Big Data Big Yield. Agenda

Transcription

1 Big Data Big Yield.

2 Big Data Big Yield Agenda 1. Quick Introduction 2. My vision for Ag culture 3. Trends enabling the new Ag culture revolution 4. Current Fields of ML applications 5. Case Study 6. Where are we headed

3 EXPERTS SOLVING THE TOUGHEST DATA CHALLENGES Without data, you re just another person with an opinion At Starschema we believe in the power of data that the information contained in data has the power to change the world. We come to work everyday to help our clients use that power to bring about that change. Image Monte Biz Company

4 OUR SERVICES We build data solutions that give our clients a competitive advantage Strategic Consulting Data Engineering Data Science & Visualization Software Engineering Deploying deep knowledge Realizing business value for our Using machine learning, AI, and Scalable application and experts to analyze clients through end to end our in-house experts to analyze development for a wide range business problems and their development, optimization and data and creating predictive of enterprise needs. We use technical underpinnings to operation of automated models for customers that reveal the latest technologies often create strategic plans and reporting, data lakes and deeper insights that result in cloud native that allow point solutions. related software platforms. competitive advantage. scalability form day one

5 STARSCHEMA STORY Overseas Expansion & Investment Two new offices in San Francisco and Washington DC, raised $5M Private Fund from OTP Group Mil(USD) Staff (FTE) Growth Continues Fastest 5000 in EU by Inc.com, Deloitte Fast 50 Technology Revenue: $12M Staff: 200 FTE Expanding Services New business model: SLA-based support and DevOps for BI Expanding Technologies Investment in new generation BI, Big Data technologies: Hadoop, Tableau, Talend, Greenplum Growth Accelerates Tech investments started to pay off as new gen techs becoming mainstream Founded Subcontracting Oracle Hungary projects Organic Growth Expanding customer-base in traditional BI Forecast 0.0

6 WHO THE HELL ARE YOU? Quick intro Once a data head, always a data head Background 7+ years experience 4+ years in Business Intelligence 3 years as a consultant Techno-utopist for my whole life Currently Marketing through the eyes of a BI expert Hobbies Coding Traveling

7 DREAM OF A TECHNOCRAT Automating the food supply chain to bring abundance Farming Transportation Grading Processing Cleaning Packaging Manufacturing Transformation Storage Wholesale Retail Export

8 FARMERS OF THE FUTURE Big Data Big Yield Farmers will change their rubber boots to R studio Farmers of the past Farmers of the future VS

9 3 TRENDS DRIVING THE REVOLUTION OF AGRICULTURE AI-driven automation - based on integrated, real-time cloud data lake. ROBOTICS Sensor technologies (price, quality) Movement Precision Software and computing power DATA SCIENCE Machine Learning frameworks Training data Industry specific solutions CLOUD MATURITY AND ADOPTION Product market fit of industry solutions Moore s law in the cloud Ease of use Wide adoption

10 3 APPLICATION CATEGORIES Farm automation is just getting started Agricultural Robots Crop and Soil Monitoring Predictive Analytics Autonomous robots to handle essential agricultural tasks such as harvesting crops at a higher volume and faster pace than human laborers. Leveraging sensors, computer vision and deep-learning algorithms to process data to monitor and analyze crop and soil health and yield. Machine learning models are being developed to track and predict various environmental impacts on crop yield such as weather changes.

11 CASE STUDY - REQUIREMENTS Tractor Manufacturing Company Vision for automated tractors No big data and data science architecture No competence in building and operating one Minimize maintenance Able to scale infinitely Go Cloud First

12 CASE STUDY ARCHITECTURE DESIGN First thing first - Technology backbone of farming analytics GDPR compliant Advanced Analytics Platform enabling analytical projects globally. AWS SAM (Serverless Application model) was our basis of the architecture design Data Scientists to work with the data using JupyterNotebooks or Rstudio Deployment of packages and models are done using Docker images SQL Layer for querying data were provided using Glue Crawlers and Athena and Parquet file formats. CI/CD pipelines created with Jenkins.

13 CASE STUDY Predictive Maintenance Not all vehicles are used the same, one is put through heavy load, one is barely used. Calling every vehicle for maintenance at the same time (lets say 3 months) is not the ideal approach. each vehicle should have their own schedule based on the usage. The telemetry data helps us to analyze the usage and the load and predict when it should come for regular maintenance and also save it from possible critical failures.

14 CASE STUDY Fuel Consumption prediction Not all terrains are the same in the world, some have loose sand some have hard soil and the wheel and traction between vehicle and the terrain differs accordingly. Based on the Geopositioning and the terrain estimate information, the amount of torque required by the drive shaft can be manipulated to suit the terrain and also reduce fuel consumption because of effective engine management.. High end luxury ATVs have this - not field heavy machinery

15 CASE STUDY Drone footage processing Drones are the future of surveillance. Why restrict the drone capabilities for military purposes when it can used to take images of fields and understand harvesting needs. Partition a field into virtual grids and using drone images analyze which grids can be harvested and which needs more time or care. Ultimately, self-driving tractors can start harvesting based on the

16 WHERE ARE WE HEADED? FULL AUTOMATION OF FOOD SUPPLY CHAIN FROM FARMING TO RETAIL Farming Transportation Grading Processing Cleaning Packaging Manufacturing Transformation Storage Wholesale Retail Export

17 Discover, manage and use the intelligence hidden in data. CONTACT US Facebook/starschema