Business model canvas
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This business model canvas outlines a value proposition of intelligently solving traffic problems through image processing controlled traffic lights, with key customers being government agencies and traffic management services. The business would utilize image processing techniques like filtering and morphological operations applied to camera images as key activities, partnering with traffic management services and relying on investors, programmers, and hardware materials as key resources to generate revenue through licensing, advertising, retail sales, and subscriptions.
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www.playtactic.com
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We use HiveMQ as open source MQTT broker to ingest data from IoT devices, ingest the data in real time into an Apache Kafka cluster for preprocessing (using Kafka Streams / KSQL), and model training + inference (using TensorFlow 2.0 and its TensorFlow I/O Kafka plugin).
We leverage additional enterprise components from HiveMQ and Confluent to allow easy operations, scalability and monitoring.
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This document discusses best practices for streaming IoT data with MQTT and Apache Kafka. It begins with an overview of a use case involving a global automotive company building a connected car infrastructure. An architecture is presented showing how sensor data from cars can be ingested via MQTT into Apache Kafka and then processed using tools like Kafka Streams, TensorFlow, and ElasticSearch for analytics and alerts. A live demo is described that implements this full pipeline. The document concludes with a discussion of best practices around choosing the right tools, separation of concerns, data types, and next steps.
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"Lyft is a ride-sharing company which is a two-sided marketplace; balancing supply and demand using various levers (passenger pricing, driver incentive etc.) to maintain an efficient system. Lyft has built a real-time optimization platform that helps to build the product faster. This complex system makes real-time decisions using various data sources; machine learning models; and a streaming infrastructure for low latency, reliability and scalability. This infrastructure consumes a massive number of events from different sources to make real-time product decisions.
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Each iteration provided better scale, unlocking different capabilities of the marketplace and new avenues for growth. To accomplish these, the team has set the roadmap for next-generation streaming platform and developing smarter tools.
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* Describing Lyft’s streaming platform for dynamic decision-making
* Where Kafka fits in streaming tech stack and how it helped us to scale better
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* Lesson learned"
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Business model canvas
- 1. BUSINESS MODEL CANVAS Image Processing Conrolled Traffic Lights
- 2. Value Proposition • Intelligently solved traffic problems • Measurably improved traffic flow • Increases road safety
- 3. Customer Segments • Government • DOTC • Traffic Management Services
- 4. Channels • Advertisement • Seminars • Websites
- 5. Customer Relationship • Websites • Blogs and Forums • Up to date monthly email
- 6. Key Activities • Captured Image processing • Filteration • Morphological operation
- 7. Key Partners • Traffic Management Services
- 8. Key Resources • Investor • Programmer • Hardware materials
- 9. Cost Structure • Image processing tool box for MATLAB(FREE) • Programmer = P30,000.00 • 3K camera= P35,000.00 • Firewire IEEE 1394a= + P500.00 Total cost = (P65,500.00)
- 10. Revenue Streams • LICENSING • Ad Revenue • Retail sales • Subscription