Low Touch Machine Learning with Leah McGuire (Salesforce)

Oct 30, 2017Download as PPTX, PDF2 likes1,260 views

Leveraging your data to make better decisions is something every business wants to do, but doing it correctly involves weeks or months of work from highly skilled, hard to find individuals. However, many of the laborious steps a machine learning specialist follows in creating a custom application can be automated with enough compute power and flexibility. By leveraging Spark to do machine learning at scale, Salesforce Einstein has created a system that lets individuals with domain knowledge, but no machine learning expertise, create high quality, high impact machine learning applications.

Low touch machine learning
lmcguire@salesforce.com, @leahmmcguire
Leah McGuire, Principal Member of Technical Staff

AI is taking over the world!
Not without a LOT of
work from humans 
Or so they say, but…

Anew approach toMLwithSalesforce Einstein
Einstein
Auto-ML
AI for CRM

Real life ML takes time and people to build
Score and Update
Models
Deploy and
Operationalize Models
Model Training
Feature Engineering
Model Evaluation

Hand made can be beautiful…
But maybe we don’t need it for every model

What can we make easier?
Score and Update
Models
Deploy and
Operationalize Models
Model Training
Feature Engineering
Model Evaluation

First things first
How do we get good features? + Metadata =

Score and Update
Models
Deploy and
Operationalize Models
Model Training
Feature Engineering
Model Evaluation
What can we make easier?

They put what in their data?!?
Sanity checking

So that is what we built, next question is HOW

Turns out there are a couple machine learning tools

Not all tools cover all parts of this diagram
Score and Update
Models
Deploy and
Operationalize Models
Model Training
Feature Engineering
Model Evaluation

And don’t forget to support ETL and Real Time Scoring…
ETL
Score and Update
Models
Deploy and
Operationalize Models
Model Training
Feature Engineering
Model Evaluation
Score in Real
Time

And a couple solutions for ETL… and a couple
solutions for streaming

Spark is great
• SCALES!
• Good ML libraries
• Good for ETL
• Spark Streaming
• Incredibly healthy and active user base
• Scala + Spark =
One thing appeared on both slides…

Spark covers the whole process
ETL
Score and Update
Models
Deploy and
Operationalize Models
Model Training
Feature Engineering
Model Evaluation
Score in Real
Time

Apps deployed in production
(completely hands-free!!)

Time taken to build a predictive app
Months -> Hours = ML for everyone

Low Touch Machine Learning with Leah McGuire (Salesforce)

Plamen Tenev created a mobile application called 0700 that allows users to call customer service for free by dialing a free number. The initial version was basic but future versions included categories based on user feedback. The app utilizes a client-server architecture for quick updates and maintenance. While some reviews were positive, others were negative, but the app aims to leverage users' help going forward.

Feature Hashing for Scalable Machine Learning with Nick PentreathSpark Summit

Feature hashing is a powerful technique for handling high-dimensional features in machine learning. It is fast, simple, memory-efficient, and well suited to online learning scenarios. While an approximation, it has surprisingly low accuracy tradeoffs in many machine learning problems. Feature hashing has been made somewhat popular by libraries such as Vowpal Wabbit and scikit-learn. In Spark MLlib, it is mostly used for text features; however, its use cases extend more broadly. Many Spark users are not familiar with the ways in which feature hashing might be applied to their problems. In this talk, I will cover the basics of feature hashing, and how to use it for all feature types in machine learning. I will also introduce a more flexible and powerful feature hashing transformer for use within Spark ML pipelines. Finally, I will explore the performance and scalability tradeoffs of feature hashing on various datasets.

Accelerating Shuffle: A Tailor-Made RDMA Solution for Apache Spark with Yuval...Spark Summit

The opportunity in accelerating Spark by improving its network data transfer facilities has been under much debate in the last few years. RDMA (remote direct memory access) is a network acceleration technology that is very prominent in the HPC (high-performance computing) world, but has not yet made its way to mainstream Apache Spark. Proper implementation of RDMA in network-oriented applications can improve scalability, throughput, latency and CPU utilization. In this talk we are going to present a new RDMA solution for Apache Spark that shows amazing improvements in multiple Spark use cases. The solution is under development in our labs, and is going to be released to the public as an open-source plug-in.

