Testing Python Full Stack & Backend for MC/ ML Engines 101

Name: Testing Python Full Stack & Backend for MC/ ML Engines 101
Rating: 3.8 (10 reviews)

Interview Prep: Running Maintaining Testing and Debugging Python Full Stack and Backend for Monte Carlo Engines 102

Created byShivgan Joshi

Last updated 11/2024

English

What you'll learn

Running Maintaining Testing and Debugging Python Simulation Engines (Monte Carlo)
Intro to Monte Carlo and Machine Engines Simulation Engines
An introductory but 102 Level course with advanced Topics
Use for training remote managerless Python Computational Science Developers

Course content

6 sections • 20 lectures • 43m total length

Introduction to the course and contents2:33
Explore Monte Carlo and machine-learning simulation engines and learn how to maintain, test, and debug them within reliability engineering in a remote managerless environment.
All related courses to do with this one to excel and launch yourself in Remote1:35
Explore five related courses on remote, unsupervised work, testing errors, big data admin, computational framework, and a Python big data refresher to boost your remote developer career.
Working in managerless and Remote settings0:36
Navigate managerless remote work by mastering expectation management and delivery, and sharpen note taking to keep clients satisfied, with a forthcoming section on simulation engine topics.
Back and Front runs2:14
Analyze three run types for financials: back book runs, front book runs, and liquidation runs, using past data for backtesting and simulations, and calibrating and training models for future projections.
Intro to Monte Carlo1:42
Explore Monte Carlo simulations and their inputs, generating thousands of scenarios to project possible company outcomes over five to ten years, with batch runs on slave nodes.
Quiz on Books
Monte Carlo Quiz

Intro to how to find what to do, how to find the runs1:26
Learn to locate the latest run and rerun Monte Carlo engine tests, using back end and front end workflows to handle changed inputs, balance sheets, or portfolios.
Replicating or Re-running an old run2:40
Replicate or rerun an old run by locating its output and applying small deltas, like unemployment rate or holdings, through the ui or driver node.
Mimicking a Proxy clone developers run1:32
Learn to mimic a proxy clone developer's run by identifying the proxy clone roles for full stack and backend, replicating GitHub branches, and reusing established runs.
Running the runs

Getting data once you have the runs1:08
Identify what to do by compiling notes from Wikipedia, Outlook, and network drives to guide Monte Carlo, full stack run, and back end run data with the driver; comparisons follow.
Intro to Input Files Backend & Full Stack1:13
Explore backend and full stack inputs defined by yaml and shell scripts. Compare the django-based frontend ui with the developer workflow for PySpark, Spark, and yaml structures.
Backend Engine runs2:59
Understand backend engine runs for Monte Carlo and ML simulations via shell scripts that invoke Python, PySpark, and YAML inputs, plus driver-node management.
Full stack runs runs1:32
Trace how front-end runs are executed via the full stack proxy runner, the beta runner, using recent graphical user interface settings and input files to reproduce runs.
Getting Outputs Monte Carlo Full Stack by B2:27
Coordinate monte carlo full stack and backend runs, with backend by b and full stack by v, download ui results and load into a PySpark dataframe for aggregation.
Getting Outputs of Monte Carlo Backend Run by V3:00
Access the edge nodes via ssh, authenticate with Kerberos and grid authentication, locate the latest run, and load the snappy files into spark data frames for driver notebook analysis.
How to use the driver2:49
Learn to access the driver node via ssh, verify Kerberos and Cloudera grid authentication, and run notebooks to access backend data and perform full stack comparisons from the driver.
Commands used for driver node notebook2:37
Execute driver node notebook commands to compare two big data frames by schema, unique key, and data, using spark sql to read hive tables and parquet or csv inputs.
Backend Engine runs
Commands used for driver node notebook

Comparing Runs3:52
Learn to compare full stack and backend runs by matching schema, universe, and data; validate releases with grid settings and Monte Carlo results, and diagnose mismatches from typos or randomization.
Running run on a new release - case study of my we need to compare2:50
Compare backend and full-stack releases to ensure development outputs align across the app. The lecture explains release workflows and tooling, including spark submit and YAML for runs.

Requirements

No programming experience needed
You can try the other 101 courses

Description

Interview Prep: Python Full Stack and Backend Engines for MC/ ML Engines 102

Running Maintaining Testing and Debugging Python Full Stack and Backend for Monte Carlo Engines 102

Intro

How to work and success in remote managerless environment
What technical skill are needed: Python shell coding spark df git commands and sshing
Running Maintaining Testing and Debugging Computational engines
Inputs given through yaml
How get old runs information so that you can pull data. What do in case you are stuck
How to handle authentication errors
Execution is through .sh file
Full stack vs Back end engine
How to get the the root of mismatch
What are clone proxy runners how to use their runs
How to make proper notes

How tos:

How to search for an old run
How to see the latest run
How to see the runs that is still in progress
How to start a run

Assignments:

Write step for Getting Outputs of Monte Carlo Backend Run
Backend runs
How to compare two dfs
What are diff type of authentication
What to do if you cannot find the runs
Common causes of mismatch of runs
Give 3 common type of grid run errors / issues
Write sample wiki notes about your findings of attempting to search the runs

Who this course is for:

Intermediate Python for Computational Full Stack engines is for anyone who want to learn more beyong python basics

Testing Python Full Stack & Backend for MC/ ML Engines 101

What you'll learn

Explore related topics

Course content

Introduction5 lectures • 9min

How to find out what to to, how to find your task3 lectures • 6min

Getting data once you have the runs or doing new runs8 lectures • 18min

Comparing runs2 lectures • 7min

Handle Error and Issues1 lecture • 2min

Assignment1 lecture • 2min

Requirements

Description

Who this course is for: