Support Vector Machine Experts in Munich
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Meet FRATCH Experts in Munich, who have recently used Support Vector Machine
Philipp G.
Last position:
Data Scientist & ML Engineer at Data-Science Factory GmbH
- Building, implementing and selling automated Data Science solutions such as Scorecard Factory and Forecast Factory
- Implementation of automated end-to-end cloud processes
- Development of LLM and NLP models
- Creation of interactive reports
- Support for national and international large corporations as well as medium-sized companies in implementing ML projects
Serge K.
Last position:
Data Scientist & MLOps (Machine Learning Operations) at REWE Digital GmbH
- It is like a startup within REWE, where we have to build a new forecasting system on Google Cloud Platform from the scratch. Although, officially my role is called MLOps, my actual tasks also include development of data processing pipelines (data engineering) and data scientists tasks such as feature engineering and model trainings.
- GCP: Terraform (tofu), Vertex AI (Kubeflow), Cloud Run, IAM, Google Cloud Storage, BigQuery, Artifact Registry
- Data engineering: Snowflake as the main data warehouse, Terraform, DBT for data model implementations
- CI/CD: GitLab. We have built a CI/CD pipeline that automates deployments of new releases up to production environment
- Development of feature store and data synchronization between environments
- Data science: evaluation of different forecasting models, including CatBoost, Contextual Gamma and Random Forest; implementation of training pipelines using Kubeflow on Vertex AI; scaling and development of an event-driven interface using Kafka as a forecasting service; evaluation of MLFlow for model tracking and model registry
- Implementation of REST API using KeyCloak OAuth2 and OpenID (JWT)
- Monitoring: implementation of dashboards with Grafana, Prometheus, Mimir, Streamlit and Loki to monitor prediction metrics, GCP and Snowflake costs for pipelines, and system metrics
- Development of Copilot Agent Skills
Martin R.
Last position:
Senior LLM Research Scientist at BYO Inc.
- Research and develop models for chatbots, NLP and LLMs (e.g. Llama, Qwen, OpenAI)
- Enhance chatbots with RAG, in-context learning
- Supervised fine-tuning (PEFT, LoRA), Huggingface or Unsloth
- Advanced training methods: Test-time training, (transductive) active learning, reinforcement learning
- High-throughput serving with vLLM
- Apply embedding models (e.g. SentenceTransformers), similarity/vector search or vector DB or ranking (e.g. LlamaIndex, Faiss, LangChain)
- Generate and filter synthetic data, clustering
- Detect hallucinations
- Evaluate chatbot models (Rouge, BLEU, F1-Score, Recall, Precision)
- Visualization of experiments (matplotlib)
Anton K.
Last position:
Head of Overall Technical Integration NSC / Hadoop Cloud Development at IABG
Head of overall technical integration NSC (National Secure Cloud, project with approx. 60 employees).
Technical integration of all subprojects into one product, definition of interfaces and basic components of a cloud including hardware, technical architecture of the IABG platform.
Development of a Cloud Management Platform (CMP) capable of creating private/mixed clouds of any complexity based on a textual description with one click or interactively.
CMP also includes the complete hardware management lifecycle.
Kubernetes, OpenStack and Hadoop are used as the foundation.
The management layer includes Harbor, Gitea, Longhorn, Keycloak, Rancher and Jenkins, which are configured automatically.
Private cloud can run any customer workloads, including a full Hadoop layer with HDFS, Spark, MapReduce, Mesos, HBase and around 20 additional ML/DL technologies.
Hadoop worker clusters can also be installed automatically without Kubernetes on bare metal or commodity hardware.
OpenStack with Nova, Neutron, Ironic, Swift, Cinder, Ceph.
Development of a Java application Rudi: SOAP, REST, containers, DB.
