Transform your business with AI

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Artificial Intelligence

Our Approach to AI projects




  • Defined business question


1/2 day free workshop to
  • Define the data science problem
  • Identify relevant datasets

Outcome: Defined use cases and scope for Data Science Accelerator Program




  • Defined problem statement with data sources identified


2-3 weeks accelerated data science project

  • Exploratory data analysis
  • Data cleansing
  • Initial feature engineering
  • AutoML modelling

Outcome: A deployable ML model and project viability validation


Deep Dive


  • Viability established through Data Science Accelerator


  • Overall scoring pipeline build
  • Custom modelling to improve performance
  • Model explainability build
  • Automated retraining pipeline
  • Drift detection
  • Alerting system
  • Dashboarding

Outcome: Productionized solution with end to end MLOps

AI Accelerators

We deliver use cases faster using our AI use cases accelerators. Some examples of AI accelerators are
Retail Vision
Brand Recognition
Just Listen
Predictive Maintenance
Sales Forecasting
Web Automation

Target Operating Model

Scale up your Data Science and AI processes throughout your organization with our Target Operating Model Process Document. This document is a set of definitions, guidelines, governance controls and assurances, carefully curated for everyone to follow in your team.

See this video to understand why you need to have a TOM and an Enablement Plan for your team

Jumpstart your AI Journey

Upskill your team to unleash the full potential of AI

Companies often struggle to achieve their full AI potential due to lack of the right frameworks and methodologies. 

Upskill your organization with our AI Enablement Program to empower them to unleash maximum value from AI in minimum time

  • Data Scientists
  • Citizen Data Scientists

Azure ML : Enablement Program

Day 1
Introduction to Azure ML with Databricks
  • Basics of Azure ML Service
  • Introduction to Databricks
  • Azure AutoML
Day 2
Model interpretability
No Code Machine learning AML
  • Basics of model interpretability
  • building model interpretability
  • Auto ML GUL
  • Using AML Designer
Day 3
  • Capturing the governance data for end to end ML lifecycle
    • Integration with Git
    • Azure ML Datasets
    • Azure ML run history
    • Azure ML model registry
  • AML Pipelines for lifecycle automation
  • ML Flow
Day 4
MLOps 2
  • Detecting and managing data drift
  • Create event-driven machine learning flows
Day 5
Target Operating Model (TOM) Walk-through

Understand the key TOM concepts (1/2 day)

Day 6 onwards
Start with your data science project migration

Azure ML : Enablement Program

Day 1
Basics of data science
  • Introduction to Machine Learning Supervised vs Unsupervised
  • CRISP-DM process for data science
  • Data preparation using ADF wrangling dataflows
  • Building blocks of Azure Machine Learning service
Day 2`
Classification problems & AutoML
  • Introduction to classification problems
  • Confusion matrix
  • Performance metrics
  • Classification using Azure AutoML
  • Scoring
Day 3
Forecasting problems & AutoML
  • Introduction to forecasting problems
  • Types of forecasting problems
  • Different configurations for framing forecasting problems
  • Forecasting using Azure AutoML
Day 4
Regression problems & MLOps
  • Introduction to regression problems
  • Performance metrics
  • Regression using Azure AutoMl
  • Model deployment
  • Building model interpretability
Day 6 Onwards
Starts with your data science project migration

Make the most of your Data

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