Training course · Replit Agent · Data Science & Machine Learning

Replit Agent for Data Scientists and ML Engineers

Build, test and host apps in the browser: go from idea to a published application with a database using Replit Agent.

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

“Replit Agent for Data Scientists and ML Engineers” is a hands-on course 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. Rather than learning Replit Agent in the abstract, you work on tasks from your daily routine – with examples, exercises and templates from Data Science & Machine Learning.

Replit brings together a development environment, runtime, database and hosting in a single browser workspace. Replit Agent builds on top of that: you describe an application in plain language, and the agent drafts a plan, creates files, installs packages, sets up a database, starts the app and tests it on its own. In this one-day course you learn to steer that process deliberately, whether you are a professional developer, a product manager validating ideas, an educator, or a founder who needs a first product quickly.

You work on an application of your own, such as an internal tool, a small API, a data dashboard or an MVP with user accounts. Along the way you learn to state requirements clearly, use checkpoints for safe rollbacks, handle secrets and the database correctly and choose the right deployment type. For technically experienced participants we go deeper into code structure, Git integration and debugging in the shell. Finally, you take a realistic look at costs, security and limits, so that a quick prototype does not turn into an unmanageable production system.

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 phrase requirements so that Replit Agent plans and builds a suitable application.
  • You extend an app step by step and use checkpoints to roll back unwanted changes safely.
  • You set up a database, user sign-in and secrets correctly.
  • You publish your application using the appropriate deployment type.
  • 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 The Replit workspace and Agent at a glance
  • Understanding the workspace: editor, preview, console, shell and files
  • How the agent works: plan, implementation, self-testing and clarifying questions
  • Agent versus Assistant for small, targeted changes
  • Recognising projects that suit Replit Agent and those that do not
Module 2 From idea to working app
  • Writing requirements as a short specification: users, features, data
  • Using plan mode to agree scope and approach up front
  • Extending the app in small steps and checking results in the preview
  • Using checkpoints and rollbacks when a change goes wrong
Module 3 Data, users and integrations
  • Creating the built-in PostgreSQL database, inspecting tables and editing data
  • Setting up sign-in and user management with Replit Auth
  • Storing API keys safely in Secrets rather than in code
  • Connecting external services such as payments, email or AI models
Module 4 Understanding code, debugging and collaboration
  • Reading the project structure: where frontend, backend and data logic live
  • Using console errors and logs and passing them back to the agent
  • Collaborating in real time and versioning projects with Git
  • Making targeted changes to Python and JavaScript projects without the agent
Module 5 Publishing, operations and limits
  • Choosing a deployment type: static, autoscale, always-on or scheduled
  • Setting up a custom domain, environment variables and monitoring
  • Estimating and capping costs for agent usage and hosting
  • Data protection, access rights and handover to a development team
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

Confident computer use and curiosity about software development are enough to get started. Programming experience in Python or JavaScript helps but 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 and a Replit account; a paid plan is required for working with the agent and for deployments.

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.

  • Wed, 25 November 2026 Online Request
  • Wed, 2 December 2026 Augsburg Request
  • Wed, 16 December 2026 Online Request
  • Wed, 13 January 2027 Kaiserslautern Request
  • Mon, 18 January 2027 Online Request
  • Thu, 4 February 2027 Bremen Request

Frequently asked questions

Can I attend without programming experience?

Yes. Replit Agent writes the code while you focus on requirements, testing and decisions. We explain the key concepts, such as database, API and deployment, in plain terms. Participants with programming experience get deeper dives into code structure and debugging, so both groups get real value from the day.

Who owns the code, and can I move away from Replit?

The generated code is a normal project in your workspace and can be pushed to your own repository via Git or downloaded. Moving to other infrastructure is possible but requires changes to the database, authentication and deployment setup. In the course we show you what to keep in mind from the start.

How do I keep costs under control?

Agent usage is billed by effort, and hosting is charged separately depending on deployment type and resources. Clear, incremental prompts, plan mode and using the Assistant for small tweaks reduce consumption considerably. We show where to monitor usage, how to set spending limits and which deployment type is cost-effective for your use case.

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 “Replit Agent 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.

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