Technologies: Kubernetes (K3s, Rke2, Minikube, Harbor, Gitea, Jenkins, Longhorn, Keycloak, Rancher), OpenStack (Nova, Neutron, Keystone, Swift, Ceph, Cinder, Sahara, Magnum, Kayobe, Kolla, Bigrost, Ironic), Hadoop (HDFS, Ambari, Solr, Livy, Ranger, YARN, Tez, HBase, Kafka, Hive, Zookeeper, MapReduce, Spark, Oozie, Flink), virtualization (Kubernetes (K3S), VMware, Oracle), scripting (Ansible, Puppet, Juju, Shell, Groovy, Gradle, Maven).
Marco F.
Last position:
System Engineer–Vmware, Veeam at DCSO
- About 800 different server VMs, operation and lifecycle/asset management of about 40 ESXi hosts (Lenovo)
- Upgrade of all clusters to ESXi/VCSA 7.0U3 and 8. Planning patch management for all hosts
- Cleaning up the AD – many different domains that all needed to be migrated into the main environment. Planning/execution
- Veeam – check the configuration and migrate to a newer, partly virtual infrastructure to make the setup leaner and faster
- VMware – upgrades/updates, general maintenance
- Proxmox POC – evaluate if an alternative to VMware
- Documentation for Veeam/VMware/storage
- Implementation of the uniflow cloud solution
- NetApp migration to new storage, CIFS maintenance and administration of SVMs
Daniel C.
Last position:
Founder & Managing Director at BotCraft GmbH
- Building the company with a focus on connectivity for IIoT and Industry 4.0, iRPA/process automation, advanced robotics and smart systems, sensors and services
- Project management and software architecture for IoT gateway development (since 2020) with protocol translation, IT/OT convergence and GRC
- Developing RPA bots for automating and monitoring industrial processes with an agent-based AI approach (since 2020)
- Implementing unsupervised clustering and anomaly detection for time series data in big data streaming pipelines (since 2021)
- Introducing a Docker-based release train for OTA updates with DevSecOps and CI/CD (since 2018)
Robert H.
Last position:
Software Developer at Open Mind Technologies AG
- Software development in geometry/CAM using C++
Discover over 15,000 top freelancers
Statistics of experts using Support Vector Machine
Aggregated from the professional profiles of matched freelancers.
Experience
25 years
Position duration
2.5 years
Positions per freelancer
17
Top business areas
Information Technology, Product Development, Research and Development
Top industries
Information Technology, Education, Healthcare
Certification focus areas
Information Technology, Business Intelligence, Legal
Bachelor's degree or higher
86%
Master's degree or higher
86%
Doctorate
57%
Certifications per freelancer
1
Most common languages
German, English, French
Speak two or more languages
100%
Based on our profile pool as of 29 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology in Munich are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows the share of experts charging within that range.
Average rates of experts in Munich using Support Vector Machine
Rates are based on recent contracts and do not include FRATCH margin.
The average daily rate is the mean of all daily rates from recent contracts of comparable freelancers on our platform.
The median daily rate is the middle value of all daily rates — half of comparable freelancers charge less, half charge more. Unlike the average, it is barely affected by outliers.
Calculated based on our freelancers’ daily rates as of 29 Oct 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Support Vector Machine experts industry focus
See which industries our matched freelancers work in most often — every figure is calculated live from the freelancers on FRATCH.
- Information Technology (100%)
- Education (86%)
- Healthcare (57%)
- Manufacturing (57%)
- Automotive (43%)
- Energy (43%)
- Banking and Finance (43%)
- Aerospace and Defense (29%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What it does
Support Vector Machine is a supervised learning method used to classify data, predict continuous values and detect unusual observations. It separates classes by finding a decision boundary with the strongest possible margin. With kernel functions, it can model complex relationships without manually transforming every feature.
Typical applications
Support Vector Machine appears in projects where structured data, clear evaluation and dependable generalisation matter:
- Text classification and document routing
- Image and signal recognition
- Customer or transaction anomaly detection
- Regression for scientific and operational data
- Biological and medical pattern analysis
Ecosystem and tooling
Most projects use Python with scikit-learn, NumPy, pandas and joblib. Specialists may also work with LIBSVM, R packages, MATLAB or Spark integrations when data workflows require them. Core tasks include feature scaling, kernel selection, hyperparameter tuning, cross-validation, model persistence and reproducible pipelines.
When to bring in expertise
Companies often seek freelance expertise when a proof of concept must become a tested service, when model quality has stalled, or when an existing pipeline produces inconsistent results. This is especially relevant for Munich organisations handling industrial signals, research data, financial records or multilingual documents. Remote delivery works well when data access, documentation and review routines are clearly defined.
Delivery and integration
A strong professional connects the model to the wider data system rather than treating it as an isolated experiment. Typical deliverables include a cleaned training dataset, a reproducible preprocessing pipeline, evaluation reports, a versioned model and an inference interface. They also document class imbalance, decision thresholds, support vectors, latency and retraining needs.
What strong specialists bring
Look for professionals who can explain why Support Vector Machine fits the data and when another method would be safer. They compare it with tree-based models, logistic regression and neural networks using suitable validation rather than a single score. Strong specialists protect against leakage, monitor drift, test edge cases and communicate trade-offs to both technical and business teams.
Frequently asked questions
Before you brief your next project: the most common questions about Support Vector Machine.
Support Vector Machine is used for supervised classification, regression and anomaly detection. It is effective for high-dimensional structured data, text features and datasets where a carefully defined boundary can generalise well.
Support Vector Machine can perform well with scaled, high-dimensional data and relatively limited training samples. Tree-based models often require less feature preparation and can be easier to interpret, so the right choice depends on data shape, noise, explainability and operational constraints.
A strong Support Vector Machine specialist usually works with Python, scikit-learn, pandas and NumPy. Useful adjacent skills include feature engineering, experiment tracking, data quality checks, model packaging, API integration and responsible handling of sensitive data.
The required experience depends on the problem, data quality and deployment expectations. A small classification proof of concept may need focused modelling expertise, while a production system requires stronger skills in validation, monitoring, reproducibility and integration.
Support Vector Machine projects are well suited to remote work when specialists receive secure access to representative data and clear evaluation criteria. Munich-based teams may choose on-site workshops for domain discovery, while modelling, documentation and review can usually happen remotely.
A Support Vector Machine with a kernel is useful when class boundaries are not linear and the dataset is not too large for the chosen training approach. Specialists should compare kernel and linear versions against simpler baselines before committing to the added complexity.
Ask for a clear data split, leakage controls, baseline comparisons and metrics that reflect the business cost of false positives and false negatives. High-quality Support Vector Machine work also includes calibrated thresholds, reproducible training and tests on data that was not used for model selection.
SVM models can be sensitive to feature scale, noisy labels, class imbalance and poorly chosen regularisation or kernel settings. Training can also become impractical as the dataset grows, so professionals should assess linear alternatives, sampling strategies and the cost of retraining.
The average hourly rate of freelancers in Munich, Germany who have used Support Vector Machine in their recent projects is 95 €, which corresponds to a daily rate of about 758 € based on an 8-hour working day.
Of the freelancers in Munich, Germany who have used Support Vector Machine in their recent projects, 86% hold at least a Bachelor's degree, 86% hold at least a Master's degree, and 57% hold a doctorate.
On average, freelancers in Munich, Germany who have used Support Vector Machine in their recent projects have 25 years of professional experience, with a single engagement typically lasting around 2.5 years.
The most common languages among freelancers in Munich, Germany who have used Support Vector Machine in their recent projects are German (100%), English (100%), and French (71%).
The most common industries among freelancers in Munich, Germany who have used Support Vector Machine in their recent projects are Information Technology (100%), Education (86%), and Healthcare (57%).
The most common business areas among freelancers in Munich, Germany who have used Support Vector Machine in their recent projects are Information Technology (100%), Product Development (86%), and Research and Development (86%).
Main locations of FRATCH Experts, who have recently used Support Vector Machine
Our freelancers and interim experts are at home across the DACH region — available on-site in the major business hubs or fully remote. Choose a location to discover matched specialists, local market insights and up-to-date availability.
Countries:
Berlin Hamburg 
Munich Cologne Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg Request a free demo
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Philipp Thomaschewski
FRATCH CEO
