Machine Learning in your marketing mix strategy: taking the guesswork out
This blog post has been written with the collaboration of Gonzalo Marín. Designing a pricing solution involves shuffling a fundamental piece in the company’s marketing mix puzzle. While the price may look like the most fundamental piece to maximize profit it can rarely be defined independently of the others. This is why, in this post, we will dive into how Machine Learning systems can be beneficial to automate and optimize other marketing tasks, and how globally they can help improve your company’s pricing strategy.
Face mask detection in street camera video streams using AI: behind the curtain
This blog post has been written with the collaboration of Marcos Toscano. In the new world of coronavirus, multidisciplinary efforts have been organized to slow the spread of the pandemic. The AI community has also been a part of these endeavors. In particular, developments for monitoring social distancing or identifying face masks have made-the-headlines. But all this hype and anxiety to show off results as fast as possible, added up to the usual AI overpromising factor (see AI winter), may be signaling the wrong idea that solving some of these use cases is almost trivial due to the mighty powers of AI.
Personal data anonymization: key concepts & how it affects machine learning models
Data anonymization is the alteration process of personally identifiable information (PII) in a dataset, to protect individual identification. This concept has acquired increasing importance over the past few years, and it has become an ongoing topic of research. New information about privacy concerns around the world appears each day, making it hard to keep track of the latest models and techniques, and it’s even harder to enter this data privacy-related world without knowing some basic concepts.
Price optimization for e-commerce: a case study
This blog post has been written with the collaboration of Maia Brenner, Gonzalo Marín and Marcos Toscano. We have previously discussed how a data-driven price optimization is no longer an option for retailers, but a question of how and when to do it. With a world that’s moving towards changing prices more and more dynamically, static pricing strategies can’t keep up, and data-driven approaches have arrived to stay. In this post, we’ll be focusing on how to perform data-driven price optimization, using a case study to showcase what can be done using sales data to predict demand and optimize prices.
How Machine Learning is reshaping Price Optimization
The challenge of setting the right price Setting the right price for a good or service is an old problem in economic theory. There are a vast amount of pricing strategies that depend on the objective sought. One company may seek to maximize profitability on each unit sold or on the overall market share, while another company needs to access a new market or to protect an existing one. Moreover, different scenarios can coexist in the same company for different goods or customer segments.
Swift: Google's bet on differentiable programming
Two years ago, a small team at Google started working on making Swift the first mainstream language with first-class language-integrated differentiable programming capabilities. The scope and initial results of the project have been remarkable, and general public usability is not very far off. Despite this, the project hasn’t received a lot of interest in the machine learning community and remains unknown to most practitioners. This can be attributed in part to the choice of language, which has largely been met with confusion and indifference, as Swift has almost no presence in the data science ecosystem and has mainly been used for building iOS apps.
Real estate and machine learning: takeaways from Inman Connect 2020
At Inman Connect (ICNY) 2020, relevant real estate players gathered to discuss the future of the industry. I got the chance to attend the latest edition in NYC. Since we know machine learning is driving innovation across every industry, real estate included, I took the time to summarize my experience as an attendee of ICNY by highlighting key findings in this blog post. These insights are based on my personal interpretation of the sessions, booth visits, and conversations with industry players (real estate agents, proptech workers, investors, and other conference participants).
AI trends: what to expect in 2020
Over the past decade, we have witnessed notable breakthroughs in Artificial Intelligence (AI), thanks in large part to the development of deep learning approaches. Healthcare, finance, human resources, retail, there is no field in which AI has not proven to be a game-changer. Who would have said just a few years ago that there would be autonomous vehicles on public roads, that large-scale facial recognition would no longer be science fiction, or that fake news could have such an impact socially, economically, and politically?
AI applications for social good
Artificial intelligence is gaining traction in areas of social responsibility. From climate change to social polarization to epidemics, humankind has been seeking new solutions to old but persistent problems. From a technological point of view, the amount of daily data produced in the digital universe now allows for state-of-the-art approaches, which may lead to innovative solutions in these underserved areas. AI for social good turned into a reality for us at Tryolabs after we collaborated with an NGO to improve upon how African lions are tracked, which helps with species preservation.
Top 10 Python libraries of 2019
Welcome back to the fifth yearly edition of our Top Python Libraries list. Here you will find some hidden gems of the open-source world to get you started on your new project or spice up your existing ones. You’ll find machine learning and non-machine learning libraries, so we got you all covered. We hope you enjoy it as much as we did creating it, so here we go! 1. HTTPX As a die-hard Python fan who usually interacts with APIs, you are probably familiar with the requests library.