321 points by ai_jobs 6 months ago flag hide 23 comments
john_doe 6 months ago next
Great work! Can you share more details about the AI models you used for job matching? I'm looking to implement something similar for my own platform.
jane_doe 6 months ago next
Sure @john_doe! We mainly used deep learning models trained on large-scale job and applicant data to perform the matching. We used AWS for our infrastructure and had a lot of fun implementing a CI/CD pipeline using CircleCI and CodeBuild.
dev_ops_guy 6 months ago prev next
@john_doe I'm impressed by the turnaround time. Did you face any challenges related to data privacy while training your models?
jane_doe 6 months ago next
@dev_ops_guy Yes, we did face similar challenges. We made sure that the data we obtained was anonymized and put in place strict data access controls. We also outsourced the training to a third-party service provider with a strong reputation for handling sensitive data.
john_doe 6 months ago prev next
Also, any infrastructure and deployment insights would be greatly appreciated.
jane_doe 6 months ago next
As for infrastructure, we containerized our applications with Docker and used ECS to orchestrate and manage the containers. We found this to be very efficient for our use case.
dev_ops_guy 6 months ago next
We've been using ECS ourselves and absolutely love it. Have you tried implementing blue/green deployments with ECS services and Application Load Balancer?
jane_doe 6 months ago next
@dev_ops_guy We haven't implemented blue/green deployments yet, but I've heard that it's an excellent method to minimize downtime and minimize risks. Will consider trying it out!
udacity_alum 6 months ago prev next
This is incredibly inspiring! The Udacity AI Program has truly been an amazing experience. Keep up the great work.
jane_doe 6 months ago next
@udacity_alum Thank you! The Udacity AI Program has provided us with the solid theoretical and practical foundation we needed to embark on this project.
ml_guru 6 months ago prev next
Congratulations! Can you share any lessons learned on selecting and preprocessing the job and applicant data? We're having a hard time figuring this out.
jane_doe 6 months ago next
@ml_guru Absolutely! We used a combination of supervised and unsupervised preprocessing techniques to learn and infer the underlying complexities in our training dataset.
jane_doe 6 months ago next
@ml_guru First, we used an unsupervised learning approach to clean and preprocess our raw data, then performed unigram, bigram feature engineering. Finally, we transformed and vectorized the text data using Word2Vec.
ml_guru 6 months ago next
@jane_doe Interesting! In our case, we've been trying to make use of pre-trained word embeddings for transformation and vectorization, but we're not seeing great results yet. Would there be any resources you would recommend for optimizing these techniques?
jane_doe 6 months ago next
@ml_guru I recently read the paper <https://arxiv.org/abs/1810.04805> which has a detailed account of how to fine-tune pre-trained word embeddings for NLP tasks. It has some truly insightful suggestions and has helped us significantly with our project.
john_doe 6 months ago prev next
@ml_guru One thing we did to preprocess job listings was to scrape Stack Overflow Careers and LinkedIn job postings for a more diverse and realistic data set.
ml_guru 6 months ago next
@johndoe That's a great point! In our case, we're relying solely on company provided data, which could lead to issues with non-standardized data entries and limited diversity in job posting terminology.
startup_founder 6 months ago prev next
Really exciting! I'm planning to develop a similar platform for my startup but uncertain about the time and resources needed. How many team members did you involve and what skill sets did you need?
jane_doe 6 months ago next
@startup_founder We had a team of 6 people, consisting of 3 developers, 1 data scientist, 1 dev-ops engineer and 1 project manager. The combined skill set we required mainly comprised of Data Engineering, Data Science, Backend Development, Frontend Development and DevOps Expertise.
john_doe 6 months ago prev next
@startup_founder The project was completed within the 3-month timeline as we managed our resources well and had a good project management workflow in place. We used the Kanban workflow, which kept us on track with task progression and allowed us to meet our sprint goals.
fsd_student 6 months ago prev next
Wow, 6 people in 3 months sounds impressive. What were your initial goals when starting out on this project?
jane_doe 6 months ago next
@fsd_student Our initial goals consisted of four main objectives: 1) Create a minimum viable product (MVP) with core job matching capabilities, 2) Have a scalable and modular architecture, 3) Ensure data privacy and compliance and 4) Establish a robust CI/CD pipeline.
john_doe 6 months ago next
@fsd_student This approach worked for us as it allowed us to quickly validate our product idea and at the same time made sure we had a strong technological foundation to build upon.