Home Secure and controllable AI systems with MLOps

Secure and controllable AI systems with MLOps

How can the efficiency and quality of machine learning projects be maximized? Machine learning operations (MLOps) refers to the automation of the entire lifecycle of ML models: from data preparation to deployment and continuous monitoring. This approach reduces costs, optimizes the use of resources, and enhances security through standardization and continuous validation.

Machine Learning Operations (MLOps) technology

Our MLOps solutions

We help clients in the fields of mobility, medical technology, and industry develop MLOps pipelines for their machine learning applications. By continuously evaluating new tools on our on-premises GPU clusters and in the Azure Cloud, we develop cloud-agnostic, secure, and powerful solutions that are tailored to industry-specific requirements. This covers all phases of development, from design to maintenance.

A 3D-illustrated MLOps machine learning field shows the production of ML models in the digital cloud environment of data and digital brains.

Machine Learning pipelines

Machine learning pipelines enable the development of state-of-the-art ML solutions through end-to-end pipelines, from data acquisition through to model deployment. These pipelines include critical steps such as feature engineering, hyperparameter tuning, and AutoML to enhance model performance and accuracy. They also aim to optimize the performance and cost-efficiency of ML systems by using resources efficiently and maximizing scalability. By integrating these aspects, companies can effectively manage their ML models while keeping costs under control.

An employee wearing AR glasses monitors the model performance of a machine in production and evaluates its effectiveness based on the data provided.

Model monitoring and maintenance

Model monitoring and maintenance within the context of MLOps refers to continuous performance tracking to ensure effective operation in production. This includes automated model evaluations in both production and shadow mode to detect changes in performance. Data drift detection techniques are also used to identify changes in input data. In addition, automated retraining cycles are scheduled to ensure that models are updated with the latest data and maintain their performance.

Data management

Data management encompasses a number of different aspects, including the selection of tools and implementations for data processing, as well as metadata management. It also involves both automated and manual data sorting techniques to ensure efficient handling. Furthermore, it includes methods for anonymizing and tagging data, as well as the selection and registration of datasets to ensure that the appropriate data is used for model development.

Graphical display of screen with lots of data and information used for continuous training.

Continuous training and deployment

Continuous training and deployment refer to ongoing learning based on schedules, new data, or changes in data to ensure that machine learning models remain up-to-date. In addition, the versioning of data, code, and models guarantees reproducibility. Managing the model lifecycle is also a crucial aspect, ensuring the effective management and updating of models. Finally, continuously trained models are automatically deployed in the production environment to speed up the process.

ITK Engineering – Your development partner for MLOps

Implementation skills

Thanks to our experience from various MLOps projects in fields such as machine learning, data management, continuous training and deployment, we provide methodologies and tools to implement customized MLOps applications. Benefit from our extensive range of tools which includes Microsoft Azure, Voxel FiftyOne, databricks, python, mlflow, TensorFlow, CVAT, Kognic, kubeflow, CosmosDB, MongoDB, OracleDB, Airflow, PySpark, hadoop, docker, kubernetes, argo, Jenkins and Jmeter.

Data security and compliance

In the context of data-driven development, protecting the data that you entrust to us is our highest priority. In addition, compliance with regulations governing AI system development is essential. We guarantee data security and compliance for your MLOps solution.

Tailored solutions

As a development partner in the fields of mobility, medical technology, and industry, we are aware that appropriate machine learning and data strategies are as diverse as our clients. Whether on-premises clusters or cloud platforms – we work with you to develop the right MLOps solution.

A look at our reference projects:

Sensors4Rail: Development of a Machine Learning Operations pipeline

MLOps enablement as an extension of existing data engineering pipelines

The challenge
Existing data engineering pipelines primarily automate classical data transformations in order to store processed data in a structured and efficient manner and make it available within organizations. One key challenge when it comes to developing efficient and cost-saving data science applications is optimizing and connecting these pipelines in such a way that they can also be extended for automated and real-time model training and predictions. For our client, the focus was on optimizing data pipelines across a wide range of domains, specifically from existing ETL (Extract-Transform-Load principle) pipelines to their use in data-driven decision-making processes, using ML algorithms and continuous integration by means of MLOps processes.

Solution
The developed MLOps components are implemented within the existing tool landscape on a private cloud environment. Primarily, the client’s local infrastructure was utilized to efficiently and scalably deploy existing AI models while also enabling the training of new models on a locally rolled-out platform. In addition to training, the generated models are also registered and deployed in the local cloud so that they can be used by various teams within the organization via interfaces. The models provided are actively monitored by MLOps frameworks (model monitoring), and automatically retrained in the future in the event of possible data drifts.

Added customer value
By directly integrating with the client’s existing infrastructure, the utilized data and generated models remain entirely within the client’s non-public data organization at all times. Automated provisioning of AI models makes the added value of the client’s existing data science activities available throughout its entire organization, improving efficiency through directly available predictions. This also increases awareness within the various departments, making the benefits and conscious use of AI-generated predictions more visible. Ultimately, the work of data science activities has been made significantly more transparent and scalable, as models and data transformations are made available on centralized infrastructure and can therefore be automated for broader use.

Key Takeaways

Increased productivity

Lower costs for ML applications

Shorter development cycles

Unsolved challenges? We look forward to your inquiry.

Expertise – Date Engineering & Artificial Intelligence

Stefan Held

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An ITK development engineer sits in front of a laptop with an external monitor and uses data-driven solutions in a customer project.