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ML development from Berlin

Machine Learning Development

Machine learning development turns your data into models that make predictions, recognise patterns and prepare decisions. Context Studios, an AI-native development studio in Berlin, builds classification, regression, time series forecasting and computer vision with PyTorch, scikit-learn and MLOps, from the first data check to a monitored model in production.

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AI-native development studio from BerlinTensorFlow · PyTorch · scikit-learnHugging Face · MLflow · Weights & BiasesEnterprise ML pipelines
  1. Workshop
  2. Setup
  3. Sprint
  4. Build & Support

Fixed price after scoping · proposal within 48 h

Last updated:

(01)

What is machine learning development?

AI technology

Machine learning development covers the full lifecycle of data-driven models, from data analysis through feature engineering and training to operation in production. The models recognise patterns in historical data and derive predictions from them. Context Studios relies on MLOps, reproducible pipelines and explainable models throughout.

Specialisation
Supervised/unsupervised learning, deep learning, MLOps
Technologies
PyTorch, TensorFlow, scikit-learn, XGBoost, MLflow
Target audience
Companies with relevant data and a need for optimisation
Typical project duration
Typically 6–20 weeks including data preparation
Compliance
GDPR, AI Act explainability, model governance

AI predictive analyticsAI data pipeline developmentComputer vision developmentAI data analyticsLLM fine-tuning

(02)

Which ML services do we offer?

End-to-end ML development from Context Studios in Berlin

(01)

Data analysis & feature engineering (pandas, scikit-learn)

Every ML project starts with exploratory data analysis: we check the quality, completeness and informative value of your data and engineer features with pandas, NumPy and Jupyter notebooks. scikit-learn pipelines and automated feature selection make sure the model works with the relevant attributes.

(02)

Model development (TensorFlow, PyTorch, scikit-learn)

Classification, regression and clustering: Context Studios trains models with TensorFlow, PyTorch and scikit-learn, including hyperparameter optimisation with Optuna and Ray Tune.

(03)

MLOps & deployment (MLflow, Weights & Biases)

Models in production: Context Studios deploys models with MLflow, Weights & Biases and Docker, with CI/CD pipelines, automated retraining and A/B testing on AWS and Vercel.

(04)

Time series analysis (Prophet, PyTorch LSTM)

Time series analysis: Context Studios builds forecasting models with Prophet, ARIMA and LSTM networks in PyTorch, for revenue forecasts, demand planning and financial market analysis.

(05)

Anomaly detection (Isolation Forest, autoencoders)

Context Studios builds anomaly detection with Isolation Forest, autoencoders and one-class SVM, for fraud detection, quality control and IT security, using TensorFlow and scikit-learn.

(06)

Explainable AI / XAI (SHAP, LIME)

Context Studios implements explainable AI with SHAP, LIME and Integrated Gradients: ML models with transparent decisions, for GDPR compliance and regulated industries.

(03)

How does an ML project work?

  1. (01)

    Initial consultation

    Free 30-minute initial video call. We understand your question and your data situation and give you a first assessment of whether the problem can sensibly be solved with machine learning.

    Day 1
  2. (02)

    Data check & proposal

    We review a sample of your data. You receive a written proposal with scope, timeline, success criteria and a fixed price.

    Days 2–3
  3. (03)

    Model development

    Data preparation, feature engineering and training with weekly progress reviews. Goal: a first validated model in about 4 weeks, depending on data and scope.

    From week 1
  4. (04)

    Launch & support

    Deployment with monitoring, drift detection and documentation. After that: 30 days of free bug fixing from final delivery, with ongoing operation by agreement.

