AGvisorPRO Tomorrowland Project: Conversational Expert Systems and Machine Learning Models- AB-061Discipline(s) souhaitée: Informatique, Sciences mathématiques, Mathématiques, Physique et astronomie, Sciences naturelles
Durée du projet: 4 to 6 months
Preferred start date: As soon as possible.
Langue exigée: English
Emplacement(s): Red Deer, AB, Canada
Nombre de postes: 1
Établissements préférés: Université McGill, L'Université de l'Alberta, Université de Waterloo
Au sujet de l’entreprise:
AGvisorPRO is building a global connectivity channel for agriculture serving all sectors of the industry from broadacre farming to indoor operations, from dairy to aquaculture. AGvisorPRO matches, connects, archives, rates and transacts knowledge sessions between “seekers” and “advisors” to provide answers now! The AGvisorPRO platform creates an agricultural matrix connecting farmers, experts, industry, government, researchers, and even the public, to address constraining issues in agriculture. We can put experts “on the farm” without being “on the farm”. We help monetize the experience and wisdom of independent advisors. We displace 1-800 with a Tech Direct Solution, connecting farmers to industry reps for free using AGvisorPRO. We can help farmers attend auction sales without being at the sale. We are device agnostic, with capability to work over low bandwidth environments.
AGvisorPRO has been scaling quickly during the pandemic, as remote connectivity has become a crucial tool worldwide. We have grown our team from 2 to over 25 people, and increased sales, during the pandemic. Activities range from digital marketing to improving matching algorithms to address current user concerns. As we grow, we require talent that can see beyond traditional marketing strategies in ag.
Veuillez décrire le projet.:
We are building conversational expert systems and machine learning models to match a question to an expert who can answer the question. This project will be focussed on building a system to help us label question data to a list of categories that they need to be matched into. As well as analyzing machine learning models to help improve our system.
- Build tools to assist with data labelling
- Build NLP models to match questions to categories
- Analyze model behaviour using standard practices
- Experiment with other modelling approaches to get successful categorization results
Expertise ou compétences exigées:
- Understanding of machine learning
- Knowledge of data science and machine learning libraries in python
- Experience in NLP
- Experience preparing datasets for machine learning