How To Implement A Product Recommendation System With Snowflake

How To Implement A Product Recommendation System With Snowflake

Previously, we discussed what product recommendation systems are and why they matter for businesses. Specifically, we discussed why providing personalized recommendations to users based on their past behavior and preferences adds so much value and allows enterprises to better compete in the market. For today’s blog, we will be diving into the actual methods for […]

How Do Product Recommendations Work in ML?

Have you ever wondered, “How did my phone know I wanted to buy this?!” It’s almost as if your devices know you better than you do yourself. How can this be possible? This is no magic trick. It is a feat of Machine Learning known as the Product Recommendation System. In this post, we will […]

How to Create an ML Visualization and Monitoring App With Streamlit on Snowflake

How to Create an ML Visualization and Monitoring App with Streamlit on Snowflake

Imagine this scenario: you’ve built and trained your machine learning models on Snowpark and have stored your forecasting results in the Snowflake Data Cloud. Your business stakeholders want to know how well your ML models are performing, but they may not have the technical expertise to query the data themselves. What can you do? Great question! […]

How To Handle Imbalanced Data in Classification

Classification is a Machine Learning task that is often used to solve critical business problems. Be it predicting customer churn or fraudulent transactions, ML helps businesses take the right actions at the right time.  Solving problems with real-world data is not always a straightforward task as it involves dealing with imbalanced classes. Not dealing with […]

How Can I Trust My ML Predictions?

Machine learning models can be extremely useful to solve critical business problems but the nature of many modeling techniques can lead to more questions than answers if great care is not taken when designing the model and in understanding the outputs. In this blog, we will look at some data science best practices that we […]

How Do I Know if I Have a Problem That ML Can Solve?

In the last few years, whether you know it or not, your life has been greatly affected by machine learning (ML). From playing basketball to recommending products on Amazon, machine learning and its many capabilities are being leveraged by more companies than ever before. The power of ML to take in vast amounts of data […]

How to Predict Customer Churn with Machine Learning

With the plethora of choices available in today’s marketplace, customer loyalty has been extremely difficult to build. Because of this, some customers will inevitably leave your service. The key question is which customers would have changed their minds if certain action(s) was taken? Customer churn is defined as the percentage of customers who leave your […]

3 Critical Steps to Take When Predicting Adverse Health Events with ML

In this post, we are going to discuss three steps to take when leveraging machine learning (ML) to successfully predict adverse health events. We will skip over security, privacy, and transparency — which you can find in this article. The three steps we’re going to cover in this post are: Population Data cleaning, manipulation, and […]

Using ML to Determine the Next Best Action for Customers

Marketing has evolved from mass mailing every household to sending mass emails to everyone on a list to now being able to send a targeted message to a select group of people within an email list. This targeted approach has helped marketers realize eye-opening ROI on their campaigns and is now where most of them […]

AI-Based Triage for Emergency Patients

If you’ve seen any American medical drama, you’ve probably watched a scene where someone comes in after a massively destructive event walking and talking normally. Then, about halfway through the episode, the person suddenly dies. Not all emergency rooms are that tragic, but it can’t be denied that we have a doctor shortage or that […]