DATA SCIENCE PLATFORM Accelerate your data science journey


Establish one Data Science Platform to centralize all advanced analytics activities in your organization ensuring state-of-the-art operations – MLOps.   





Every data science project follows more or less the same pattern which we call Data Science Lifecycle: 

The mission of a good advanced analytics platform is to ensure that every step of this Lifecycle is properly defined, tracked, audited, and as automated as it is possible.  

The overall goal is to minimize the time to market of every project ensuring at the same time the highest quality standard and governance. 


Democratize Advanced Analytics 

From AutoML through Low Code to Code First Azure Machine Learning enables advanced analytics to every user, regardless of skill levels. 

What it means is that mature Data Science Platform should be accessible for every group of users: 

  • Citizen Data Scientist – people whose primary expertise is a business domain not a technical  
  • Highly technically advanced machine learning engineers  
  • Every user that is placed in between this spectrum. 


Azure Machine Learning provides these capabilities in three tiers: 

  • Auto ML – no-code solution allowing to focus on the business problem where all technicalities are handled automatically. 
  • Azure ML Designer – user-friendly drag and drop interface to build advanced analytics pipelines. 
  • Python/R notebooks that provide first-class code first experience to code and collaborate on any compute environment that can be needed.


Optimize Time to Value 

Environment configuration & Data Wrangling is crucial for every Data Science Project. Ready to use, preconfigured environments and tools ensures maximum productivity with minimal effort. 

The challenge for Machine Learning Engineer is to align the version of Python, Libraries, and drivers that are going to be used. This equals the huge amount of project time without providing explicit business value. Azure ML Compute Clusters and Compute Instance can remove this issue from the Data Scientist allowing him to maximize his time for data analysis. 

Another issue is Data WranglingAzure ML from the beginning enforces MLOps best practices.

  • allows abstracting connectivity aspects to data sources as
  • enables sharing already prepared queries between the Data Science Team
  • configure or develop queries once and reuse them later for every project requiring the same data

Scalability and cost-effectiveness 

Scalability is the inherent power of the cloud and AML enables fast scaling and adjusting compute power to exactly match your needs. Regardless of whether you need CPU, GPU, or FPGAs you pay exactly for the time Your experiments need them. 

Cost is the basic limitation of every project – especially when it comes to machine learning. In the past acquiring GPUs or dedicated compute clusters where always a serious business decision that could postpone or even block a data science initiative. Azure solves it by enabling renting these powerful resources to pay only for a given hour of use, therefore intelligent compute scaling enables cost flexibility and ensures that you only pay for the resources that you need and when you need them. 

Real-time or batch scoring

​Azure machine learning enables automatic deployment of models both for batch and real-time/ad-hoc scoring as well as native integration with all Azure data processing services.

Modeling is fun and deployment is hard. One of the true challenges of every machine learning is exciting the experimental phase and entering production. Very often integration with existing data pipelines or even applications requiring ad hoc scoring is a huge development overhead that has to be covered. Azure ML aims to change that by native integration with Azure ADF as well as automatic creation of Rest API endpoints that enable real-time inference in just a few clicks.

Train centrally deploy at site ​ 

Azure ecosystem aims to address every data scenario that an organization may face therefore it provides native integration between data processing and analytics tool.  

Especially when it comes to advanced analytics, inference latency can be the key factor for successful project deployment. We know for sure that in IoT scenarios – reaction time is money. AML delivers on that promise by greatly simplifying and automating the process of deploying Machine learning models with IoT Edge.  

It means that we have fully prepared the technical path between modeling, the path to cloud deployment, and deployment at the site. 

MLOps – Operationalize Machine Learning models and take control   

With every advanced analytics project in an organization sooner or later the time of experimenting ends, deployment to production is concluded and the process of maintenance begins.  

AML provides a fully-featured MLOps framework to ensure control, tracing, and monitoring over ongoing processes. Therefore, we have end-to-end tooling to ensure that when data drift will happen we will catch it and know exactly what is the reason behind it. 

The Data Science platform does not only have to ensure the tooling for the Data Science process itself. Integration with regards to ETL, Databases, and Logging is also a major point to address. At the same time, ensuring compliance to the highest security standards and providing thorough cost management is a must.  

Thankfully, Azure addresses all those aspects by strictly following a common technical standard through all Azure services. At Elitmind we are dedicated to providing guidance and support to ensure that every aspect of successful digital and analytical transformation is addressed. 

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Robert Woźniak

Founder / Data&AI Strategic Advisor