Developing a modern, open source machine learning pipeline with Kubeflow
Who is this presentation for?
- Machine learning engineers, data scientists, architects, AI and ML researchers, and CTOs
In recent years, there have been significant developments in open source machine learning toolkits, such as scikit-learn, TensorFlow, PyTorch, and many others. These toolkits have been widely adopted by business and developers looking to introduce machine learning into their organizations. However, to deliver on their potential, they must to be embedded in an end-to-end machine learning pipeline.
Steve Flinter and Ahmed Menshaw explore the work that Mastercard Labs undertook to build an end-to-end machine learning pipeline, suitable for both R&D and production, using Kubernetes and Kubeflow. They demonstrate how the pipeline can be defined in software, configured, connected to a data streaming service (Apache Kafka), and used to train and deploy a model, which can be exposed for inference via an API. If you’re a professional software engineer or machine learning engineer seeking to understand how to introduce a robust ML pipeline into your organization, this talk is ideal for you. You’ll also learn how to manage the end-to-end workflow of data to training to deploying and serving a trained model.
- General knowledge of how Kubernetes works and the benefit that it brings
- A basic understanding of TensorFlow or similar ML libraries
What you'll learn
- Understand that developing an end-to-end machine learning pipeline is crucial for any organization planning on introducing ML into their IT environment
- Learn to build such a pipeline using modern, open source tools
Steve Flinter is the artificial intelligence practice lead at Mastercard Labs. He’s an IT professional with more than 20 years’ experience in industry, government, and academia. Previously, Steve was with the global Mastercard start path team, Mastercard’s startup engagement activity, where he supported fintech startup companies by connecting them to Mastercard and its global network of customers; managed an investment portfolio of approximately €120M in the software and computer science areas at Science Foundation Ireland (SFI), the Irish Government agency investing in academic research; and worked in various senior software development roles in a variety of industry verticals. Steve holds a BSc in computer applications from Dublin City University and a PhD in computer science specializing in artificial intelligence from Trinity College Dublin.
Ahmed Menshawy is a machine learning engineer in the AI Practice within the R&D Group at Mastercard Labs, where he works on a wide range of problems related to the application of AI and machine learning to Mastercard’s products and services. Ahmed is interested in studying the overlap between knowledge, logic, language, and learning. In particular, his focus is in how machine learning can be used for distilling large amounts of unstructured, semistructured, and structured data with hidden patterns into new knowledge about the world by using methods ranging from deep learning to statistical relational learning. Ahmed has authored two books, Deep Learning with TensorFlow and Deep Learning by Example, which focus on advanced deep learning topics. Ahmed has a BSc in computer science and an MSc in machine learning from Helwan University, Cairo, Egypt.
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