What can Machine Learning Architects Basel do for you?
With 10+ years experience in managing highly reliable software and infrastructure projects our team has acquired the conceptual and practical know-how that any organisation aiming towards digital transformation should consider. Together with expert Data Scientists and Machine Learning Engineers we are forming a polyvalent team with a mix of highly experienced consultants, project managers and innovative engineers from different backgrounds to extend our successful concepts of a “Digital Highway for Continuous Software Delivery” and “Effective Site Reliability Engineering (SRE)” to a “Digital Highway for Machine Learning” and “Effective MLOps”.
Are you wondering how to unlock your data and operationalise machine learning to create value for your business?
We can help you address the following challenges:
- Lack of an agile AI ecosystem and enterprise readiness
- Scattered IT environments where data is spread out over multiple systems and architectures and not prepared for AI
- Teams working in silos
- BI is limited to fraction of data
- On-premise infrastructures not meeting scalability and flexibility needs
- Building ML is hard, operationalising ML is even harder
With our Unified Analytics and Machine Learning approach we can help you design and build an end-to-end data analytics and machine learning lifecycle architecture as a starting point for efficient tooling chains and collaborative Data Science and Engineering work spaces. Furthermore, Machine Learning (ML) systems have a tendency to incur technical debt and get unmanageable, while collaboration between different roles and teams seems challenging to implement sometimes. We help our customers address these challenges. By referring to “MLOps” as engineering culture and practice to unify ML system development and operations we help you enhance collaboration between data scientists, ML engineers, software developers, and other IT teams to manage the end-to-end ML lifecycle. In this regard, we design and build robust, repeatable ML model, data and code pipelines, along with streamlining the way our customers can efficiently develop, test, deploy, monitor, audit, track versions, and reproduce their ML systems. We call that the “Digital Highway for Machine Learning”.
In line with the Swiss Digital Network’s approach to consider operating model, technologies as well as culture and skills for a successful Digital Transformation, our consulting, engineering and training offers around the Digital Highway for Machine Learning cover all these three pillars for our customers to effectively and sustainably leverage all the investments and work they are putting into Analytics, Data Science and Machine Learning initiatives.
A TRANSVERSAL OFFER TO SUIT ALL YOUR NEEDS
MLOps Operating Models, Architecture Designs & Engineering Expertise
- Maturity assessments and roadmaps to manage governance, processes and tools
- MLOps and self-service analytics architecture designs for end-to-end ML system development (Dev) and ML system operation (Ops)
Engineering expertise to build Analytics, Data Science and MLOps workspaces and tool chains
- Continuous delivery, observability and model performance management support and guidance to leverage DevOps and Site Reliability Engineering (SRE) principles for Machine Learning
Data Science, Machine Learning, Self-Service & Real-time Analytics Technologies for MLOps
- Customer specific and adapted technology evaluations and roll-outs of:
- Effective MLOps for end-to-end ML system development (Dev) and ML system operation (Ops)
Continuous Delivery tooling
Self-service and real-time analytics for data insights and operations monitoring
- Objective and up-to-date benchmarks of both established market solutions (e.g. Gartner Magic Quadrant for Data Science and Machine Learning) and new, innovative (next generation) tools
Data Science & SRE Trainings for Effective MLOps
- Domain specific offerings of comprehensive, local and cost-effective Data Science trainings for:
- Data Science for Life Sciences trainings
- Further domains and customisable enterprise offerings (on our 2022 roadmap)
- Site Reliability Engineering (SRE) trainings by industry practitioners to build end-to-end MLOps skills:
- Public bootcamps
- Enterprise trainings
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Engineering – Architecture – Consulting – Implementation – Training