MLM Binary & AI Training
Budget / Salary₹1,500–12,500
TypeFreelance project
LocationRemote
Posted1 hour ago
I’m putting together a hands-on training series for my team that already has a working knowledge of software development and network marketing concepts. The goal is to guide them, step by step, through building and optimising a full-scale MLM binary tree system while weaving in the latest AI technologies for forecasting, lead scoring and automated support.
I want the sessions to be run as interactive workshops—live, project-based and code-heavy—so participants can implement concepts in real time and leave each module with a working feature. Written guides or recorded videos can be added later if you think they’ll reinforce the lessons, but the core delivery method must remain workshop-style.
Key areas we must cover
• Practical design and scaling of the MLM binary tree structure, including spill-over handling, commission calculations and real-time genealogy visualisation.
• Integration of current AI tools (e.g., TensorFlow, PyTorch, OpenAI APIs) to add predictive analytics, chatbot onboarding and fraud detection layers to the platform.
The audience is solidly at an intermediate level—they know their way around Git, REST APIs and basic data structures—so you can skip beginner explanations and focus on architecture decisions, optimisation tricks and AI model fine-tuning.
By the end of the programme I expect:
1. A complete workshop schedule with objectives and sample code for each session.
2. Source files and datasets used during demonstrations, neatly organised in a shared repo.
3. A short assessment or mini-project brief to verify that participants can replicate the binary logic and AI components on their own.
If this format and scope match your expertise, let’s discuss timelines and any environment requirements you might have so we can get the first workshop on the calendar.
I want the sessions to be run as interactive workshops—live, project-based and code-heavy—so participants can implement concepts in real time and leave each module with a working feature. Written guides or recorded videos can be added later if you think they’ll reinforce the lessons, but the core delivery method must remain workshop-style.
Key areas we must cover
• Practical design and scaling of the MLM binary tree structure, including spill-over handling, commission calculations and real-time genealogy visualisation.
• Integration of current AI tools (e.g., TensorFlow, PyTorch, OpenAI APIs) to add predictive analytics, chatbot onboarding and fraud detection layers to the platform.
The audience is solidly at an intermediate level—they know their way around Git, REST APIs and basic data structures—so you can skip beginner explanations and focus on architecture decisions, optimisation tricks and AI model fine-tuning.
By the end of the programme I expect:
1. A complete workshop schedule with objectives and sample code for each session.
2. Source files and datasets used during demonstrations, neatly organised in a shared repo.
3. A short assessment or mini-project brief to verify that participants can replicate the binary logic and AI components on their own.
If this format and scope match your expertise, let’s discuss timelines and any environment requirements you might have so we can get the first workshop on the calendar.
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