codingnirvana

object scala {
   val version = "SCALA_VERSION$"
}

val xml =         <dependencies>
            <dependency>                
                <groupId>org.scalanlp</groupId>
                <artifactId>scalala_${scala.version}</artifactId>
                <version>0.3.1</version>
            </dependency>

codingnirvana

object SampleClusterMember {

  def main(args: Array[String]): Unit = {
    val config = ConfigFactory.parseString("""
     akka {
       actor {
         provider = "akka.cluster.ClusterActorRefProvider"
       }

       remote {

codingnirvana

object Foo extends App{
  val TAKE_COUNT = 5

  def filterFirstFive(xs: List[Int]) = xs.take(TAKE_COUNT)
}

object FooSpec extends App {
  assert(Foo.filterFirstFive(List(1,2,3,4,5,6)).size == 5)
}

codingnirvana

# Ubuntu 16.04
# Forked from tonyzhu/ubuntu_setup.sh
#
# Requirements:
# sudo apt-get install curl -y
#
# Post install if you hadn't done so:
# 1. Install Dropbox Daemon

# Add all third party repositories

codingnirvana

_ = require('underscore'),
requestLibrary = require('request');

module.exports = {

  getProjectedQcuh : function(qcuh){
    var today = new Date();
    var dayOfMonth = today.getDate();
    var daysInCurrentMonth =  new Date(today.getFullYear(), today.getMonth()+1, 0).getDate();
    return ((qcuh * 1.0)/dayOfMonth) * daysInCurrentMonth;

codingnirvana

On the seasonablity example, we have currently only one model in production that is built using predictive modeling and that explicitly takes seasonality into account. Slide #10 mentions it - left column, item #4.

We use the following techniques to minimize the deterioration of models in production.

Amount of New data vs Data used to generate the training data

We use lambda architecture at Indix and our batch pipelines usually run every week. Lets assume we built a classifier model on Day 1. These pipelines use a prediction cache so that we can reuse the scoring output for already seen data. I did not mention this in my talk but for the deployment in batch mode, the prediction cache, we call it IB (information base) internally, is an artifact in addition to the model container. Now lets assume the batch pipelines runs on Day 1, Day 8, Day 15 etc. and the model and prediction cache being used is from Day 1. For every run we monitor the number of predictions that did not come from the cache. If that n

codingnirvana

codingnirvana

// SPDX-License-Identifier: MIT

pragma solidity ^0.8.3;

import "@openzeppelin/contracts/utils/math/SafeMath.sol";
import "@openzeppelin/contracts/token/ERC20/IERC20.sol";
import "@openzeppelin/contracts/security/ReentrancyGuard.sol";
import "@openzeppelin/contracts/utils/Counters.sol";

codingnirvana

// SPDX-License-Identifier: MIT

pragma solidity ^0.8.3;

import "@openzeppelin/contracts/utils/math/SafeMath.sol";
import "@openzeppelin/contracts/token/ERC20/IERC20.sol";
import "@openzeppelin/contracts/security/ReentrancyGuard.sol";
import "@openzeppelin/contracts/utils/Counters.sol";

codingnirvana

// SPDX-License-Identifier: MIT
pragma solidity ^0.8.13;

contract PiggyBank {
    address child;
    address owner;
    uint maxWithdrawAmount;

    constructor(address _child, uint _maxWithdrawAmount) {
        owner = msg.sender;