Comments (2)
0:00:00 - Delay over: Answering Marcus' Q on managing app/container access to S3.
0:00:37 - Securely manage permissions with AWS EC2.
0:01:33 - Secure EC2 instance with admin permissions.
0:02:21 - Securely access resources with EC2, S3, and Kubernetes.
0:03:04 - Deploying to Kubernetes with AWS.
0:03:45 - Securely deploy bots with roles.
0:04:25 - Deploy docker container to Kubernetes for prod use.
0:05:04 - Collecting metrics from Flask app.
0:05:47 - Deploy ML models with ease.
0:06:35 - Deploy web service with EC2, but watch traffic.
0:07:22 - Automatically scale up/down with services.
0:08:09 - Deploy app with container service for auto-restart.
0:08:54 - Deploy container with auto-scaling.
0:09:37 - Python back-end development: pipen vs conda.
0:10:20 - Many options for back-end dev, conda or pip.
0:11:10 - Install conda for context on applications.
0:11:49 - Conda has advantages, e.g. faster numpy.
0:12:39 - Using both Conda and Anaconda.
0:13:31 - Creating a folder and installing Circuit Learn.
0:14:18 - Creating ML Flow, Prefect, and Python 3.8 environments.
0:15:06 - Create Python 3.8 environment for project.
0:15:45 - Testing python library in Windows.
0:16:29 - Python 3.8 and 3.9 in conda env.
0:17:42 - Create virtual environment with Python 3.9.
0:18:28 - Using conda and pip to set up local python environment.
0:19:07 - Use Python 3.10 with PIP and PATH.
0:19:55 - Create conda environment for Python 3.10.
0:20:49 - Installing Python 3.9 and activating environment.
0:21:28 - Python web serving alternatives explored.
0:22:20 - Parallel processes, sync IO, Flask, FastAPI to bypass restrictions.
0:23:09 - Scaling up applications with Kubernetes.
0:23:58 - Programmatically set run id in MLFlow.
0:24:40 - Get run ID for model in staging/prod.
0:25:27 - Build docker, run python server.y, get run id.
0:26:03 - Build model in docker image for reliability.
0:26:44 - Advantages of removing dependencies.
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Updated timecodes. Thank you!
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Related Issues (20)
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