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In this post, we introduce a series of hands-on workshops and associated code artifacts to help you build secure ML environments on top of Amazon SageMaker, a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy ML models quickly. The objective of these workshops is to address the aforementioned challenges by helping bring together different IT stakeholders and data scientists and provide best practices to build and operate secure ML environments. These workshops are a summary of recommended practices from large enterprises and small and medium businesses. You can access these workshops on Building Secure Environments, and you can find the associated code on GitHub. We believe that these workshops are valuable for the following primary teams:

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The workshops and associated code let you implement recommended practices and patterns and help you to quickly get started building secure environments, and improve productivity with the ability to securely build, train, deploy and monitor ML models. Although the workshop is built using SageMaker notebook instances, in this post we highlight how you can adapt this to Amazon SageMaker Studio. Although the workshop is built using SageMaker notebook instances, in this post we highlight how you can adapt this to Amazon SageMaker Studio, the first integrated development environment for machine learning on AWS.

A common pain point that you may face is a lack of recommended practices around code and ML lifecycle traceability. Often, this can arise from data scientists not being trained in MLOps (ML and DevOps) best practices, and the inherent experimental nature of the ML process. In regulated industries such as financial services, regulatory bodies such as the Office of the Comptroller of the Currency (OCC) and Federal Reserve Board (FRB) have documented guidelines on managing the risk of analytical models.

Lack of best practices around documenting the end-to-end ML lifecycle can lead to lost cycles in trying to trace the source code, model hyperparameters, and training data. The following figure shows the different steps in the lineage of a model that may be tracked for traceability and reproducibility reasons.

Traceability refers to the ability to map outputs from one step in the ML cycle to the inputs of another, thereby having a record of the entire lineage of a model. Enforcing data scientists to use source and version control tools such as Git or BitBucket to regularly check in code, and not approve or promote models until code has been checked in, can help mitigate this issue. In this workshop, we provision a private CodeCommit repository for use by data scientists, along with their notebook instance. Admins can tag these repositories to the users, to identify the users responsible for the commits, and ensure code is being frequently checked into source control. One way to do this is to use project-specific branches, and ensure that the branch has been merged with the primary branch in the shared services environment prior to being promoted to pre-production or test. Data scientists should not be allowed to directly promote code from dev to production without this intermediate step.

In addition to versioning code, versioning data used for training models is important as well. All the buckets created in this workshop have versioning automatically enabled to enforce version control on any data stored there, such as training data, processed data, and training, validation and test data. SageMaker Experiments automatically keeps track of the pointer to the specific version of the training data used during model training.

Data scientists often tend to explore data in notebooks, and use notebooks to engineer features as well. In this workshop, we demonstrate how to use SageMaker Processing to not only offload the feature engineering code from the notebook instance onto separate compute instances to run at scale, but also to subsequently track parameters used for engineering features in SageMaker Experiments for reproducibility reasons. SageMaker recently launched SageMaker Clarify, which allows you to detect bias in your data as well as extract feature importances. You can run these jobs as you would run SageMaker Processing jobs using the Clarify SDK.

AWS CodePipeline is a continuous integration and continuous delivery service for fast and reliable application and infrastructure updates. AWS CodePipeline builds, tests, and deploys your code every time there is a code change, based on release process models you define.

  • Implement basic Q-learning through the Deeplizard Frozen Lake tutorial: Install Python 3 and OpenAI Gym on your computer. You may use any IDE, but you can find installation instructions for some in the editable Google doc link I sent by email.

  • Follow the step-by-step deeplizard tutorial through step 10 (short of deep Q-learning).

  • As challenged in the video, tune the learning parameters (without changing num_episodes or max_steps_per_episode) so as to get a consistent success rate above 0.7. It is possible, just by tuning min_exploration_rate and exploration_decay_rate.

  • I'll have you demonstrate the Q-learning and agent performance as shown in the videos.

  • UPDATE:OpenAI Gym now supports a different version of Frozen Lake. This requires a few differences in the tutorial code: env = gym.make("FrozenLake-v0") → env = gym.make("FrozenLake-v1")

  • env.render() → print(env.render("ansi"))

  • Download my Approach-n Environment for OpenAI Gym and unzip it into your OpenAI gym work directory.

  • (You can optionally learn about how to create your own environments by reading this and this along with OpenAI Gym documented open-source code such as gym/

  • To install this into your Python "gym" OpenAI Gym environment, first activate your "gym" Python environment, and then enter the command "pip install -e" from the root directory of this unzipped new environment. See the linked documentation in the previous bullet for more details.

  • Documentation: # Approach n OpenAI Gym EnvironmentThe dice game "Approach n" is played with 2 players and a single standard 6-sided die (d6). The goal is to approach a total of n without exceeding it. The first player roll a die until they either (1) "hold" (i.e. end their turn) with a roll sum less than or equal to n, or (2) exceed n and lose. If the first player holds at exactly n, the first player wins immediately. If the first player holds with less than n, the second player must roll until the second player's roll sum (1) exceeds the first player's roll sum without exceeding n and wins, or (2) exceeds n and loses. Note that the first player is the only player with a choice of play policy. For n >= 10, the game is nearly fair, i.e. can be won approximately half of the time with optimal decisions.- Non-terminal states: Player 1 turn totals less than n (n is a terminal state)- Actions: 0 (HOLD), 1 (ROLL)- Rewards: +1 for transition to terminal state with win, -1 for transition to terminal state with loss, 0 otherwise- Transitions: Each die roll of 1 - 6 is equiprobable

  • This is a simpler environment than Frozen Lake. Read on for differences in how you will approach it:

  • The programming assignments are designed to be run in GNU/Linux environments. Most or all of the grading code may incidentally work on other systems such as MacOS or Windows, and students may optionally choose to do most of their development in one of these alternative environments. However, no technical support will be provided for issues that only arise on an alternative environment. Moreover, no matter what environment is used during development, students must confirm that their code (specifically, the student's runs on Gradescope,. The submitted code will not be graded if it has one of the following issues: The original script (operating on the submitted may not exit normally if you use calls such as quit(), exit(), sys.exit(), and os._exit().Also note that Python packages outside the standard library are not guaranteed to work. This includes packages like numpy, scikit-learn, and pandas.

  • The code reads external resources other than the files given in the assignment. The code is malicious. This is considered a violation of the honor code. The score of the assignment will be zero (0) and the incident will be reported to the Office of Judicial Affairs. Collaboration policy and honor code: You are free to form study groups and discuss homeworks and projects. However, you must write up homeworks and code from scratch independently, and you must acknowledge in your submission all the students you discussed with. The following are considered to be honor code violations: Looking at the writeup or code of another student.

  • Showing your writeup or code to another student.

  • Discussing homework problems in such detail that your solution (writeup or code) is almost identical to another student's answer.

  • Uploading your writeup or code to a public repository (e.g. github, bitbucket, pastebin) so that it can be accessed by other students.

  • Looking at solutions from previous years' homeworks - either official or written up by another student.

When debugging code together, you are only allowed to look at the input-output behavior of each other's programs (so you should write good test cases!). It is important to remember that even if you didn't copy but just gave another student your solution, you are still violating the honor code, so please be careful. We periodically run similarity-detection software over all submitted student programs, including programs from past quarters and any solutions found online on public websites. Anyone violating the honor code will be referred to the Office of Judicial Affairs. If you feel like you made a mistake (it can happen, especially under time pressure!), please reach out to the instructor or the head CA; the consequences will be much less severe than if we approach you. Submission 041b061a72


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