Training course · Lovable · Data Science & Machine Learning
Lovable for Data Scientists and ML Engineers
Build web apps from prompts: take Lovable from idea to a working React app with Supabase backend, GitHub sync and deployment.
- Duration
- 1 day (7 hrs)
- Format
- Online or on-site
- Language
- German or English
- Participants
- open dates or exclusive for your team
- Certificate
- Certificate of attendance
Overview
“Lovable for Data Scientists and ML Engineers” is designed for roles such as Data scientists in business units and analytics teams, ML engineers responsible for training and deployment and Data analysts moving into Python and machine learning and shows step by step how to use Lovable productively, safely and transparently in Data Science & Machine Learning – from the fundamentals to advanced workflows.
Lovable turns plain-language descriptions into complete web applications built on React, TypeScript, Tailwind CSS and Vite. In this one-day course you learn how to phrase requirements so that Lovable produces clean, maintainable code, how to connect a database, authentication and storage through the Supabase integration, and how to steer changes iteratively instead of starting over every time something breaks. The focus is on real workflows: from the first prompt through data model and permission design to a published application running on your own domain.
For developers, Lovable becomes genuinely useful when the generated code does not stay locked inside the tool. You therefore work extensively with the two-way GitHub sync, review the generated code in your own editor, add tests and move the project into your existing toolchain where needed. You also get a clear view of the limits: Row Level Security, Edge Functions, secrets handling, performance and the maintainability of larger codebases. That lets you judge when Lovable is the fastest route and when conventional development or an agentic coding tool is the better fit.
Data science and machine learning thrive on moving quickly between exploration, modelling and production. A great deal of time goes into data preparation with pandas or Polars, feature engineering, visualisation, hyperparameter experiments and finally turning notebook code into maintainable pipelines. AI coding assistants and agents speed up every one of these steps: they write exploration and plotting code, suggest validation strategies, refactor notebooks into tested Python modules, build scikit-learn or PyTorch training scripts and set up experiment tracking with MLflow. Methodological rigour is essential, though, because plausible-looking code can hide data leakage or misleading metrics. This course teaches you to use AI productively while safeguarding the statistical quality of your results.
The course is delivered live – either online in a virtual classroom or on-site in our training rooms in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart and Kaiserslautern. We can also run it exclusively for your team.
Who is this course for?
- Data scientists in business units and analytics teams
- ML engineers responsible for training and deployment
- Data analysts moving into Python and machine learning
- Researchers and developers with notebook-heavy workflows
- MLOps teams running models in production
Typical challenges we address
- Messy notebooks that are hard to reproduce and put into production
- Time-consuming data preparation and feature engineering
- Methodological pitfalls such as data leakage, unsuitable metrics or flawed validation
- Lack of traceability for experiments, models and data versions
What you will be able to do
- You write prompts that lead Lovable to produce well-structured, maintainable React applications.
- You connect Supabase for database, authentication and storage and verify Row Level Security.
- You iterate on the design deliberately without breaking parts of the app that already work.
- You sync projects with GitHub and continue development in your usual coding environment.
- Accelerate exploration, feature engineering and modelling with AI assistants without sacrificing methodological rigour.
- Use AI agents to turn notebook code into tested, reproducible ML pipelines.
- Recognise typical errors in generated ML code, such as data leakage or unsuitable metrics, and correct them deliberately.
Course outline
Module 1 Fundamentals and prompting strategy
- How Lovable works: the generated stack of React, TypeScript, Tailwind CSS and shadcn/ui
- Describing requirements in a structured way: audience, pages, data objects, roles
- Chat mode versus direct edits: when to plan and when to build
- Using the knowledge base and project instructions for consistent output
Module 2 Backend with Supabase
- Connecting a Supabase project and deriving tables from your prompt
- Setting up authentication with email, magic links and OAuth providers
- Understanding, reviewing and tightening Row Level Security policies
- Using Edge Functions, storage buckets and secrets for external APIs
Module 3 Design iteration and UI quality
- Visual editing of individual elements and targeted layout prompts
- Working from screenshots and Figma designs
- Ensuring responsive behaviour, accessibility and design-system consistency
- Debugging with console output and reverting to earlier versions
Module 4 GitHub sync and a professional workflow
- Setting up two-way synchronisation with a GitHub repository
- Continuing work locally in VS Code, Cursor or Claude Code
- Introducing branches, code review and tests for generated code
- Keeping dependencies, environment variables and project structure tidy
Module 5 Deployment, operations and limits
- Publishing with Lovable hosting and a custom domain
- Alternative deployments via Vercel, Netlify or your own infrastructure
- Realistically assessing credit usage, project size and common dead ends
- Security and data-protection review before go-live, including GDPR questions
Module 6 Practice lab: Data Science & Machine Learning
- Exploratory data analysis
- Turning notebooks into pipelines
- Model training and validation
- Experiment tracking and MLOps
- Explaining and auditing models
- Evaluating LLM and RAG prototypes
Module 7 Data protection, quality & transfer to daily work
- Training and analysis data often contains personal information, so use schemas, aggregates or synthetic data in prompts and observe GDPR principles such as purpose limitation. For high-risk applications, the EU AI Act sets requirements on data quality, documentation and transparency, and the licences of datasets, pre-trained models and Python packages must also be checked.
