Machine Learning on Fiverr: prices, tags and skills

What Machine Learning gigs charge

Median package prices across 119 gigs:

• Basic: $90
• Standard: $195
• Premium: $325

Most used tags

• machine learning (106 gigs)
• python (106 gigs)
• deep learning (78 gigs)
• tensorflow (71 gigs)
• pytorch (71 gigs)
• scikit-learn (71 gigs)
• data science (70 gigs)
• keras (62 gigs)
• nlp (61 gigs)
• classification (54 gigs)

Skills buyers expect

Clients in this subcategory expect end-to-end machine learning deliveries: from exploratory data analysis and model selection to evaluation, documentation and often deployment (APIs/web apps). Projects commonly focus on tabular predictive modeling, NLP, computer vision and increasingly LLM/GenAI integrations, delivered primarily with the Python stack.

• Python programming
• Machine learning
• Deep learning
• Data analysis
• Feature engineering
• Model evaluation
• Natural language processing
• Computer vision
• Statistics & probability
• Data visualization

What stands out in this market

[Commoditization of standard ML services at low-to-mid price points]

Many gigs offer core ML tasks (model building, prediction, data cleaning, visualization) with "basic" packages clustered around $90–$200 and standard/premium tiers up to ~$450–$850. Market significance: routine supervised learning, data analysis and reporting are highly commoditized — competing on price and turnaround. Sellers should optimize repeatable deliverables (notebooks, dashboards, evaluation reports) and automation to maintain margins.

[High-value demand for custom LLMs and generative-AI applications]

A smaller but notable segment lists premium packages that jump into the thousands (examples up to $5k–$7.5k) explicitly for chatbots, custom LLM integrations, and generative-AI apps (OpenAI, GPT, Claude). Market significance: clients are willing to pay large sums for end-to-end, production-ready generative AI solutions and custom integrations — opportunity for specialist teams offering architecture, prompt engineering, security and deployment.

[Python-first ecosystem with deep learning and NLP specialization]

Tags and descriptions overwhelmingly reference Python toolchains (Jupyter, scikit-learn, PyTorch, Keras), plus deep learning, NLP and computer vision skills. Market significance: buyers expect Python-based deliverables and advanced model types (CNNs, RNNs, transformers). Sellers should highlight PyTorch/TF expertise, model explainability, and pretrained model/transfer-learning workflows to win higher-value work.

[End-to-end, app-integrated solutions outrank one-off models]

Many gigs advertise full-project support — from data analysis and modeling to app/website/chatbot integration and visualization (Tableau, web AI, chatbot deployment). Market significance: clients prefer turnkey solutions that include deployment and user-facing features. There’s a growing opportunity for offerings that combine ML + front-end/backend integration + monitoring (MLOps-lite).

[Business-focused niche offerings and packaged use-cases]

Frequent tags reference business use-cases (churn, segmentation, predictive analytics, ranking, sentiment analysis) rather than purely academic models. Market significance: demand favors verticalized, outcome-driven solutions (e.g., churn reduction, sentiment-driven insights). Sellers can command higher rates by packaging domain-specific models, KPI-linked deliverables, and clear ROI/cost-savings narratives.