#5 Machine Learning Engineering for Production (MLOps) Specialization [Course 1, Week 1, Lesson 5] | Summary and Q&A

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April 20, 2022
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#5 Machine Learning Engineering for Production (MLOps) Specialization [Course 1, Week 1, Lesson 5]

TL;DR

Deploying machine learning models involves addressing challenges in concept drift, data drift, and software engineering for successful deployment.

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Key Insights

  • πŸ’± Concept drift refers to changes in the desired mapping from input data to output, while data drift relates to changes in the distribution of input data.
  • πŸͺ‘ Changes in data can occur gradually or suddenly, and both types of drift need to be addressed to maintain system performance.
  • πŸ˜Άβ€πŸŒ«οΈ Software engineering choices for deployment include deciding between real-time and batch predictions, determining the deployment location (cloud, edge, or web browser), considering compute resources, latency, throughput, logging, security, and privacy.

Transcript

one of the most exciting moments of any machine learning project is when you get to deploy your model but what makes deployment hard i think there are two major categories of challenges in deploying a machine learning model first are the machine learning or the statistical issues and second are the software engine issues let's step through both of ... Read More

Questions & Answers

Q: What is concept drift and how does it impact machine learning deployment?

Concept drift refers to changes in the desired mapping from input data to output. For example, sudden shifts in purchase patterns during a pandemic can cause existing anti-fraud systems to fail. To address concept drift, machine learning teams may need to collect new data and retrain models to adapt to the new distribution.

Q: What is data drift and why is it important in machine learning deployment?

Data drift refers to changes in the distribution of input data while the desired mapping remains the same. In cases like changes in lighting conditions for smartphone inspection or the introduction of new models with different microphones for speech recognition, the performance of a machine learning system can degrade. It is crucial to be able to detect and manage data drift to maintain system performance.

Q: What are some software engineering considerations in machine learning deployment?

When deploying a machine learning model, decisions need to be made regarding real-time or batch predictions, running the system in the cloud or at the edge, available compute resources, latency, and throughput requirements. Additionally, logging data for analysis and retraining, as well as ensuring appropriate security and privacy measures, are essential considerations.

Q: How does the deployment process change after the initial deployment of a machine learning model?

The first deployment is just the beginning of the process. After deployment, there is ongoing work in maintaining and updating the model, especially in the face of concept drift and data drift. This involves continuously monitoring system performance, gathering feedback data, and updating the model as necessary.

Summary & Key Takeaways

  • Deploying machine learning models involves managing concept drift and data drift, which refer to changes in the desired mapping from x to y and changes in the distribution of input x, respectively.

  • Concept drift can occur when definitions or mappings change, such as in the case of a sudden shift in purchase patterns due to a pandemic.

  • Data drift refers to changes in the distribution of input data, which can affect the performance of a machine learning system.

  • Successful deployment also involves addressing software engineering issues, such as deciding between real-time or batch predictions, determining whether the model runs in the cloud or at the edge, and considering compute resources, latency, throughput, logging, security, and privacy.

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