In near future, you can raise groups of exceptions at a time in Python.

A new grammar feature, except*, will allow multiple except clauses to match and execute.


A single ExceptionGroup can cause several except* clauses to execute, but each such clause executes at most once (for all matching exceptions from the group) and each exception is either handled by exactly one clause (the first one that matches its type) or is reraised at the end.

Metaflow is a human-friendly Python/R library that helps scientists and engineers build and manage real-life data science projects. Metaflow was originally developed at Netflix to boost productivity of data scientists who work on a wide variety of projects from classical statistics to state-of-the-art deep learning.


pip install metaflow


A framework for real-life data science

https://youtu.be/TATmeEFdLuI


This is yesterday’s recorded session


Check resources links in video description


30 AI and Datascience Resources

Everyweek we will be connecting for DataScience Dialogue. We discussed AutoML and DataRobot Demo #2 in this week. I have demonstrated the DataRobot which market leading AutoML tool.

Unicorn - A Magical Full-stack Framework for Django - My Talk #2 https://youtu.be/UlFcUXxYSto

Machine learning steps:


Analyze the problem
Gather the data
Prepare the data
Choose the right model
Train the model
Evaluate the results
Look for biases
Tune it
Deploy the model
Monitor it
Retrain it

Fixing overfitting:

Simplify the model (fewer parameters)
Simplify training data (fewer attributes)
Constrain the model (regularization)
Use cross-validation
Use Early stopping
Build an ensemble
Gather more data

Fixing underfitting:

More complex model (more parameters)
Increase number of features
Feature engineer should help
Unconstrain the model (no regularization)
Reduce noise on the data
Train for longer

The common convolutional neural networks (CNN).

LeNet-5
AlexNet
VGG-16
Inception-v1
Inception-v3
ResNet-50
Xception
Inception-v4
Inception ResNets
ResNext-50

YAML- Introduction, Read and Write YAML files using Python Script - Talk #3 https://youtu.be/XrPwTkyoBok

This is an important configuration file for deployment AI models and DevOps configurations

Free Azure training and Free Voucher for Az-900 (Azure Fundamentals)
https://www.microsoft.com/en-ie/training-days?mkt_tok=eyJpIjoiTWpZeFlqVXlabU13T1RaayIsInQiOiJpelVQcVFlSHZQUWR3YXBBT0dpVGhoVzdyNGJNc3R4TENJdzlaSjZUdlhha2JMdzR5b2JubmRZbnJObVJ1NjB4MHF6WFVEdE5HR2p4OXA5SkFBbmk5dlFlWTl5eDdzUG9LK2lNZE9jSVdsdDI1MnRcL29OZXVhRHduWitIb1RhNjREWmVIUTBnTEx4MVAxWXU5OSs3UGJnPT0ifQ==

Simple multi-dataset detection

Github: https://github.com/xingyizhou/UniDet

Paper: https://arxiv.org/abs/2102.13086v1

Amazon Web Services
◦ Amazon DynamoDB - 25GB NoSQL DB
◦ Amazon Lambda - 1 Million requests per month
◦ Amazon SNS - 1 million publishes per month
◦ Amazon Cloudwatch - 10 custom metrics and 10 alarms
◦ Amazon Glacier - 10GB long-term object storage
◦ Amazon SQS - 1 million messaging queue requests
◦ Amazon CodeBuild - 100min of build time per month
◦ Amazon Code Commit - 5 active users per month
◦ Amazon Code Pipeline - 1 active pipeline per month

IBM Cloud
◦ Cloud Functions - 5 million executions per month
◦ Object Storage - 25GB per month
◦ Cloudant database - 1 GB of data storage
◦ Db2 database - 100MB of data storage
◦ API Connect - 50,000 API calls per month
◦ Availability Monitoring - 3 million data points per month
◦ Log Analysis - 500MB of daily log