AZURE MACHINE LEARNING

An affordable Machine Learning workshop by SolidQ and Microsoft that adapts to your needs. Simply indicate the type of industry you work in and your forecasting needs and we will suggest solutions to transform your business.

5-DAY AZURE MACHINE LEARNING WORKSHOP

A 5 day lab workshop where we will be working with your real data together with your own team.  This will be undertaken in 5 stages:

STAGE 1: identification and specification

We will identify your data sources (Excel, Access, SQL Server, ERPs, OData, etc.) in order to define the business issues to be predicted.

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STAGE 2: Analysis and diagnosis

We shall analyse the variables and check the quality of all affected business data variables.

STAGE 3: Implementation

A first draft solution is implemented featuring a basic algorithm with no customisation.

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STAGE 4: Result validation

Validation of results together with your team and analysis of the different resulting factors.

STAGE 5: In-house training

We will train your team to learn the solution’s basic principles, as well as the technologies used, how to interpret the data and how can the solution benefit your business.

“From the moment our solution was launched, we have made savings in our production processes since our ability to predict our sales volumes has prevented the accumulation of excess stock”

Mario Real

“Our decision making has become more specific as we are now able to predict what will happen in terms of sales and to take advantage of our own data. After only 5 days, the workshop has helped us to see all the possibilities and the large amounts of hidden data we had, which can now be provided by our own system”

Paloma Romero

“Our hospital has implemented a forecast analysis system in order to predict whether a patient is likely to suffer any specific disease over time”

Carmen Gilabert

It can be applied to any industry, including but not limited to the examples below:

RETAIL / SUPERMARKETS

Sales forecasts, geolocalisation to open new branches, customer loyalty cards and lost trolleys

CAR WORKSHOP / DEALERSHIP

Stock maintenance, fault and product shelf life prediction

CATERING

Service customisation, geolocalisation to open new branches, search engine optimisation and sales forecasts

HEALTH / INSURANCE

Risk forecasting and policy calculations. Prediction of patient’s future diseases and living standards

BANKING

Financial data mining, geolocalisation to open new branches, fault and tax fraud detection

TELECOMMUNICATIONS

Customer renewals, forecast analysis, data profiling, market analysis, etc.

Churn Detection

Text Mining

Basket Market Analysis

Collaborative Filtering

Fraud Detection

Forecasting

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Prescriptive Analytics

Data Profiling

Data Understanding

ANALYTICS AND DATA SCIENCE

 

Because of the massive data explosion in every industry, we turn to Machine Learning to help us fill the gaps in what we can compute. With the Fractional Data Science offering, SolidQ helps your team understand the standard process for Data Mining and the implementation of Machine Learning.

A typical Fractional Data Science engagement comprises the listed six points below. It starts with developing a business understanding and then asks what data corresponds to that understanding. SolidQ helps you prepare your data, model it, and then deploy using Machine Learning.

Training

Our Data Science team excels at mentoring and training. In this first step of Data Science adoption they walk your team through the tactics of data preparation and modelling. This is a classroom-style element designed for the entire team. Training topic can include Azure ML, R coding, SSAS, and Data Mining.

Data Preparation and Overview

After your team transforms the data and loads it into a SQL Server testing database, SolidQ works to discover more in-depth understanding of the data. By preparing several data sets that take into account various time frames (months, years) we can determine the distribution of values and prepare several additional computed variables.  We then work to identify the relationship between the passage of time and the customers’ behavior.

Preparing and Evaluating Data Mining Models

We identify patterns within the data by using directed data mining techniques – Decision Trees, Naïve Bayes and Neural Networks. We prepare several models with different data sets then prepare those models by applying different algorithm parameters for each set. Because there is no way of knowing in advance how many models we will actually need, we limit this phase of development with time constraints.

Evaluating the Efficiency of the Models

It’s important to spend time determining if the deployed models need refinement or become obsolete. We conduct all efficiency measures in a small, controlled data warehouse and analyze them with OLAP cubes or PowerPivot.

Deployment

Deployment parameters are set by you according to your teams skills and project scope.  SolidQ can prepare deployment reports and DMX queries in your OLTP application. We can also include churn results into your existing OLAP cubes.

Review of the data and models

SolidQ will review the data models with your team after deployment to encourage refinement and learning.
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