Text Analytics Engine: 2-Week Proof of Concept

Do you believe that the voice of the customer is the key to unlocking their best experience? Do you collect a lot of customer feedback and struggle to pull meaningful insights from the data?

Understanding what your customers are saying about your products, services, or events can be challenging—especially if you have large volumes of text data across numerous channels. For customers to be satisfied, you need to know what they are saying, what their pain points are, and how to improve their experience.

You can manually collect and analyze text data from multiple sources, but this process is time-consuming and prone to bias.

Our Text Analytics Engine uses advanced machine learning to generate insights, automate processes, and reduce the effects of bias. During our two-week Text Analytics Proof of Concept (POC), our team will work with you to understand your text data and define end-to-end Azure pipelines and data flows. We will also enable you to implement the model with minimal effort, based on your unique requirements.

Target Audience

• Project Managers
• Architects
• IT Business Heads
• Technical Solution Leads

Agenda

Day 1–3

• Connect with data SMEs from your organization and understand the nature of your data.
• Define the problem statement for the use case.
• Sign up for the necessary Azure subscriptions.

Day 4–7

• Create Python notebooks with sample data in your subscription for data cleaning, feature selection, and model implementation.

Day 8–10

• Undergo model tuning, review, demo, and testing.

Deliverables

• Detailed plan and architecture diagram of the production setup, including estimates for the:

a. Ingestion pipelines and data processing (using ADF). b. Reusable Azure Databricks notebooks that are used for data cleaning, model execution, and retraining. c. ML operation using Azure ML Service, serverless architecture, Azure functions, Azure Databricks, and more (depending on the use case).

• POC Python notebooks for data cleaning, feature selection, and model execution (using immediately available MAQ Text Analytics libraries for the identified use case)