Category: Tutorials

System Design: How to Design a Rate Limiter?

What is a Rate Limiter?

Rate limiting refers to preventing the frequency of an operation from exceeding a defined limit. In large-scale systems, rate limiting is commonly used to protect underlying services and resources. In distributed systems, Rate limiting is used as a defensive mechanism to protect the availability of shared resources. It is also used to protect APIs from unintended or malicious overuse by limiting the number of requests that can reach our API in a given period of time.

In this blog, we’ll see how will tackle the question of designing a rate limiter in a system design interview.

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Top Machine Learning Interview Questions for 2024 (Part-1)

 

These Machine Learning Interview Questions, are the real questions that are asked in the top interviews.

For hiring machine learning engineers or data scientists, the typical process has multiple rounds.

  1. A basic screening round – The objective is to check the minimum fitness in this round.
  2. Algorithm Design Round – Some companies have this round but most don’t. This involves checking the coding / algorithmic skills of the interviewee.
  3. ML Case Study – In this round, you are given a case study problem of machine learning on the lines of Kaggle. You have to solve it in an hour.
  4. Bar Raiser / Hiring Manager  – This interview is generally with the most senior person in the team or a very senior person from another team (at Amazon it is called Bar raiser round) who will check if the candidate fits in the company-wide technical capabilities. This is generally the last round.

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Writing Custom Optimizer in TensorFlow Keras API

Recently, I came up with an idea for a new Optimizer (an algorithm for training neural network). In theory, it looked great but when I implemented it and tested it, it didn’t turn out to be good.

Some of my learning are:

  1. Neural Networks are hard to predict.
  2. Figuring out how to customize TensorFlow is hard because the main documentation is messy.
  3. Theory and Practical are two different things. The more hands-on you are, the higher are your chances of trying out an idea and thus iterating faster.

I am sharing my algorithm here. Even though this algorithm may not be of much use to you but it would give you ideas on how to implement your own optimizer using Tensorflow Keras.

A neural network is basically a set of neurons connected to input and output. We need to adjust the connection strengths such that it gives the least error for a given set of input. To adjust the weight we use the algorithms. One brute force algorithm could be to try all possible combinations of weights (connections strength) but that will be too time-consuming. So, we usually use the greedy algorithm most of these are variants of Gradient Descent. In this article, we will write our custom algorithm to train a neural network. In other words, we will learn how to write our own custom optimizer using TensorFlow Keras.

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How to create an Apache Thrift Service – Tutorial

Overview

Say you come up with a wonderful idea such as a really great phone service. You would want this phone service to be available to the APIs in various languages. Whether people are using Python, C++, Java or any other programming language, the users should be able to use your service. Also, you would want the users to be able to access globally. In such scenarios, you should create the Thrift Service. Thrift lets you create a generic interface which can be implemented on the server. The clients of this generic interface can be automatically generated in all kinds of languages.

Let us get started! Here we are going to create a very simple service that just prints the server time.

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Phrase matching using Apache Spark

Recently, a friend whose company is working on large scale project reached out to us to seek a solution to a simple problem of finding a list of phrases (approximately 80,000) in a huge set of rich text documents (approx 6 million).

The problem at first looked simple. The way engineers had solved it is by simply loading the two documents in Apache Spark’s DataFrame and joining those using “like”. Something on these lines:

select phrase.id, docs.id from phrases, docs where docs.txt like ‘%’ + phrases.phrase + ‘%’

But it was taking huge time even on the small subset of the data and processing is done in distributed fashion. Any Guesses, why?

They had also tried to use Apache Spark’s broadcast mechanism on the smaller dataset but still, it was taking a long while finishing even a small task.

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How To Optimise A Neural Network?

When we are solving an industry problem involving neural networks, very often we end up with bad performance. Here are some suggestions on what should be done in order to improve the performance.

Is your model underfitting or overfitting?

You must break down the input data set into two parts – training and test. The general practice is to have 80% for training and 20% for testing.

You should train your neural network with the training set and test with the testing set. This sounds like common sense but we often skip it.

Compare the performance (MSE in case of regression and accuracy/f1/recall/precision in case of classification) of your model with the training set and with the test set.

If it is performing badly for both test and training it is underfitting and if it is performing great for the training set but not test set, it is overfitting.

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Introduction to Apache Flume in 30 minutes

What is Apache Flume?

