How ML cube can help

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Do you want to accelerate the AI transformation journey of your business, but don’t know where to begin?

Get started with ML cube

Use Case Definition Data Preparation AI Model Development

PICK YOUR AI CONSULTING SCENARIO

Identify use cases that can impact your business. Analyze the value of each cases. Prioritize your use cases.

WHY IS DATA PREPARATION IMPORTANT?

Most machine learning algorithms require data to be formatted in a very specific way, so datasets generally require some amount of preparation before they can yield useful insights.

WE PROVIDE PROFESSIONAL READY-MADE AI BUSINESS MODELS

With our in-house team of AI consultants, we work across the entire stack, with an end-to-end approach, to develop AI-powered systems for the customers, and guarantee the maintenance of high standards performance of their systems over time.

We've got it all covered

Our in-house, collective skillset and experience allow us to develop AI projects end-to-end: we will take care of your entire project from business framework to KPIs, from data preparation to scalable deployment and production.

Prepare

Scoping & Architecture Design

We understand your goals and analyze your business requirements: We investigate to determine whether it requires a ML solution. We work closely together to define KPIs, prepare a road map, review scientific literature and determine requirements.

Project Setting & Governance

We make a decision about introducing AI in your business: If positive, we gather the requirements and prepare a plan for your project, with milestones, deliverables and specific deadlines. If not, we save you time and money by not implementing AI.

Data Exploration

We explore robustness and completeness of available data If you have sufficient data ready for collection and analysis, that’s perfect! If not, we will collect them in any way possible. Once data are available, we perform an exploratory analysis phase to find patterns and correlations.

Execute

Model Developement

Kick it off! We run thousands of parallel experiments to develop an AI model. A model is the core of an algorithms system – trained on historical data it can predict future trends or understand the semantics of a text.

Data Collection & Storage

Perform it… We perform state-of-the-art scraping, with our sound expertise in sourcing data: from the web, documents, databases and APIs. We also do crawling and build bots which automatically achieve our clients’ tasks.

Productization

…Productize When we get the proof that our prototype model can address your business problem, we will launch it in production. We'll integrate the model with your application and provide support services to make sure it works on a broader data set.

Our management process is designed to minimize organizational overhead and avoid information mismatching.

The documentation produced at each step allows to capitalize the experiences and to reuse part of the work done for other future scenarios.

The trial step is independent of the productization, so you can decide to proceed after observing the forecast test’s quality.

A structured and modular approach

Our Project Management

Phases Activities Documentation Methodology

Phases Withouttime, ML cube

Requirements analysis
KPIs design (metrics & measures)
Critical issues Identification
Data set extraction and analysis
Alignment with IT and Business regarding normalization strategies
Accuracy report processing
Software Development and setting
Role-based access control or role-based security
Training
Check KPIs results
Identification of improvements and new features

Use case description
Dataset report
Requirements report
Accuracy report
SW technical documentation
Overall scenario report
KPI results and appraisal

At every stage of the journey, we will formalize every decision, objective, analysis and result to improve the understanding of the project and the sharing of choices
We guarantee a close collaboration with the client's key resources and a thorough training course for the entire journey
Modern data analytics technologies require rigorously quantitative application methodologies to ensure a perfect alignment of interests between supplier and customer.
For this reason we invite our customers to deliver the complete dataset only after having received the red-list at the end of the trial project.