The Hitchhiker's Guide to the Machine Learning Engineering Galaxy

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Are you a Software Engineer who got tasked to deploy a machine learning or deep learning model for the first time in your life? Are you wondering what steps to take and how AI-powered software is different from traditional software? Then it is the right workshop to attend.


The internet offers thousands of articles and free of charge courses, showing how it is easy to train and deploy a simple AI model. At the same time in reality it is difficult to integrate a real model into the current infrastructure, debug, test, deploy, and monitor it properly. In this workshop, I will guide you through this process sharing tips, tricks, and favorite open source tools that will make your life much easier. So, at the end of the workshop, you will know where to start your deployment journey, what tools to use, and what questions to ask.

This workshop has been presented at ML conf EU 2020, check out the latest edition of this Tech Conference.

FAQ

The MLConf conference is organized to bring together professionals and enthusiasts in the field of machine learning to share insights, learn from each other, and explore the latest developments in the industry.

Workshops at the MLConf conference are spread over the entire week, allowing attendees to watch conference talks and join or rewatch webinar videos at their own pace.

The workshop covers the theory behind AI-powered software, differences from traditional software, deployment strategies, MLOps, AI pipelines, machine learning platforms, and hands-on experience with AI pipelines using pseudocode.

The speaker is an AI and Data Engineering Lead at a Dutch consulting company named Linked and a volunteer for nonprofit organizations like Women in Artificial Intelligence and PyLadies Amsterdam chapter.

AI-powered software involves additional complexities such as versioning code, data, and models, whereas traditional software typically involves only versioning code. AI software also requires different deployment strategies and monitoring techniques.

MLOps is important because it combines DevOps practices with additional considerations for AI models, such as versioning data and models, monitoring model performance, and automating pipelines for model training, deployment, and retraining.

Versioning data and models is crucial for tracking changes, understanding the impact of new data on model performance, and ensuring reproducibility. It helps in managing the lifecycle of AI models and maintaining their reliability.

The two main types are offline serving (batch mode) and online serving (real-time or near real-time). Offline serving involves scheduled jobs for processing data and storing predictions, while online serving involves real-time predictions through APIs.

Flask is a Python-based framework used to create RESTful APIs for online model deployment. It helps in exposing machine learning models as web services, enabling real-time predictions.

Recommended tools and libraries include Conda for environment management, DVC for data versioning, Packarderm for data lineage and pipeline management, MLflow for experiment tracking and deployment, Selden core for deploying models as microservices, and TensorFlow Extended for end-to-end ML pipelines.

Alyona Galyeva
Alyona Galyeva
112 min
19 Jul, 2021

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Video Summary and Transcription
The video workshop explores the difference between AI-powered software and traditional software, emphasizing the importance of MLOps and AI pipelines. It covers tools for ML deployments, such as FB Learner, Azure Machine Learning, and Amazon SageMaker. The Babelfish concept is introduced for navigating the multitude of tools available. The workshop discusses various machine learning tasks, including classification, regression, and sequence prediction. Key skills for an ML engineer include Bash, Python, and Docker knowledge. The video also highlights the importance of data versioning and tools like MLflow and Kubeflow. It emphasizes the need for model evaluation and validation, and the use of Flask for deploying models via RESTful APIs. The workshop also mentions the importance of communication skills and understanding the bottlenecks in the system for ML engineers. The video concludes by recommending resources like 'Papers with Code' and conferences such as Pi Data for further learning.
Video transcription and chapters available for users with access.

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