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.
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.
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, geolocation to open new branches, customer loyalty cards and lost trolleys
CAR WORKSHOP / DEALERSHIP
Stock maintenance, fault and product shelf life prediction
Service customisation, geolocation 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
Financial data mining, geolocalisation to open new branches, fault and tax fraud detection
Customer renewals, forecast analysis, data profiling, market analysis, etc.
Basket Market Analysis
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.
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 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.