# dida.do > dida.do (dida Datenschmiede GmbH) is a Berlin-based company specializing in applied machine learning & AI. They provide consulting, research, prototyping, deployment (MLOps), and education. Expertise spans computer vision, NLP/LLMs, remote sensing, and domain-specific solutions. Important notes: - dida bridges ML research and production with strong academic roots and real-world delivery. - Operates across public and private sectors; publishes research, blogs, demos, and runs an annual conference. - Offers curated learning formats (e.g., Tech Lunch) and open-source software. --- ## Core Resources - [Home](https://dida.do) - [Services](https://dida.do) – ML Solutions, ML Consulting, ML Research, ML Operations - [What is MLOps](https://dida.do/machine-learning-operations/what-is-mlops) - [Projects](https://dida.do/projects) - [About Us](https://dida.do/about-us) - [Company News](https://dida.do/news) - [Book a Tech Lunch](https://dida.do/tech-lunch) - [dida conference](https://dida.do/conferences) - [Jobs](https://dida.jobs.personio.de/?language=en) - [Industry Use Cases](https://dida.do/industry-use-cases-of-machine-learning) - [Public Sector](https://dida.do/public-sector) - [Blog](https://dida.do/blog) - [Recorded Talks](https://dida.do/recorded-talks) - [Publications](https://dida.do/publications) - [Demos](https://dida.do/demos) - [Topics](https://dida.do/demos) - [Knowledge Base](https://dida.do/knowledge/overview) - [Software](https://dida.do/software) --- ## Home - [Home](https://dida.do) – main overview of dida as a provider of tailor-made machine learning software, highlighting core services, key sectors, representative projects, client testimonials, and the latest news and blog posts. Use this as the starting point for a general understanding of dida. --- ## Services dida provides a broad spectrum of machine learning services, including: - [Machine Learning Solutions](https://dida.do/machine-learning-solutions) – development of versatile ML software that integrates seamlessly into existing IT infrastructures - [Machine Learning Consulting](https://dida.do/machine-learning-consulting) – expert guidance, workshops, and strategy support for ML adoption - [Machine Learning Operations](https://dida.do/machine-learning-operations) – scalable deployment, monitoring, and optimization of ML systems - [Machine Learning Research](https://dida.do/research) – scientific publications and collaborations with academic and industry partners ### Machine Learning Solutions [Machine Learning Solutions](https://dida.do/machine-learning-solutions) – At dida, an interdisciplinary team of machine learning experts develops versatile ML software that integrates seamlessly into existing IT infrastructure. Their tailor-made solutions cover a broad range of applications across many industries and major fields of machine learning, with a clear focus on production-ready systems rather than one-off prototypes. Key aspects: - Customized AI software – solutions tailored to the specific data, processes, and constraints of each client. - Team of experts – interdisciplinary team with strong backgrounds in mathematics, computer science, and software engineering. - Focus on production software – emphasis on robustness, maintainability, and long-term operation rather than one-off prototypes. Development process: 1. Setting main objectives – clarifying goals, requirements, and success criteria with the client. 2. Feasibility study – evaluating data, constraints, and solution approaches to assess the viability of an ML solution. 3. Development of production software – designing, implementing, and validating the ML system and its surrounding software. 4. Maintenance and additional features – ongoing monitoring, improvements, and feature extensions for the deployed solution. ### Machine Learning Consulting [Machine Learning Consulting](https://dida.do/machine-learning-consulting) – dida helps organisations identify AI potentials and teaches teams how to advance process automation with machine learning, focusing on concrete economic value and sustainable digitalization of internal processes. Key aspects: - Identify AI potentials – drawing on experience from many industries to uncover where ML can add value beyond classical approaches, together with your (ideally technical) key employees. - Roadmap to smart digitalization – analysing internal processes and defining where ML initiatives make sense, then developing AI strategies with management and IT for long-term, sustainable process automation. - Workshops and coaching – tailored workshops and training formats for technical and non-technical staff, including dedicated support for existing data science teams. - Concrete next steps – definition of actionable follow-up steps that can be implemented either by the client or in a subsequent machine learning project with dida. Workshops: - The page includes a list of workshops and coaching formats that introduce ML concepts, showcase relevant use cases, and prepare teams for practical AI projects. ### Machine Learning Operations [Machine Learning Operations](https://dida.do/machine-learning-operations) – dida builds robust, flexible, and automated machine learning systems, covering the full lifecycle from development and training to production using MLOps practices. MLOps combines modern software engineering and data engineering methods to make ML products scalable, reproducible, and reliable across their entire lifecycle. Key aspects: - Reproducible experimental phases and model development – setting up tooling and workflows so experiments, training runs, and results can be shared and reproduced across teams, reducing future technical