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Te interesează să angajezi un Streamlit Specialist? Acestea sunt cele mai bune proiecte de Streamlit finalizate recent pe platforma Freelancer. Au fost alese pe baza recenziilor reale ale clienților care au acordat minimum 4,5 stele pentru servicii. Rezultatele sunt actualizate lunar.
PROIECTUL
Built a single-page Streamlit app performing SPF/DMARC domain scanning with color-coded vulnerability metrics. A ReportLab engine generated styled, in-memory PDF audit reports with a session-state download to prevent page refresh.
RECENZIA CLIENTULUI
It was a pleasure working with Muhammad. We got a clean and timely job done.
Python · Software Architecture · Google App Engine
PROIECTUL
A real-time IT quiz platform was built with three synchronized WebSocket views covering admin controls, an audience leaderboard display, and team interfaces for a school 4-house competition. The client confirmed thorough testing and smooth delivery.
RECENZIA CLIENTULUI
It was great working with Ajith. He really knows his stuff technically and handled everything we needed without any hassle. He was always quick to respond to feedback and made sure everything was thoroughly tested and working perfectly before handing it over. Highly recommend him!
JavaScript · Python · Django
PROIECTUL
A Streamlit dashboard was built alongside a web scraping system to collect, deduplicate, and display structured data with search and filter functionality. The review confirmed accurate delivery, clean extraction, and full alignment with requirements.
RECENZIA CLIENTULUI
Scraping project delivered accurately and fully aligned with requirements. Data extraction is clean, well-structured, and complete. Excellent execution and timely delivery.
JavaScript · Python · Web Scraping
PROIECTUL
Built a local Streamlit dashboard in Python connecting Outlook via Microsoft Graph API, with OpenAI-powered email classification, a follow-up tracker, draft reply generation, and CSV exports. The project was delivered across structured milestones with full documentation and a handover call.
RECENZIA CLIENTULUI
recommended and will use again
Python · Linux · Django
A Streamlit specialist is a Python developer who builds interactive data applications, dashboards, and machine learning interfaces using the Streamlit open-source framework. Hiring a Streamlit specialist gives you a fast path from raw data and trained models to a shareable web app, without the overhead of building a full-stack frontend from scratch.
Streamlit developers turn Python scripts, notebooks, and machine learning models into browser-based applications that stakeholders can use without installing anything. The output is typically a live web app with widgets, charts, file uploads, and authenticated access, deployed on Streamlit Community Cloud, AWS, GCP, Azure, or Docker-based infrastructure.
This matters commercially because internal tools, prototypes, and ML demos that would otherwise take a frontend team weeks can ship in days. Data teams use Streamlit apps to expose model predictions to business users, run what-if simulations, and replace fragile spreadsheets with auditable interfaces.
A capable Streamlit developer works fluently across the Python data stack and modern deployment tooling. Core competencies include:
Streamlit is widely adopted across data-driven industries. Common engagements include:
Strong candidates combine Python engineering with product sensibility. Look for these signals on a profile:
Useful interview questions to ask shortlisted candidates:
Depending on scope, you may want a freelancer who also covers Python development, data engineering, machine learning engineering, FastAPI, Plotly Dash, data visualisation, or DevOps. For LLM-heavy projects, look for experience with vector databases like Pinecone, Weaviate, or pgvector, plus prompt engineering and retrieval-augmented generation patterns.
Freelancer.com gives you access to a global pool of Python developers and data professionals with deep, demonstrable Streamlit experience. You can review portfolios, ratings, completion rates, and verified reviews before you commit, and you set your own budget while receiving competitive bids from freelancers on Freelancer.com.
Whether you need a one-week ML demo or an ongoing internal tools partner, you can post a project on Freelancer.com and shortlist candidates within hours. Milestone Payments hold funds securely until agreed deliverables are met, giving both sides confidence throughout the engagement.
Ready to turn your data, models, or internal workflow into a working web app?
Hiring a Streamlit specialist is straightforward when you treat the brief as a filter for technical fit. The process below helps you attract candidates who understand Python, data, and app design, and lets you compare proposals against the actual work you need delivered.
Your project post is the single biggest determinant of bid quality. A clear brief filters for Streamlit developers whose Python, data, and UI experience genuinely match your needs, and saves you hours of back-and-forth. Head to the
Bids are short proposals, not just price quotes. They reveal how each Streamlit specialist interprets your brief, what architecture they propose, and whether their timeline is realistic. Read carefully and shortlist candidates whose understanding of the work matches the project.
The final decision combines proposal quality with profile evidence. Weigh consistency across past Streamlit work rather than a single impressive demo, and pay close attention to written client reviews that describe how the freelancer handled changes, debugging, and deployment.
A focused dashboard or model demo can be delivered in a few days to two weeks, depending on data complexity and integrations. Production-grade multi-page apps with authentication, custom components, and cloud deployment usually run four to eight weeks. Scope, data readiness, and review cycles are the biggest drivers of timeline.
Any Python developer can write a Streamlit script, but a specialist understands the framework's execution model, caching, session state, and performance constraints in depth. They also bring opinions on layout, widget design, and deployment patterns that prevent the common pitfalls of slow, brittle apps.
Streamlit is ideal for fast iteration on data apps, internal tools, and ML demos where Python-only development matters. Dash suits highly customised analytics dashboards with complex callbacks, while Gradio is geared toward ML model demos with minimal UI. A good Streamlit specialist can advise honestly on which fits your use case.
Yes. Most Streamlit freelancers handle deployment to Streamlit Community Cloud, Hugging Face Spaces, AWS, GCP, Azure, or your own Docker and Kubernetes infrastructure. They can also configure custom domains, SSL, authentication, and CI/CD pipelines as part of the engagement.
For most dashboards, prototypes, and internal tools, a single experienced freelancer is sufficient and faster to engage. Agencies make more sense when you need a coordinated team across data engineering, ML, and frontend work. You can always start with a freelancer and scale the team as the project grows.
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Inspiră-te din proiectele de Streamlit
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Designul unui pliant.
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50 USD în 6 zile.
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