#6 Machine Learning Engineering for Production (MLOps) Specialization [Course 1, Week 1, Lesson 6] | Summary and Q&A

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April 20, 2022
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#6 Machine Learning Engineering for Production (MLOps) Specialization [Course 1, Week 1, Lesson 6]

TL;DR

Learn about different deployment patterns for learning algorithms, including shadow mode, canary deployment, and blue-green deployment, and the importance of monitoring the system.

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Key Insights

  • 💚 Different deployment patterns, such as shadow mode, canary deployment, and blue-green deployment, offer flexibility in deploying learning algorithms.
  • 🚥 Gradually ramping up traffic and monitoring are common themes in deployment strategies to ensure the algorithm's performance and minimize risks.
  • 🔁 Choosing the appropriate degree of automation, from human-in-the-loop deployments to full automation, depends on the application and performance of the system.
  • 🖐️ Monitoring plays a vital role in spotting problems and addressing them in a timely manner.
  • 🤗 Machine learning ops tools can assist in implementing deployment patterns, but careful design and consideration are necessary.
  • 🈸 Consumer internet applications often require full automation, while other applications may benefit from human-in-the-loop deployments.
  • ❓ Human judgments can provide valuable data for training and improving algorithms, especially in partial automation scenarios.

Transcript

when you've trained the learning algorithm the best way to deploy it is usually not to just turn it on and hope for the best because well what if something goes wrong when deploying systems there are a number of common use cases or types of use cases as well as different patterns for how you deploy depending on your use case let's go through that i... Read More

Questions & Answers

Q: What is shadow mode deployment, and how does it help in verifying the performance of a learning algorithm?

Shadow mode deployment involves running the learning algorithm alongside a human inspector, gathering data to compare the algorithm's predictions with human judgments. This allows for verification of the algorithm's accuracy before making real decisions.

Q: How does canary deployment minimize the impact of any mistakes made by the learning algorithm?

Canary deployment involves gradually ramping up the traffic to the learning algorithm, starting with a small fraction. By doing so, any mistakes made by the algorithm will only affect a small percentage of the traffic, allowing for monitoring and increasing confidence before scaling up.

Q: What is blue-green deployment, and how does it enable rollback?

Blue-green deployment involves having an old version (blue) and a new version (green) of the algorithm. The traffic is switched from the old version to the new version. If something goes wrong, it's easy to roll back by reconfiguring the router to send traffic back to the old version.

Q: Why is monitoring important in deploying learning algorithms?

Monitoring allows the detection of any issues or problems with the learning algorithm's performance. It helps in identifying and addressing these problems early on before they have significant consequences.

Summary & Key Takeaways

  • There are different deployment patterns for learning algorithms depending on the use case: offering a new product, automating or assisting with existing tasks, or replacing an older implementation.

  • Shadow mode deployment involves running the learning algorithm in parallel with a human inspector to gather data and verify its performance.

  • Canary deployment gradually ramps up the traffic to the learning algorithm, allowing for monitoring and reducing the impact of any mistakes.

  • Blue-green deployment switches the traffic from the old version to the new version of the algorithm, with the ability to easily roll back if something goes wrong.

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