Systems | Development | Analytics | API | Testing

Last time in this blog series, we provided an overview of how to leverage the Iguazio Feature Store with Azure ML in part 1. We built out a training workflow that leveraged Iguazio and Azure, trained several models via Azure's AutoML using the data from Iguazio's feature store in part 2. Finally, we downloaded the best models back to Iguazio and logged them using the experiment tracking hooks in part 3. In this final blog, we will.

When dealing with large data sets, it is not feasible to manage data transfers without tools that can efficiently scale. Heroku provides effective data management resources for managing data sets in your cloud data warehouse, but you still need tools that can make the ETL process simpler and as automated as possible. Here are the top five ETL tools for Heroku.

We often hear different terms used to describe forward-looking versions of a company’s financial statements. People frequently use these terms interchangeably, with some having a deeper understanding of the nuances in terminology than others. Forward-looking financial documents may include budgets, projections, forecasts, and pro forma financials.

Is your model ready for production? It depends on how it’s measured. And measuring it with the right metric can unlock even better performance. Evaluating model performance is a vital step in building effective machine learning models. As you get started on Continual and start building models, understanding evaluation metrics helps to productionize the best performing model for your use case.

Learn why data transformation is essential to data modeling and bringing your organization to the forefront of data literacy.

Google Analytics Enhanced Ecommerce Reporting is one of the best tools you can use to gain customer engagement data and create better CRO strategies. Go through this guide to learn how to use this tool to your advantage.

Data analytics is invaluable to companies that want to drive growth. Research from giants like McKinsey, the Financial Times, and Google confirms it: Companies that rely on data analytics to drive business decision-making grow 2.5x faster than their lagging competitors. So how does descriptive analytics fit into the wider frame of data analytics?

The amount of money invested in “Transformation Programs” is staggering. In the past 20 years we have seen Digital and Agile Transformation Programs grow and swell to $1.3 trillion dollars in 2020 alone. According to the HBR, 70% of that spend is wasted. Many companies miss a key component in their Transformation Program. How will a Product-Led Transformation be different?

More than a decade into the Internet of Things (IoT) era, the immense potential of IoT is becoming real. We’re moving from proof of concepts and pilots to projects at scale. What’s become increasingly clear is the vast complexity of deploying IoT solutions at scale and the necessity to do so to become a data-driven business.