- Ground rules for safe AI use in your team
- Checking results: facts, sources, tests and the four-eyes principle
- Personal action plan for the next 30 days
- Open questions, outlook and resources for further learning
Practical scenarios from your field
Exploratory data analysis
The assistant writes code for data profiles, distributions, correlations and missing values with pandas or Polars, produces meaningful visualisations and summarises anomalies as hypotheses for modelling.
Turning notebooks into pipelines
The agent refactors notebook code into Python modules with functions, configuration and pytest tests, removes hidden state and makes training and inference reproducible. Results become reliably comparable.
Model training and validation
The AI builds scikit-learn pipelines or PyTorch training loops with proper cross-validation, suitable metrics and baselines, and actively flags risks such as data leakage or class imbalance.
Experiment tracking and MLOps
The assistant sets up MLflow or similar tools for parameters, metrics and artefacts, versions models and creates training and deployment workflows for the CI/CD pipeline.
Explaining and auditing models
Using SHAP, permutation importance or partial dependence plots, the AI produces explanations of model decisions as well as analyses of fairness and robustness across relevant subgroups.
Evaluating LLM and RAG prototypes
The AI helps build evaluation datasets, automated scoring metrics and comparison experiments for prompt variants, embedding models or retrieval strategies. Quality, cost and latency of variants can be compared objectively.
Possible hands-on projects
Churn prediction with explanations
A complete project from data preparation through a gradient boosting model to a small Streamlit app displaying predictions and SHAP explanations.
Model monitoring script
A script that compares data and prediction distributions over time, detects drift and produces a compact report with charts. The team is notified automatically when anomalies appear.
Evaluation harness for a RAG chatbot
A test set-up with a question set, reference answers and automated scoring that compares different retrieval and prompt configurations. Results are summarised per configuration in a clear table.
Prerequisites
Basic web development knowledge (HTML, CSS, JavaScript) and some experience with Git are helpful. Prior React or Supabase experience is not required. Good Python skills, experience with pandas or Polars and scikit-learn, and a grounding in statistics and machine learning.
Technical requirements
A laptop with an up-to-date browser, a Lovable account (a paid plan is recommended for intensive use) and free GitHub and Supabase accounts.
Data protection & compliance
Training and analysis data often contains personal information, so use schemas, aggregates or synthetic data in prompts and observe GDPR principles such as purpose limitation. For high-risk applications, the EU AI Act sets requirements on data quality, documentation and transparency, and the licences of datasets, pre-trained models and Python packages must also be checked.
Suggested dates
All dates are suggestions – your preferred date is always possible.
Frequently asked questions
Do I own the code Lovable generates?
Yes. The output is standard React and TypeScript code that you can push to your own repository via the GitHub sync. From there it can be developed, tested and deployed entirely independently of Lovable. In the course we show you how to set this up properly from day one so you avoid any lock-in.
Is Lovable suitable for production applications?
For prototypes, MVPs, internal tools and modest customer-facing apps, yes, provided you review security and the data model carefully. Supabase Row Level Security, secrets handling and the maintainability of a growing codebase all need attention. The course covers exactly which checks make sense before you go live.
How does Lovable differ from Cursor or Claude Code?
Lovable is a browser-based app builder with a fixed stack, built-in hosting and Supabase integration, which makes it ideal for getting started quickly. Cursor and Claude Code work inside any existing codebase and technology. Many teams combine them: start in Lovable, then continue in the editor via GitHub.
Can I trust AI on statistical questions?
AI assistants know common methods very well but occasionally make methodological mistakes, for example in splitting training and test data or choosing metrics. On the course you learn to ask the AI explicitly about assumptions and risks, check results against baselines and sanity checks, and keep professional accountability firmly with yourself.
Does AI-assisted coding work in Jupyter notebooks?
Yes. Many assistants support notebooks directly, and agents can also read, execute and convert notebooks into modules. We show when notebooks are ideal for exploration and how to make the transition to versioned, tested code efficiently with AI support. The two approaches complement each other.
How much does “Lovable for Data Scientists and ML Engineers” cost?
The course costs a flat €949 plus VAT per participant – whether online or on-site. This includes 1 training day(s) with a live trainer, course materials, a lab environment and a certificate of attendance.
Can I suggest my own date?
Yes. Our suggested dates are just a guide. In the request form you can always describe your preferred date or period in free text – we check availability and confirm within one working day.
Is the course online or on-site?
Both are possible: live online in a virtual classroom or on-site in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart or Kaiserslautern. We can also run the course exclusively for your team.
More courses for Data Scientists and ML Engineers
- Claude Code for Data Scientists and ML Engineers
- Vibe Coding for Data Scientists and ML Engineers
- Agentic Coding for Data Scientists and ML Engineers
- Cursor for Data Scientists and ML Engineers
- GitHub Copilot for Data Scientists and ML Engineers
- OpenAI Codex for Data Scientists and ML Engineers
- Windsurf for Data Scientists and ML Engineers
- Replit Agent for Data Scientists and ML Engineers
- Model Context Protocol (MCP) for Data Scientists and ML Engineers
- AI Agent Development for Data Scientists and ML Engineers
- RAG & LLM Applications for Data Scientists and ML Engineers
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