Summary In today's episode, I put Meta's new Llama 4 Maverick model head-to-head against Gemini, DeepSeek V3, and ChatGPT using a tricky prompt about egg substitutes to see which one actually delivers the right answer. Here's what this means for you. You'll get a real-world benchmark for choosing between closed and open-weights AI models for…
Summary In today's episode, I walk through Meta's release of Llama 3.3 and test its instruction-following capabilities against the larger 405B model. Here's what this means for you. You gain a new top-tier option for running capable open models locally or through cloud platforms, with instruction-following quality that beats much bigger competitors. You'll also learn…
Summary In today's episode, I test Meta's new Llama 3.2 multimodal models on IBM Watson X, comparing the 90 billion and 11 billion parameter versions across image description and data extraction tasks. Here's what this means for you. You can run capable vision models locally on your own hardware for the cost of electricity, skipping…
Summary In today's episode, I walk through how to choose the right algorithm or model for any given data set by exploring factors like data type, intended outcome, and performance metrics. Here's what this means for you. You gain a clear framework for matching algorithms to your specific business problem rather than guessing or testing…
Today, let’s take a look at a new offering from IBM, the new AutoAI Time Series Forecasting module. Before we begin, let’s define a few things. What is Time Series Forecasting? Time series forecasting is predicting one or more data variables over some future time, based on previous data. Why is Time Series Forecasting Valuable?…
When it comes to learning data science, one of the challenges we face is just how seemingly daunting the field is to learn. There are so many techniques, tactics, and strategies that it’s difficult to know where to start. Learning something new always begins with an understanding of the basics. From martial arts to dance…
Summary In today's episode, I walk through Part 3 of cleaning and prepping data for machine learning analysis, using a real Twitter analytics dataset to demonstrate advanced feature engineering, error detection, and analytic approach selection. Here's what this means for you. You gain a realistic understanding that data preparation is where most of the work…
Summary In today's episode, I break down how to craft bottom-of-the-funnel content that matches the emotional drivers behind purchase decisions and backs them up with rational proof. Here's what this means for you. You'll align your offers with what buyers actually feel at the moment of purchase, lifting conversion rates without changing your product. You'll…
Arjuna asks, “Could you please suggest an approach to forecast multiple targets (e.g., is there a way to select multiple columns in AutoAI). In our use case, we need to develop time series forecasts for multiple products. If we correctly understood AutoAI, it will allow us to select one column at a time to generate…
CC asks, “What’s the most valuable analysis overall for a small business to perform?” If we define a small business as an enterprise with less than $1 million in sales and fewer than 10 employees, then from an overall business perspective it’s got to be RFM analysis. Nothing else comes close in terms of helping…
