MLOps

At 10Clouds, we’ve long been aware of the increasingly important role of ML in the future of digital products. At the same time, we’re passionate about driving innovative solutions which shorten the development lifecycle without compromising on quality. This is why we ensure that our data scientists, developers and operations teams work effectively together, in order to streamline processes and to provide deeper, more consistent, and more useful insights from ML.

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We are leading the way in inter-team communication

We’ve always had a strong culture of inter-team collaboration, which means that our Machine Learning and Engineering teams work together effectively to leverage both skillsets, resulting in much more efficient processes.

We remove technical obstacles

We help you realise your business goals without worrying about obstacles. It doesn’t matter to us if you have a hundred or a million clients - we prepare your website for high traffic. Importantly we will you solve business problems quicker.

We’re on top of the latest regulations

As machine learning becomes more common, there are new regulations surrounding the operational side and penalties can be invoked for failure to comply. Our Ops team are on top of all the latest regulations, allowing our data scientists to do their job and for you to have piece of mind.

We leave more time for innovation

Because our streamlined processes lead to improved efficiency, we are left with more time for proactively brainstorming and helping you to further improve your digital product. We give you the support you need to further drive your business forward.

How do we implement MLOps?

There are several stages in our MLOps system. If you're interested to find out more about how we work, you can take a look at them below.

01

Collaboration

We ensure effective collaboration between developers, data scientists and the ops team. Our ML team also has development skills including code modularization, testing and versioning so they have a strong understanding of how their work will sit within the broader product.

02

Building using pipelines

When it comes to machine learning, any form of build involves pipelines formed of extractions, transformations and loads - all of which form an essential element of data management. As data transformation is a key part of ML, pipelines are essential which is why we have built them into our processes.

03

Monitoring

Any projects involving machine learning require a far greater level of monitoring than those without. Why? It’s because you need to ensure that you’re constantly operating within regulations and that quality information is being returned by your systems. For this reason, we make sure our data is retrained when needed.

04

Versioning

In a regular software setup you only need versioning code because all behavior is determined by it. In ML, as well as the familiar versioning code, we also need to track model versions, the data used to train it, and some meta-info such as training hyperparameters.

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MLflow

At 10Clouds, we use MLflow, an open source library that offers a full-cycle machine learning platform, making it easy to develop, deploy, and share models. It offers a set of APIs that work with any library (TensorFlow, PyTorch, XGBoost, etc.) and in any environment, including the cloud.

MLFlow records and tracks training runs and model artifacts, which helps the DS team to track the experiments and for the client to see the progress. The MLflow library also has a built-in Model registry, which serves as storage for all production ready ML models.

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