Apache Flume is a distributed, reliable, and available system for efficiently collecting, aggregating & moving large data from many different sources to a centralized data store.

Flume supports a large variety of sources Including:

  • tail (like unix tail -f),
  • syslog,
  • log4j – allowing Java applications to write logs to HDFS via flume

Flume Nodes

Flume nodes can be arranged in arbitrary topologies.Typically there is a node running on each source machine, with tiers of aggregating nodes that the data flows through on its way to HDFS.

Topics Covered

  • What is Flume
  • Flume: Use Case
  • Flume: Agents
  • Flume: Use Case – Agents
  • Flume: Multiple Agents
  • Flume: Sources
  • Flume: Delivery Reliability
  • Flume: Hands-on

Introduction to Flume Presentation

 

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If you wish to learn Hadoop and Spark technologies such as MapReduce, Hive, HBase, Sqoop, Flume, Oozie, Spark RDD, Spark Streaming, Kafka, Data frames, SparkSQL, SparkR, MLlib, GraphX and build a career in BigData and Spark domain then check out our signature course on Big Data with Apache Spark and Hadoop which comes with

  • Online instructor-led training by professionals having years of experience in building world-class BigData products
  • High-quality learning content including videos and quizzes
  • Automated hands-on assessments
  • 90 days of lab access so that you can learn by doing
  • 24×7 support and forum access to answer all your queries throughout your learning journey
  • Real-world projects
  • A certificate which you can share on LinkedIn

Machine Learning with Mahout

[This blog is from KnowBigData.com. It is pretty old. Many things have changed since then. People have moved to MLLib. We have also moved to CloudxLab.com.]

What is Machine Learning?

Machine Learning is programming computers to optimize a Performance using example data or past experience, it is a branch of Artificial Intelligence.

Types of Machine Learning

Machine learning is broadly categorized into three buckets:

  • Supervised Learning – Using Labeled training data, to create a classifier that can predict the output for unseen inputs.
  • Unsupervised Learning – Using Unlabeled training data to create a function that can predict the output.
  • Semi-Supervised Learning – Make use of unlabeled data for training – typically a small amount of labeled data with a large amount of unlabeled data.

Machine Learning Applications

  • Recommend Friends, Dates, Products to end-user.
  • Classify content into pre-defined groups.
  • Find Similar content based on Object Properties.
  • Identify key topics in large Collections of Text.
  • Detect Anomalies within given data.
  • Ranking Search Results with User Feedback Learning.
  • Classifying DNA sequences.
  • Sentiment Analysis/ Opinion Mining
  • Computer Vision.
  • Natural Language Processing,
  • BioInformatics.
  • Speech and HandWriting Recognition.

Mahout

Mahout – Keeper/Driver of Elephants. Mahout is a Scalable Machine Learning Library built on Hadoop, written in Java and its Driven by Ng et al.’s paper “MapReduce for Machine Learning on Multicore”. Development of Mahout Started as a Lucene sub-project and it became Apache TLP in Apr’10.

Topics Covered

  • Introduction to Machine Learning and Mahout
  • Machine Learning- Types
  • Machine Learning- Applications
  • Machine Learning- Tools
  • Mahout – Recommendation Example
  • Mahout – Use Cases
  • Mahout Live Example
  • Mahout – Other Recommender Algos

Machine Learning with Mahout Presentation

Machine Learning with Mahout Videohttps://www.youtube.com/embed/PZsTLIlSZhI

How to Install Hortonworks Data Platform – HDP 2.6 on AWS

In this post, we will show you how you can install Hortonworks Data Platform on AWS.

You can also watch the video of this tutorial here

 

We start with three machines. We could install Hadoop on these machines by manually downloading and configuring them, but that’s very insufficient. So either we could use Cloudera manager or Ambari. In this tutorial, we are going to use Ambari.

On the first machine, we are going to install the Ambari server. For that, we need to buy these three instances at Amazon and we will follow the Ambari guidelines.

Ambari will then install all the components that are required in other two machines.

Please note, we will use 16 GB ram machines so that installation goes smoothly. 

Let’s get started.

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Streaming Twitter Data using Flume

In this blog post, we will learn how to stream Twitter data using Flume on CloudxLab

For downloading tweets from Twitter, we have to configure Twitter App first.

Create Twitter App

Step 1

Navigate to Twitter app URL and sign in with your Twitter account

Step 2

Click on “Create New App”

Create New App

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