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"Level Up Your Machine Learning Lifecycle" by Yaqi Chen (Strange Loop 2022)
Learn how to level up your machine learning lifecycle with consistency, automation, and human oversight.
- The machine learning lifecycle is like a puzzle with different pieces, including data preparation, model training, and deployment. Each piece is critical in ensuring the success of the model.
- Consistency is key in the machine learning lifecycle, every process should be consistent from data preparation to deployment.
- There is no one-size-fits-all solution to the machine learning lifecycle, different industries and problems require different approaches.
- Automation is a trend in the machine learning lifecycle, it can help simplify the process and make it more efficient.
- Domain knowledge is important in the machine learning lifecycle, it can help data scientists make informed decisions and create more accurate models.
- Human oversight is necessary in the machine learning lifecycle, it can help correct mistakes and ensure that the model is performing as expected.
- Reusability is a key concept in the machine learning lifecycle, models should be designed to be reused in different contexts.
- Experimentation is a critical step in the machine learning lifecycle, it can help data scientists test different approaches and find the best solution.
- Real-world applications are important in the machine learning lifecycle, they can help data scientists test and validate their models.
- Collaboration is essential in the machine learning lifecycle, data scientists should work closely with cross-functional teams to ensure the success of the model.