Operationalizing AI pipelines is notoriously complex. For deep learning applications, the challenge is even greater, due to the complexities of the types of data involved. Without a holistic view of the pipeline, operationalization can take months, and will require many data science and engineering resources. In this blog post, I'll show you how to move deep learning pipelines from the research environment to production, with minimal effort and without a single line of code.

While it is a little dated, one amusing example that has been the source of countless internet memes is the famous, “is this a chihuahua or a muffin?” classification problem. Figure 01: Is this a chihuahua or a muffin? In this example, the Machine Learning (ML) model struggles to differentiate between a chihuahua and a muffin.

Machine learning (ML) is the area of artificial intelligence (AI) that focuses on how algorithms learn to change and grow. This emerging technology is already a big part of modern life, such as the automation of various tasks and voice-activated technologies. ML is closely linked to big data, data mining, data analytics, and various other aspects of data management.

In recent years, Ethical AI has become an area of increased importance to organisations. Advances in the development and application of Machine Learning (ML) and Deep Learning (DL) algorithms, require greater care to ensure that the ethics embedded in previous rule-based systems are not lost. This has led to Ethical AI being an increasingly popular search term and the subject of many industry analyst reports and papers.

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.

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.