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The advantages of automation, AI, and machine learning have caused a shift toward smart factories and data-driven manufacturers. We help you to become competitive, innovative, and resilient.
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How to navigate intense competition, stay in compliance with ever-changing regulations and leverage commercial opportunities. We help you create an IT landscape that enables efficient operations and connects processes and data.
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People-centered, client-focused and project-driven: that’s professional services in a nutshell. We design and implement technology solutions that enable better decision-making and effective working.
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We modernize the retail workplace, bringing previously unseen insights to the business, implementing intelligent order management and get you closer to your customer.
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HSO helps public and government organizations to meet and leverage increasing expectations for digital service, responsiveness and transparency.
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We support Non-Profits by modernizing applications, automating processes, and providing insight. Through technology, we nourish successful relationship management and enable greater impact.
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HSO is one of the world’s top business solution and implementation partners, large enough to serve, small enough to care. Through our services, we help companies transform the way they work and improve business performance.
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Microsoft Dynamics 365 Business Applications for ERP and CRM are leading in the industry. But their real strength is in the integration. Combined, they deliver even more value.
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Join HSO
Our workforce is a growing dynamic, unique, and diverse global community that is quality and performance-driven. For every stage in your career, we offer challenging opportunities. We look forward to meeting you!
Life at HSO
We are a global community of people with an entrepreneurial spirit, in an environment where you can really make a difference and have your voice heard. We constantly challenge ourselves to be better. Come join the journey with us!
Graduate Programs
Skyrocket your career with our graduate programs and accredited certification opportunities at HSO around the world!
HSO Academy
From technical skills, soft skills, mentorships, leadership coaching, masterclasses, informal knowledge sharing and globally recognised certifications - HSO Academy is your personal and professional knowledge accelerator.
Vacancies
Find opportunities in Consultancy & Technology, Project Management, Leadership, Sales and Business Development, Marketing, Legal and HR.
Machine Learning: a platform perspective
Businesses are making increasing use of Machine Learning. Predicting behavior or automating decisions - some examples of Machine Learning - has great value for organizations. Often these projects begin as separate innovation initiatives, but how do you ensure that the models built remain available and accurate? How do you create an overview of which models are in development? More importantly, how do you facilitate Data Scientists so they can work quickly and securely? A mature Machine Learning Platform plays a crucial role in this.
More and more companies are discovering the added value of Machine Learning, and are employing one or more Data Scientists to develop models. A Data Scientist, like any software developer, must be supported with the right tools.
A Machine Learning Platform aims to organize and facilitate the data science process. Machine Learning processes require support in process security, computing power requirements and efficient development to models (version control). We group these terms together under the heading 'organizing'.
'Facilitation' looks primarily at the process. Machine learning has fixed steps that must be supported within the organization, including data acquisition, training and deployment.
Organizing
What is needed in order to organize?
Who has access to what code, models and data? How do we keep this data within a secure environment? Data science tasks performed on not (fully) managed work environments, such as local laptops, pose a major threat in terms of loss of personal or privacy-sensitive data. A Machine Learning Platform facilitates a secure working environment that shields data from unwanted access.
Computing power is needed for two purposes: for training models and for hosting models. The requirements for the two environments are often different. A training environment is used for model learning. This process is characterized by a period of intensive computing power that can often be parallelized. The training process can then be split up and run simultaneously on multiple computing cores.
Hosting models involves making the trained model available so that it is ready for use. There are two categories in this:
A data science process is iterative. It is not a linear process in which you plan the details in advance and then work them out. It is, however, a process of discovery, adjustment, adaptation and testing. Version tracking is essential in this context, and this tracking is done in two areas:
Facilitate
Now that we've seen how a Machine Learning Platform organizes, it's time to look at how the data science process is facilitated. What phases should be supported?
Correct data is essential for a well-functioning model. Data acquisition includes obtaining and making this data available for Machine Learning purposes. A Machine Learning Platform provides an enabling role in this. Datasets must be discoverable and of good quality to be used directly in Machine Learning processes. In addition, a data platform, the role of Data Engineers and good cooperation between different disciplines are also important in this process.
Model Training is the central step in the Machine Learning process. This step results in a model that can be validated, tested and published. To carry this out, a Data Scientist needs a wide range of tools and frameworks. These can include development environments in Python or R and deep learning frameworks such as PyTorch. A Machine Learning Platform provides this environment. These tools and frameworks can be offered in a GUI, in a managed environment or in a linked, proprietary environment.
In practice, it becomes clear that Data Scientists prefer environments that can be set up flexibly, with the open source tools and frameworks to perform Model Training. In doing so, the platform enables the model training process to be recorded and monitored.
Once a model has been trained and validated, the time has come to put it into action. This results in a model that can be used repeatedly. The computational step required for repetitive deployment differs for Batch Inferencing and Real-Time Inferencing.
Both environments require a process to track versions and roll out new versions. A Machine Learning Platform helps track and automate this process so that new versions can be delivered at the push of a button. After deployment, the deployed versions can be monitored for performance (is the model online) and for model-fit (do the results match the truth). Both values can be a reason to make adjustments. This makes the above steps an iterative and repetitive process.
"With a Machine Learning Platform, the power of your Data Scientists and Engineers is fully leveraged."
Without using such a platform, your organization runs risks in the area of data security and the transparency of your Machine Learning processes.
Wondering about the added value of Machine Learning for your organization, or what innovative solutions data science can offer you? We will be happy to help you!
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From our Data, Machine Learning and AI experts
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