Building Machine Learning Algorithms on Apache Spark with William BentonSpark Summit

There are lots of reasons why you might want to implement your own machine learning algorithms on Spark: you might want to experiment with a new idea, try and reproduce results from a recent research paper, or simply to use an existing technique that isn’t implemented in MLlib. In this talk, we’ll walk through the process of developing a new machine learning model for Spark. We’ll start with the basics, by considering how we’d design a parallel implementation of a particular unsupervised learning technique. The bulk of the talk will focus on the details you need to know to turn an algorithm design into an efficient parallel implementation on Spark: we’ll start by reviewing a simple RDD-based implementation, show some improvements, point out some pitfalls to avoid, and iteratively extend our implementation to support contemporary Spark features like ML Pipelines and structured query processing. You’ll leave this talk with everything you need to build a new machine learning technique that runs on Spark.

Experimental Design for Distributed Machine Learning with Myles BakerDatabricks

This document discusses experimental design for distributed machine learning models. It outlines common problems in machine learning modeling like selecting the best algorithm and evaluating a model's expected generalization error. It describes steps in a machine learning study like collecting data, building models, and designing experiments. The goal of experimentation is to understand how model factors affect outcomes and obtain statistically significant conclusions. Techniques discussed for analyzing distributed model outputs include precision-recall curves, confusion matrices, and hypothesis testing methods like the chi-squared test and McNemar's test. The document emphasizes that experimental design for distributed learning poses new challenges around data characteristics, computational complexity, and reproducing results across models.

Art of Feature Engineering for Data Science with Nabeel SarwarSpark Summit

No More Cumbersomeness: Automatic Predictive Modeling on Apache Spark Marcin ...Spark Summit

Building accurate machine learning models has been an art of data scientists, i.e., algorithm selection, hyper parameter tuning, feature selection and so on. Recently, challenges to breakthrough this “black-arts” have got started. In cooperation with our partner, NEC Laboratories America, we have developed a Spark-based automatic predictive modeling system. The system automatically searches the best algorithm, parameters and features without any manual work. In this talk, we will share how the automation system is designed to exploit attractive advantages of Spark. The evaluation with real open data demonstrates that our system can explore hundreds of predictive models and discovers the most accurate ones in minutes on a Ultra High Density Server, which employs 272 CPU cores, 2TB memory and 17TB SSD in 3U chassis. We will also share open challenges to learn such a massive amount of models on Spark, particularly from reliability and stability standpoints. This talk will cover the presentation already shown on Spark Summit SF’17 (#SFds5) but from more technical perspective.

How to Share State Across Multiple Apache Spark Jobs using Apache Ignite with...Spark Summit

Attend this session to learn how to easily share state in-memory across multiple Spark jobs, either within the same application or between different Spark applications using an implementation of the Spark RDD abstraction provided in Apache Ignite. During the talk, attendees will learn in detail how IgniteRDD – an implementation of native Spark RDD and DataFrame APIs – shares the state of the RDD across other Spark jobs, applications and workers. Examples will show how IgniteRDD, with its advanced in-memory indexing capabilities, allows execution of SQL queries many times faster than native Spark RDDs or Data Frames.

Improving Traffic Prediction Using Weather Data with Ramya RaghavendraSpark Summit

As common sense would suggest, weather has a definite impact on traffic. But how much? And under what circumstances? Can we improve traffic (congestion) prediction given weather data? Predictive traffic is envisioned to significantly impact how driver’s plan their day by alerting users before they travel, find the best times to travel, and over time, learn from new IoT data such as road conditions, incidents, etc. This talk will cover the traffic prediction work conducted jointly by IBM and the traffic data provider. As a part of this work, we conducted a case study over five large metropolitans in the US, 2.58 billion traffic records and 262 million weather records, to quantify the boost in accuracy of traffic prediction using weather data. We will provide an overview of our lambda architecture with Apache Spark being used to build prediction models with weather and traffic data, and Spark Streaming used to score the model and provide real-time traffic predictions. This talk will also cover a suite of extensions to Spark to analyze geospatial and temporal patterns in traffic and weather data, as well as the suite of machine learning algorithms that were used with Spark framework. Initial results of this work were presented at the National Association of Broadcasters meeting in Las Vegas in April 2017, and there is work to scale the system to provide predictions in over a 100 cities. Audience will learn about our experience scaling using Spark in offline and streaming mode, building statistical and deep-learning pipelines with Spark, and techniques to work with geospatial and time-series data.

