It is not an easy task to design and build systems that involve Machine Learning and Data Science requirements. In addition to this, managing the complexity of intelligent systems requires careful planning and execution. In this talk, I will share the different strategies and solutions on how to design, build, deploy, and maintain complex intelligent systems and workflows. I will discuss how different concepts like Metaprogramming, Infrastructure as Code, Continuous Integration and Deployment, and Architecture Patterns work in the real world and how these concepts are used in a practical setting.
We will talk about how to use Python with different tools and services to perform machine learning experiments ranging from fully abstracted to fully customized solutions. These include performing automated hyperparameter optimization and bias detection when dealing with intermediate requirements and objectives. We will also show how these are done with different ML libraries and frameworks such as Scikit-learn, PyTorch, TensorfFlow, Keras, MXNet, and more. In addition to these, I will also share some of the risks and common mistakes Machine Learning Engineers must avoid to help bridge the gap between reality and expectations. While discussing these topics, we will show how containerization and serverless engineering helps solve our technical requirements.
While discussing these concepts, tools, frameworks, and techniques, we will provide several examples and recipes on how these ML workflows and systems solve different business requirements (e.g., finance, digital transformation, automation, sales).
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