AI ML Development

AI ML DEVELOPMENT COMPANY

Artificial Intelligence (AI) & Machine Learning (ML) Development services

Explore how AI & Machine Learning can help you transform your business

ai ml development company

How AI-ML Solutions Help Businesses

20% of C-level executives (across 10 countries and 14 different industries) report that they are using machine learning as a core part of their business. (Mckinsey)

Our AI ML Development Offerings

First Time Adopters

For First Time Adopters

We analyze your user case to develop an AI-driven software so that we can ensure its positive user buy-in. In the process, we gauge your businesses’ readiness for AI-driven automation and take you through every stage of adopting artificial intelligence in processes and operations.

Audit

AI Audit and Re-engineering

We offer our AI audit services for businesses whose AI systems fails to meet desired results. We review the algorithm (code), its architecture, the underlying business logic, and its usability. Our team then works on redesigning, correcting, or upgrading the software as per the audit findings.

Expansion

AI Expansion

We help in scaling your AI-adoption from a single purpose to company-wide business solutions. Or AI consultants will work to create a sustainable, AI-driven ecosystem for your business keeping in mind future-expansions of services, teams, and broader changes happening in the industry.

Have a project for us?

Our 4-Week
AI-Plan

product discovery workshop
  • Screening
    Screening the existing technologies (both open-source and off the shelf) that could be useful in solving your immediate business problem.
  • Proof of Concept
    Our AI-consultants looks into the perceived performance of an AI-solution which would be deployed to solve your immediate pain point.
  • Prototyping
    The lines might get blurred here during the 2nd and 3rd week as we don’t waste time. As soon as we’re ready with the PoC, we go ahead with rapid prototyping for A/B testing and decide on the best possible solution based on results.
  • Deployment Roadmap
    Our AI-consultants chalk out a detailed roadmap which will act as the blueprint in deploying the AIsolution for your business pain point.

Our 4-Week
AI-Plan

product discovery workshop

Screening

Screening the existing technologies (both open-source and off the shelf) that could be useful in solving your immediate business problem.

Proof of Concept

Our AI-consultants looks into the perceived performance of an AI-solution which would be deployed to solve your immediate pain point.

Prototyping

The lines might get blurred here during the 2nd and 3rd week as we don’t waste time. As soon as we’re ready with the PoC, we go ahead with rapid prototyping for A/B testing and decide on the best possible solution based on results.

Step 4

Deployment Roadmap

Our AI-consultants chalk out a detailed roadmap which will act as the blueprint in deploying the AIsolution for your business pain point.

Our AI-ML Development Process

01

Source and preparing dataset

We articulate the problem early on and start working on sourcing and creating datasets. We establish data collection mechanisms, audit the data quality, format it, complete sampling process and data cleaning.

02

Coding the ML model

We use the raw data or datasets prepared in the earlier stage to test the possible Ai-based solution for a particular business problem. Our AI-consultants have loads of experience in creating an algorithm (coding on server) according to the business logic and then applying it to the datasets.  

03

Evaluating and training the model

Evaluating the model with classification metrics to check accuracy and precision of the results from that model. Ultimately, the goal of any ML model is to learn from examples and generalize some degree of knowledge vis-a-vis the task we’re training it to perform. 

04

Deploying the model

For most of our projects, we deploy machine learning models in batch prediction mode or as an on-demand prediction service. Both the model has its own merits, and the decision is taken as per the requirement.  

05

Getting predictions from the model

A fit machine learning model takes inputs and makes a prediction. This means that we will provide all of the training data (previous dataset) to a learning algorithm (that we’ve created or an open source) and let the learning algorithm to discover the delineate between the inputs and the output class label that minimizes the prediction error. 

06

Monitoring ongoing predictions

Machine learning models are dynamic in nature and may degrade over time after deployed to production. Hence, it is prudent to monitor both functional and operational factors of the ML model to check any data or feature issues.  

07

Managing models and versions

The ML development model is quite complex – as it uses huge amounts of data, optimization, testing of multiple models, feature tuning etc. Hence, to make the research to be reproducible, we deploy proper version control tools to manage and track everything.

People using computers

Services we offer
What do
we offer

Technologies we use

snowflake - data intelligence technologies used by growexx

Snowflake

Amazon Web Services

tebleau - data intelligence technologies used by growexx

Tableau

powerbi - data intelligence technologies used by growexx

Power BI

apache airflow - data intelligence technologies used by growexx

Airflow

kubernetes - data intelligence technologies used by growexx

Kubernetes

jenkins - data intelligence technologies used by growexx

Jenkins

azure - data intelligence technologies used by growexx

Azure

FAQs

AI ML represents an important evolution in computer science technology and data processing that is transforming a large range of industries across teh globe.

As businesses undergo digital transformation, they’re faced with mountains of data that is at once incredibly valuable and burdensome to collect, process and analyze. New tools and methodologies are needed to manage the vast quantity of data being collected, to mine it for insights and to act on those insights and make real-time businesss decision. And this is where artificial intelligence and machine learning come in.

AI enables better and fast decision making which in turn, is based on speedy feedback mechanism. It also brings precision, speed, and efficiency to the entire software development lifecycle. AI-powered tools help find errors and fix bugs in the code. This, in turn, ensures smooth functioning in all the running environments.

The average cost to develop an AI ML app would range anywhere between $100,000 to $200,000. However, this is a very rough estimate. The final price mostly depends on the features to be added and complexity of your app. If you want to get an estimate of developing an AI app or software for your business, contact us.

Adaptive ML is a more advanced solution that takes real-time data collection and analysis seriously. As its name suggests, it easily adapts to new information and provides insights almost instantaneously.

Adaptive learning collects and analyzes data in sequential order, not all at once. This enables these adaptive ML models to monitor and learn from the changes in both the incoming and outgoing data; it allows the model to adapt its data collection, grouping, and analysis methods based on new information.

Looking to build a digital product?
Lets build it together.

Contact us now

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