A Tale of Two Graph Frameworks on Spark: GraphFrames and Tinkerpop OLAP Artem...Spark Summit

Graph is on the rise and it’s time to start learning about scalable graph analytics! In this session we will go over two Spark-based Graph Analytics frameworks: Tinkerpop and GraphFrames. While both frameworks can express very similar traversals, they have different performance characteristics and APIs. In this Deep-Dive by example presentation, we will demonstrate some common traversals and explain how, at a Spark level, each traversal is actually computed under the hood! Learn both the fluent Gremlin API as well as the powerful GraphFrame Motif api as we show examples of both simultaneously. No need to be familiar with Graphs or Spark for this presentation as we’ll be explaining everything from the ground up!

Indicium: Interactive Querying at Scale Using Apache Spark, Zeppelin, and Spa...Spark Summit

This document discusses Indicium, a system for enabling interactive querying at scale on Spark. It describes a unified data platform using Spark for storing and processing data, as well as enabling scheduled jobs, BI reports, and real-time lookups. It then discusses two parts of Indicium: (1) a managed context pool using Apache Zeppelin and Spark Job Server for multi-user SQL queries, and (2) a smart query scheduler for dynamic scheduling, load balancing, and high availability of queries. The smart scheduler aims to improve on the managed context pool by addressing issues like FIFO scheduling and lack of failure handling.

MMLSpark: Lessons from Building a SparkML-Compatible Machine Learning Library...Spark Summit

With the rapid growth of available datasets, it is imperative to have good tools for extracting insight from big data. The Spark ML library has excellent support for performing at-scale data processing and machine learning experiments, but more often than not, Data Scientists find themselves struggling with issues such as: low level data manipulation, lack of support for image processing, text analytics and deep learning, as well as the inability to use Spark alongside other popular machine learning libraries. To address these pain points, Microsoft recently released The Microsoft Machine Learning Library for Apache Spark (MMLSpark), an open-source machine learning library built on top of SparkML that seeks to simplify the data science process and integrate SparkML Pipelines with deep learning and computer vision libraries such as the Microsoft Cognitive Toolkit (CNTK) and OpenCV. With MMLSpark, Data Scientists can build models with 1/10th of the code through Pipeline objects that compose seamlessly with other parts of the SparkML ecosystem. In this session, we explore some of the main lessons learned from building MMLSpark. Join us if you would like to know how to extend Pipelines to ensure seamless integration with SparkML, how to auto-generate Python and R wrappers from Scala Transformers and Estimators, how to integrate and use previously non-distributed libraries in a distributed manner and how to efficiently deploy a Spark library across multiple platforms.

One-Pass Data Science In Apache Spark With Generative T-Digests with Erik Erl...Spark Summit

This document discusses using T-digests and generative sampling techniques for data science applications in Apache Spark. It introduces T-digests as a way to compute quantiles from streaming or large datasets in a compact sketch. T-digests can be used for feature importance calculations and generative sampling via inverse transform sampling. The document demonstrates using T-digests for random forest clustering, feature reduction, and calculating feature importance in Spark. It concludes with a demo of these techniques on toxicity prediction data.

VEGAS: The Missing Matplotlib for Scala/Apache Spark with Roger Menezes and D...Spark Summit

In this talk, we’ll present techniques for visualizing large scale machine learning systems in Spark. These are techniques that are employed by Netflix to understand and refine the machine learning models behind Netflix’s famous recommender systems that are used to personalize the Netflix experience for their 99 millions members around the world. Essential to these techniques is Vegas, a new OSS Scala library that aims to be the “missing MatPlotLib” for Spark/Scala. We’ll talk about the design of Vegas and its usage in Scala notebooks to visualize Machine Learning Models.

