Data science in the cloud

Fully integrated solutions for every data science project

Some data science projects never make it past the prototype phase. And this is despite the fact that the performance of the developed models is satisfactory. This is primarily 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 project in data science will be successful in the long term.

It's all about data and analytics

When we talk about machine learning in the cloud, the three public cloud providers Google, AWS and <a href=” https://www.taod.de/tech-beratung/microsoft-azure “data-webtrackingID="blog_content_link” > Azure are actually always mentioned in the same breath</a>. 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 with the cloud with a data science project is therefore successful on a small scale and can be scaled up as required. Offering, costs and usability differ significantly among providers, so that a detailed evaluation of the individual features should be carried out before choosing a suitable platform.

Machine learning for data science in the cloud offers an easy introduction to the subject matter and enables easy handover to people who are not technically savvy, because 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 database to dashboard

The fact that data science development takes place directly in the cloud means immediate integration into the organization's data workflow right from the start. This has several advantages: On the one hand, the model connects directly to the databases and thus avoids detours due to caching on development devices. This is reflected in the transfer speed, especially when it comes to large amounts of data, but also in the costs. At the same time, direct transmission, which may even take place within the server, is less susceptible to attacks by malware.

<a href=” https://www.taod.de/tech-beratung/power-bi “data-webtrackingID="blog_content_link” > Power BI</a> oder <a href=” https://www.taod.de/tech-beratung/tableau “data-webtrackingID="blog_content_link” >Just like the data input, the results of the machine learning model are directly connectable for processing in further steps, such as visualization in Tableau dashboards.</a> This ensures direct integration of data science measures into the company's work processes. Data science solutions must always be thought of and developed from the outset in the overall context of the organization.

Scalability, flexibility, availability

The general benefits of the cloud are described in the context of data science and in particular Data science projects as an additional game changer. As a result, the cloud provider's full bandwidth is available for the sometimes huge amounts of data, completely independent 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 unsteady load peaks, <a href=” https://www.taod.de/services/data-engineering-consulting “data-webtrackingID="blog_content_link” > cloud solutions for data science projects provide high scalability and flexibility.</a> These abstract advantages are invaluable in specific applications.

Here's an example: A retail chain wants to use machine learning algorithms to predict inventory levels and product demands. The aim is to ideally tailor the daily supply chain to customer demand. Depending on the variety of offers, a wide variety of data can be processed. The cloud architecture not only ensures the availability of the necessary bandwidth, but also that failures of such critical data processing can be distributed to other servers in an emergency. The risk of inventory bottlenecks and the associated loss of revenue is thus reduced to a minimum.

Data protection in the cloud

<a href=” https://www.taod.de/tech-beratung/microsoft-azure “data-webtrackingID="blog_content_link” > Azure-based cloud solutions are GDPR-compliant</a>. With the right configuration, the architecture can be designed so that sensitive data does not leave the EU.

• Free and large selection of server locations enables GDPR-compliant storage of data
• Evidence 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 <a href=” https://www.taod.de/services/data-engineering-consulting “data-webtrackingID="blog_content_link” > cloud services, data science projects in the cloud</a> are particularly interesting for use cases where little or no data is available. For such cases, Azure Cognitive Services, for example, already provides standardized models pre-trained with its own data, which can be integrated into your own project with minimal effort.

For example, if text documents are to be read in using speech recognition AI for sentiment analysis, there is no need to train extra complex NLP models using texts digitized by hand. Instead, freely available modules such as Azure Cognitive Services can be built into the workflow, which already have their own vocabulary. But even when larger amounts of data are already available, pre-trained models are useful because — instead of starting from scratch — time and data can be used efficiently to further develop the specific use case.

From MVP to production in just a few clicks

Is at the end of cloud-based Data Science MVP Development A promising product, integration into operational business is significantly easier than with local development. Thanks to development in the cloud, there is no need for a complex move from the development environment to the production environment and data pipelines do not have to be adapted. Services such as AzureML are designed so that MLOps can be done with the least possible effort. This means that the interface between machine learning (ML) on the one hand and operational business (ops) on the other is optimally integrated through features such as automated deployment pipelines, version control, and advanced monitoring.

This not only makes the work of data scientists easier, but also enables reliable use of the algorithm/model. Decision-makers in companies can therefore rely on the knowledge gained at an early stage of development when making decisions in everyday life — thanks to machine learning and data science in the cloud.

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