Enterprise AI

Our Approach to AI projects

Discovery

Pre-requisite

Defined business question

Activity

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

Accelerator

Pre-requisite

Defined problem statement with data sources identified

Activity

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

Learn more about Data Science Accelerator Program

Deep Dive

Pre-requisite

Viability established through Data Science Accelerator

Activity

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

MLOps1

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

Transform your business with AI

Our Approach to AI projects

Discovery

Accelerator

Deep Dive

AI Accelerators

Target Operating Model

Jumpstart your AI Journey

Upskill your team to unleash the full potential of AI

Azure ML : Enablement Program

Azure ML : Enablement Program

Make the most of your Data

Quick links

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