AI ADOPTION PROCESS.

Our AI Adoption Process is a widely-used service that enables organizations to boost their efficiency, optimize processes, and raise their production standards.

By availing nouns.ai AI Adoption process you stay competitive & prepared for whatever the future holds!

Develop extra features needed for a proof of concept that checks functionalities meet expected standards & meet business needs too. MVP Project Plan: Outline major milestones - structure/ phases/ intersections/ interdependencies - so we know how much effort is required & what costs will be incurred.

Proof of concept

Execution

We dig into datasets for information on the current problem - preprocessing data, visual analysis, finding correlations, identifying biases in data & performing unsupervised learning - so we get an overall picture of what's available. Predictive Analysis: Lastly we review technical & business risks & offer quick solutions based on use cases.

Exploratory Analysis

Idetation

Here we identify what value this model will bring, assess its viability & align expectations, then formalize the problem by detailing inputs/outputs, validation & metrics/constraints.

Use Cases

Viability

We identify and examine all available data sources (your own data, public data etc), check the services & systems for POC integration viability at the end, as well as look for potentially helpful sources of data to supplement current resources.

Data Sources

At this step we carry out a workshop with your team to explain the business vision and provide a bird's eye view of the system and data infrastructure of your business, any constraints or limitations. We also request access to data models and data if available.

Briefing

Research

This process ensures you reduce risks and costs with your AI adoption. We'll help you analyze your data and recommend the best strategy to implement AI in your company. We take a multi-faceted approach composed of four stages:

Our tech stack.

Our team of developers, data scientists and designers provide a comprehensive tech stack.

AI & Data Science

Pytorch

DVC

Mlflow

Amazon
sagemaker

Google auto ml

Vertex AI

Azure AI

Scikit-learn

Tableau

XGboost

Seaborn

Databreaks

Fastapi

Matlotlib

Pandas

Numpy

Tensor flow

Air flow

Flask

Kubernetes

Microsoft power BI

Spark

Docker

R

Ploty

Mobile

Java

Swift

React Native

Flutter

Kotlin

Design

Figma

Photoshop

illustrator

Indesign

XD

React

Angular

Vue

Webflow

Front-end

Node

Python

Rails

Django

Back-end

Amazon web
services

Azure

Google Cloud

Systems