NirantK

def validate_data(predictions, references):
    # Define expected fields and types for predictions and references
    expected_pred_keys = {
        'query': int, 'q0': str, 'docid': str, 'rank': int, 'score': float, 'system': str
    }
    expected_ref_keys = {
        'query': int, 'q0': str, 'docid': str, 'rel': int
    }

NirantK

## Recommended Imports
from qdrant_client import QdrantClient
from qdrant_client.models import (
    Distance,
    NamedSparseVector,
    NamedVector,
    SparseVector,
    PointStruct,
    SearchRequest,
    SparseIndexParams,

NirantK

NirantK

NirantK

import codecs
import functools
import heapq
import io
import mimetypes
import operator
import random
import re
import subprocess
import tempfile

NirantK

• Focus Areas
  • Metrics
    • Ability to understand how does a machine learning model performance tie back to product performance e.g. does reducing ML error improve NPS? Retention?
  • Errors
    • Candidate should be able to navigate a confusion matrix, without being confused
  • Experimentation
    • Most PMs coming from the feature/problem solving nature of the universe are often fumbling around when they realise that experiments have high failure rate — even after everything worked on your local tests
    • The ask here is not for patience, but for the ability of the PM to call out/rank different errors. Here is an incomplete example:
      • Credit card fraud: Is it okay if you blocked emergency medical payment while on travel?
• Payments in new geographies often get blocked

NirantK

#!/usr/bin/env bash

#
# Sets the GOPATH and GOBIN to the current directory.
# Adds GOBIN to the PATH.
#
# Usage:
# 1. Put this script somewhere on your PATH.
# 2. Enter directory of a golang project (contains src, pkg, bin)
# 3. Execute: . gopath

NirantK

# If you feel you must use lambdas, they may be used inside of parentheses:
print(f'{(lambda x: x*3)(3)}')
# '9'

# not that this returned a <str> and not <int>

NirantK

items = list(range(0, 5)) # create list of 5 elements
print(items)
# [0, 1, 2, 3, 4]

print(f'number of items: {len(items)}')
# number of items: 5

NirantK

correct = 'correct'
phonetic_correct = 'phonetic_correct'
typo = 'typo'
phonetic_typo = 'phonetic_typo'
phonetic_distance = 'phonetic_distance'

print(f'No Spacing:')
print(f'{correct}|{phonetic_correct}|{typo}|{phonetic_typo}|{phonetic_distance}|\n')
# No Spacing:
# correct|phonetic_correct|typo|phonetic_typo|phonetic_distance|