    After development

Frequently asked questions about ML projects

(01)How much data do we need for an ML project?
It depends on the problem and the data quality. As a rough guide, models on tabular data often work with a few thousand clean records, while deep learning usually needs considerably more examples. With small datasets, transfer learning, pre-trained models and data augmentation help. Whether your data is sufficient is something we clarify in the data check before the proposal.
(02)What is the difference between machine learning and deep learning?
Machine learning covers all methods that learn from data, from linear regression to random forests. Deep learning is a subset based on neural networks that shows its strengths above all with unstructured data such as images, text and audio. For structured tabular data, classical methods are often just as good, faster and easier to explain.
(03)What does a machine learning project cost?
Costs depend on the data situation, model complexity, integrations and operating requirements such as real-time inference or regular retraining. That is why we calculate ML projects individually. After a short data check you receive a binding proposal with a clearly described scope: fixed price after scoping, proposal within 48 hours.
(04)How do you make sure an ML model is fair and unbiased?
We check the training data for bias, test models with fairness metrics across different groups and apply debiasing techniques where needed. Explainable AI makes the decision factors visible so that discriminatory patterns can be detected and corrected. We document the results so that your business units and your data protection officer can follow them.
(05)How long does an ML model stay up to date?
That depends on how quickly your data changes. We therefore set up automatic drift detection, which reports when input data or prediction quality deviate, and retraining pipelines that update the model in a controlled way. Models in fast-moving areas such as finance often need more frequent updates, while models for stable industrial processes usually last longer.
(06)Can existing databases and data warehouses be used?
Yes, we connect ML pipelines to your existing data sources, such as PostgreSQL, BigQuery, Snowflake, Redshift, S3 data lakes or on-premise databases. Feature stores unify data access and ensure that training and inference use the same transformations. This way you avoid discrepancies between development and production.
(07)What is MLOps and why does it matter?
MLOps applies the principles of DevOps to machine learning: automated pipelines for data preparation, training, deployment and monitoring, plus versioning of data and models. Without MLOps, models go stale unnoticed, results cannot be reproduced and every update becomes a project of its own. With MLOps, a model in production stays traceable and maintainable.
(08)Can you work with our data scientists?
Of course. We often work hand in hand with in-house data science teams, whether as reinforcement for specific tasks, with MLOps experience or during the transition from a notebook prototype to a production system. Knowledge transfer and enabling your team are an integral part of our projects, so you can develop the models further yourself later.
(09)How explainable are the ML models?
We use explainable models by default: SHAP values show the influence of each feature, LIME explains individual predictions and feature importance rankings give an overview. For regulated industries such as finance or healthcare, we choose interpretable models and document them so that transparency requirements from the GDPR and the AI Act are addressed.
(10)Which language are the models developed in?
Mainly in Python with the established ML ecosystem: PyTorch, scikit-learn and pandas. For particularly fast inference, we export models to ONNX. Integration into your applications happens via REST APIs with FastAPI or gRPC endpoints, regardless of the language your other systems are written in.
(04)

Which stack do we use to build ML models?

(01)

AI & ML

Anthropic ClaudeOpenAI GPTGoogle GeminiOpen-Source LLMs (Llama, Qwen, DeepSeek, Mistral)ConvexRAG & Vector DBs (Pinecone, Weaviate)MCP (Model Context Protocol)Hugging Face TransformersComputer Vision (YOLO, SAM)ElevenLabs (Voice AI)Google Veo (Video AI)
(02)

Web & Mobile

Next.js & ReactTypeScriptReact Native & ExpoTailwind CSSShadcn/uiVercel Edge Runtime
(03)

Backend & Data

Node.js & Hono (Edge)PythonPostgreSQL & SupabaseConvex (Real-Time DB)RedistRPC & GraphQLOpenAPI
(04)

DevOps & Infrastructure

Vercel & AWSDocker & KubernetesCI/CD-Pipelines (GitHub Actions)OpenTelemetry & GrafanaLangfuse (LLM Monitoring)
(05)

In which industries do we use machine learning?

Financial services

ML for financial services: credit scoring, fraud detection and risk assessment with models that analyse transaction patterns and flag suspicious activity for review.

Manufacturing & Industry 4.0

ML for manufacturing and Industry 4.0: predictive maintenance, quality forecasting and process optimisation with ML models that analyse sensor data and predict failures.

Healthcare

Diagnostic support, drug response prediction and patient risk stratification with ML models trained on clinical data that support doctors in their decisions.

Energy & utilities

Load forecasting, grid optimisation and failure prediction for energy suppliers: ML models forecast consumption patterns and support the control of energy distribution.

Retail & e-commerce

ML for retail and e-commerce: demand forecasting, dynamic pricing and customer segmentation, from inventory optimisation to personalised product recommendations.

Telecommunications

Churn prediction, network anomaly detection and customer lifetime value modelling: ML identifies customers at risk of leaving and helps target retention measures.

(06)

Project examples

Examples we can build

Retail

Demand forecasting for retail

An example we can build: a forecasting model that predicts demand per item and store from sales data, seasonality and promotions and prepares order suggestions.

Forecast per item · Order suggestions · Automatic retraining
Manufacturing

Anomaly detection in sensor data

An example we can build: a model that continuously analyses machine sensor data, detects deviations from normal behaviour and alerts maintenance at an early stage.

Early warnings · Explainable alerts · Dashboard integration
Telecommunications

Churn model with explanations

An example we can build: a classification model that identifies customers at risk of churning and uses SHAP to show which factors drive the risk.

Risk score per customer · Explained factors · CRM integration
(07)

Machine learning development: consulting in Berlin

Founder AI-native since
2024
Email
info [at] contextstudios [dot] ai

Data-driven decisions with machine learning

Start your ML project with a free 30-minute initial call or write to [email protected]. Together we check which predictions your data can support.