debt and improving maintainability. - Large-scale training, monitoring, and validation of models – establishing ML pipelines for efficient large-scale training, continuous monitoring of model validity, run tracking, diagnosis via metrics, and transparent hyperparameter optimisation. - Secure data management and data protection – enabling fast, secure, and flexible access to large volumes of data while aligning storage and processing solutions with data protection policies (e.g. GDPR). - Automated model life cycles and CI/CD – integrating MLOps tools into CI/CD pipelines to automate the steps from model training to evaluation and deployment, minimising manual work and speeding up the path from development to production. Further resources: - [What is MLOps?](https://dida.do/machine-learning-operations/what-is-mlops) – conceptual introduction to MLOps and typical challenges in ML production. - The page also lists concrete MLOps services, including support for experiment tracking, model management, data pipelines, and infrastructure automation. ### Machine Learning Research [Machine Learning Research](https://dida.do/research) – dida advances novel machine learning techniques and bridges the gap between research and industrial applications. In a fast-moving field, the team keeps up with the latest scientific developments to solve challenging real-world problems and feeds these insights back into client projects. Key aspects: - Scientific publications – in-house ML research published at renowned AI conferences such as NeurIPS and ICML, covering both methodological and applied topics. - Cross-institutional collaboration – joint projects with universities and research institutes to connect recent theoretical AI concepts with practical industrial use cases. - A team of machine learning scientists – scientifically trained employees who actively follow current research through internal seminars, reading groups, and ongoing paper discussions. - Research partners – collaborations with a network of academic and industry partners to strengthen knowledge transfer between research and application. --- ## Projects dida has completed numerous AI projects over the past years, with a strong focus on natural language processing and computer vision. Below is a selection of representative work: - [Projects](https://dida.do/projects) – detailed case studies and applications across industries --- ## Company ### About dida is an interdisciplinary team with a strong background in both theoretical research and practical machine learning applications. - [About](https://dida.do/about-us) – team, values, and company profile ### News Regular updates on events, collaborations, publications, and company activities: - [News](https://dida.do/news) ### Tech Lunch Free, customizable 45–60 minute sessions led by dida experts. Topics include: - Machine learning foundations and workflow automation - NLP and information extraction (including OCR) - Computer vision (e.g., defect detection, remote sensing) - [Tech Lunch](https://dida.do/tech-lunch) – learn more or book a session ### dida conference Annual Berlin event focusing on applied machine learning, including talks, workshops, and recordings. - Archives available: 2023, 2024, 2025 - [dida conference](https://dida.do/conferences) ### Jobs Open positions and career opportunities at dida: - [Jobs](https://dida.jobs.personio.de/?language=en) --- ## Sectors ### Industry Use Cases Key sectors & applications: - Agriculture (remote sensing, crop monitoring) - Back Office (document automation) - Banking (fraud/risk) - Commerce (forecasting, recommendations) - Healthcare & Pharma (diagnostics, compliance) - Insurance (claims, fraud) - Manufacturing & Automotive (quality control, predictive maintenance) - Meteorology (forecasting) - Mining (extraction monitoring) - Transportation & Logistics (routing, supply chain) - Utilities (infrastructure & energy optimization) - Details: [Industry Use Cases](https://dida.do/industry-use-cases-of-machine-learning) ### Public Sector - Overview: secure, compliant, transparent AI tailored to government needs; flexible deployments (on-prem or cloud) and modular integrations. - Selected clients: BMWK, BMEL, BMBF, BMUV, MWIKE NRW, d-NRW AöR, ESA, Deutsche Bahn, Deutscher Wetterdienst. - Reference work areas: Geo-enabled resource mgmt, flight safety, governance, digital citizen services, responsive archives, urban development monitoring, smart infrastructure, land-use analysis, animal monitoring. - More: [Public Sector page](https://dida.do/public-sector) --- ## Blog Recent & representative articles: - “Popular methods of Explainable AI (XAI)” — Aug 18, 2025 - “Setting Up a Secure Python Sandbox for LLM Agents” — Jun 16, 2025 - “Patching uploaded files for usage in FastAPI background tasks” — May 1, 2025 - “Rapid Json Loading” — Apr 24, 2025 - “Detecting illegal mines from space” — Sep 1, 2020 - Explore: [Blog](https://dida.do/blog) --- ## Resources Publications, recorded talks, demos, topics, knowledge base, and open-source: - [Recorded Talks](https://dida.do/recorded-talks) - videos from events and lectures - [Publications](https://dida.do/publications) - scientific papers and articles - [Demos](https://dida.do/demos) - interactive machine learning demos - [Topics](https://dida.do/topics) - AI/ML focus areas - [Knowledge Base](https://dida.do/knowledge/overview) - knowledge - [Software](https://dida.do/software) - ML tools --- ## Optional - Topics: [Focus areas](https://dida.do/topics) - Memberships: KI Bundesverband e.V., Bitkom e.V., GovTech Campus - Legal & Contact: [Imprint](https://dida.do/imprint) • [Privacy Policy](https://dida.do/privacy-policy) • Contact form on site