Fully integrated solutions for every data science project
Some data science projects never make it further than the prototype phase. This is despite the fact that the performance of the models developed is satisfactory. This is mainly due to the fact that data science as a development is not or barely integrated into the company's existing data ecosystem and processes. However, if machine learning is located in the cloud right from the start, a data science project can achieve sustainable success.
Everything revolves around data and analysis
When talking about machine learning in the cloud, the three public cloud providers Google, AWS and Azure are always mentioned in the same breath. These services are not only established hyperscalers, but above all offer a quick and cost-effective introduction to the cloud technologies required for data-driven projects. Data scientists use their machine learning processes for a wide range of application scenarios directly in the cloud and execute them there. Getting started in the cloud with a data science project is therefore possible on a small scale and can be scaled up as required. Offerings, costs and user-friendliness can differ significantly between providers, so a detailed evaluation of the individual features should be carried out before selecting a suitable platform.
Machine learning for data science in the cloud offers an easy introduction to the subject matter and enables a simple handover to non-technical people, as no programming knowledge is required for drag & drop. Nevertheless, the environment remains flexible, as individual special solutions can be easily integrated using Python code. The model structure is always clearly visualized.
Integration from the database to the dashboard
The fact that data science development takes place directly in the cloud means direct integration into the organization's data workflow right from the start. This has several advantages: On the one hand, the model links directly to the databases and thus avoids detours due to intermediate storage on development end devices. This is reflected in the transfer speed, but also in the costs, especially for large volumes of data. At the same time, direct transfer, which can even take place within the server if necessary, is less susceptible to attacks by malware.
Just like the data input, the results of the machine learning model are also directly connectable for processing in further steps, such as visualization in Power BI or Tableau Dashboards. This ensures direct integration of the data science measures into the company's workflows. Data science solutions must always be considered and developed in the overall context of the organization from the outset.
Scalability, flexibility, availability
The general advantages of the cloud become an additional game changer in the context of data science and, in particular, data science projects. The full bandwidth of the cloud provider is available for the sometimes huge volumes of data, regardless of the company's own infrastructure, which can only provide the same performance at high cost. And even with rapidly changing requirements, such as strong growth or erratic load peaks, cloud solutions for data science projects provide a high level of scalability and flexibility. These abstract advantages are invaluable in specific use cases.
Here is an example: a retail chain wants to use machine learning algorithms to make predictions about stock levels and product demand. The aim is to ideally align the daily supply chain with customer demand. Depending on the variety of products on offer, a wide range of data can be processed. The cloud architecture not only ensures the availability of the necessary bandwidth, but also that failures in critical data processing can be distributed to other servers in an emergency. The risk of stock shortages and the associated loss of sales is therefore reduced to a minimum.
Data protection in the cloud
Azure-based cloud solutions are GDPR-compliant. With the right configuration, the architecture can be designed in such a way that sensitive data does not leave the EU.
- Free and large selection of server location enables GDPR-compliant data storage
- Proof of data security through recognized cloud computing certificates, e.g. "TrustedCloud" from the Federal Ministry for Economic Affairs and Climate Protection
- Various encryption and anonymization options with Azure or AWS
Pre-trained models for every data size
Even though big data is often mentioned in the same breath as cloud services, data science projects in the cloud are particularly interesting for use cases where little to no data is available. For such cases, Azure Cognitive Services, for example, already provides standardized models pre-trained with your own data, which can be integrated into your own project with minimal effort.
If, for example, text documents are to be read in for sentiment analysis using speech recognition AI, there is no need to train complex NLP models using manually digitized texts. Instead, freely available modules such as Azure Cognitive Services, which already have their own vocabulary, can be integrated into the workflow. However, pre-trained models are also useful if large amounts of data are already available, as time and data can be used efficiently for the further development of the specific use case instead of starting from scratch.
From MVP to production in just a few clicks
If a promising product is created at the end of cloud-based data science MVP development, integration into the operational business is much easier than with local development. Thanks to development in the cloud, there is no need for a complex move from the development to the production environment and data pipelines do not need to be adapted. Services such as AzureML have been developed in such a way that MLOps can be implemented with the least possible effort. This means that the interface between machine learning (ML) on the one hand and operations (Ops) on the other is optimally integrated through functions such as automatic deployment pipelines, version control and extended monitoring.
This not only makes the work of data scientists easier, but also enables the algorithm/model to be used reliably. Decision-makers in the company can therefore rely on the insights gained at an early stage of development in their day-to-day decision-making - thanks to machine learning and data science in the cloud.
