutions.

DS step4
DS step4

4

Step

Modeling & visualizations

Performing analysis of data leveraging different modeling techniques to answer business-critical challenges.

DS step5
DS step5

5

Step

Maintenance and Support

This process can be further extended through supporting to automate failure resolution proactively.

Artificial Intelligence & Machine Learning Solution

AI and machine learning are coming into their own amid a data explosion. Machine learning is contingent on huge amounts of data to train the algorithms so that they can adjust and improve. Organizations today have a mountain of data - and will continue to generate more and more. With AI and ML, it's possible to use that data to get value not possible with human analysis alone, leading to improved effectiveness and productivity with new insights and automation.

Knoldus believes that AI and machine learning are going to play a critical role in your ability to get answers from your machine data. Hence, we allow you to complement your expertise of your organization and your data with AI and machine learning for enhanced effectiveness and productivity, across industries, use cases, and skill set.

Our Data Science service with AI and ML enable organizations:

Build Scalable and highly accurate product demand forecast
Build Scalable and highly accurate product demand forecast
Identify the most valuable customer segment
Identify the most valuable customer segment
Recommends products to cross-sell and upsell
Recommends products to cross-sell and upsell
Better collaborate with peers
Better collaborate with peers
Avoid costly downtime and improve customer satisfaction
Avoid costly downtime and improve customer satisfaction
Dissolve IT silos and disjointed responses
Dissolve IT silos and disjointed responses
Eliminate tedious and manual tasks
Eliminate tedious and manual tasks
Predict and optimize maintenance to reduce servicing costs
Predict and optimize maintenance to reduce servicing costs

Tools and Technologies

At Knoldus, we build and operate fast data using an emerging architecture "SMACK" stack (Spark, Mesosphere, Akka, Cassandra, and Kafka). This enables enterprises to easily access tremendous amounts of data - businesses extract value from data and build modern applications that serve customers with personalized services, in real time, and at scale.

spark

Fast and general engine for distributed, large-scale data processing, machine learning, SQL, and graph computation

Mesos

Cluster resource management system that provides efficient resource isolation for running data services and containerized microservices

Akka

A toolkit and runtime for building highly concurrent, distributed, and resilient message-driven applications on the JVM

Cassandra

A distributed, highly available database designed to handle large amounts of data across multiple data centers

Kafka

A distributed, highly available messaging system to ensure millions of events per second arecaptured through connected endpoints with no loss

How we did it before?

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Case Study

Hewlett Packard Enterprise

Knoldus helps HPE not only build customer value, but also gain momentum for analytics transformation.

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Case Study

Dun and Bradstreet

D&B handles almost 8x times the volume of alerts through rapid development and concise code offered with Scala.

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Case Study

Elsevier

Elsevier enables the user to derive new data insights with the reactive technology stack and architecture

Ready to dive into the future with AI and machine learning?

Schedule a Meeting
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