Data Science Lab and Machine Learning for Enterprises
Move your company into the Data Science Lab powered age with help of Addepto’s experienced data consultants. Take advantage of our Machine Learning Lab solutions.
Gain a reliable business partner – let’s introduce your enterprise to a new era of Data Science Lab opportunities!
What do we do?
Addepto experts help enterprise customers to optimize their operations, increase efficiency, and avoid risks by introducing custom made Artificial Intelligence (AI) models on top of their internal systems.
We deploy custom Data Science Lab solutions to enrich their internal system with insights. Our team is a trusted Business Intelligence and Machine Learning consulting partner for many big corporations.
Addepto’s full scope of Machine Learning projects includes:
- Data Science Lab solutions,
- analysis of AI potential in corporations,
- integration of internal systems,
- implementing customized machine learning solutions on top of their own data.
Solutions that we build, help companies to gain a competitive advantage, optimize operations, and increase sales.
Our full scope of machine learning project includes:
- analysis and development of data integration packages and jobs,
- building and implementing Enterprise Data Warehouses,
- developing OLAP and Tabular cubes,
- implementing self-service BI tools (Tableau, Power BI, Tibco Spotfire, Looker, and many others) to visualize all customer’s data.
What is Data Science Lab?
Simply put, Data Science Lab is a separate environment, designed to enable data scientists and analysts to extract valuable insights that are hidden in your company’s data.
Data Science Lab will help you:
- find the right questions to ask,
- get accurate answers that will help your enterprise grow.
Why work with us on Data Science Lab projects?
You don’t have to worry about the quality thanks to our expertise
We implement solutions that can save you thousands of dollars in operational costs
Start in 1 week
We can assign experts to your project in as short as 1 week
Creating own AI departments might be a big investment
You can create your internal team whenever you want
We are able to keep high work quality and low rates thanks to our own training methodology.
What is the process?
1. Needs assessment and brainstorming
In the first stage of our cooperation, we help particular teams inside the company come up with ways of solving their problems using AI or Business Intelligence by providing context and examples of how it can work. After deeply analyzing a company’s goals and structure we also suggest our own ideas. It gives a clear overview of what benefits advanced solutions could bring to your company.
2. Tech stack and database overview
Having general ideas about the directions of our project, we need to analyze the company’s internal tech stack and available data to assess the feasibility and time we need for the implementation.
3. In-depth project planning and analysis
At this stage, we make a schedule of the workflow and lay out our exact needs from the company’s team. Tasks are planned and the team for development is prepared.
4. Integrate data
The key step before we can create our solution is to gather all necessary data from a variety of sources (structured and unstructured) and prepare this data for the modeling stage.
Building a machine learning solution
5. Model creation
This is one of the most important stages which is responsible for model creation, test, and deployment of different algorithms that will be used in the final product.
This step is optional. In many cases, companies need a way to display the insights from our models. In order to do that, we create custom business intelligence dashboards. Those visualizations are integrated with companies reporting systems or internal business applications.
7. Integration and testing
At this stage, we integrate our models and dashboards with your infrastructure and begin testing the service.
8. Service and enhancement
After the implementation, we support the company in case of any troubles and continually optimize the models and data processing.