Working with Skewed Data: The Iterative Broadcast with Fokko Driesprong Rob K...Spark Summit

Skewed data is the enemy when joining tables using Spark. It shuffles a large proportion of the data onto a few overloaded nodes, bottlenecking Spark’s parallelism and resulting in out of memory errors. The go-to answer is to use broadcast joins; leaving the large, skewed dataset in place and transmitting a smaller table to every machine in the cluster for joining. But what happens when your second table is too large to broadcast, and does not fit into memory? Or even worse, when a single key is bigger than the total size of your executor? Firstly, we will give an introduction into the problem. Secondly, the current ways of fighting the problem will be explained, including why these solutions are limited. Finally, we will demonstrate a new technique – the iterative broadcast join – developed while processing ING Bank’s global transaction data. This technique, implemented on top of the Spark SQL API, allows multiple large and highly skewed datasets to be joined successfully, while retaining a high level of parallelism. This is something that is not possible with existing Spark join types.

Natural Language Understanding at Scale with Spark-Native NLP, Spark ML, and ...Spark Summit

Natural language processing is a key component in many data science systems that must understand or reason about text. Common use cases include question answering, paraphrasing or summarization, sentiment analysis, natural language BI, language modeling, and disambiguation. Building such systems usually requires combining three types of software libraries: NLP annotation frameworks, machine learning frameworks, and deep learning frameworks. Ideally, all three of these pieces should be able to be integrated into a single workflow. This makes development, experimentation, and deploying results much easier. Spark’s MLlib provides a number of machine learning algorithms, and now there are also projects making deep learning achievable in MLlib pipelines. All we need is the NLP annotation frameworks. SparkNLP adds NLP annotations into the MLlib ecosystem. This talk will introduce SparkNLP: how to use it, its current functionality, and where it is going in the future.

Histogram Equalized Heat Maps from Log Data via Apache Spark with Arvind RaoSpark Summit

Reverse geocoding is one of HERE Technologies most heavily used services. From its access logs geocodes can be extracted and then counted with respect to some cellulation of the earth–-creating a sparse heat map. For our Place & Address Search products we use such a heat map to define a notion of relative place importance to rank and index addresses and places. However, the large data size, sparsity, and variations in traffic from which different global heat maps may be derived, makes faithful visualization and comparison a challenge. Additionally, common implementations of spatial image processing techniques that can help address the aforementioned challenges don’t map directly onto Spark’s computing engine. In this talk Arvind Rao will describe implementations of histogram equalization and kernel-based sparse image processing methods on Spark. Histogram equalization, which is best known as a method of contrast enhancement, automatically normalizes images, facilitating comparison. Along the way, Arvind will talk about how HERE uses heat maps as a feature in their autocompletion service, and say just enough about perception of contrast to put histogram equalization in context.

Handling Data Skew Adaptively In Spark Using Dynamic RepartitioningSpark Summit

This document discusses handling data skew in Spark using dynamic repartitioning. It presents an architecture for dynamic repartitioning that collects data characteristics during job execution, decides when repartitioning is needed based on those characteristics, and constructs a new hash function to redistribute the data more uniformly. It aims to mitigate the problem of slow tasks caused by skewed data distributions. Benchmark results show the approach can significantly reduce the size of the largest partition and execution time for jobs operating on skewed data.

Apache Spark—Apache HBase Connector: Feature Rich and Efficient Access to HBa...Spark Summit

Both Spark and HBase are widely used, but how to use them together with high performance and simplicity is a very challenging topic. Spark HBase Connector(SHC) provides feature rich and efficient access to HBase through Spark SQL. It bridges the gap between the simple HBase key value store and complex relational SQL queries and enables users to perform complex data analytics on top of HBase using Spark. SHC implements the standard Spark data source APIs, and leverages the Spark catalyst engine for query optimization. To achieve high performance, SHC constructs the RDD from scratch instead of using the standard HadoopRDD. With the customized RDD, all critical techniques can be applied and fully implemented, such as partition pruning, column pruning, predicate pushdown and data locality. The design makes the maintenance easy, while achieving a good tradeoff between performance and simplicity. In addition to fully supporting all the Avro schemas natively, SHC has also integrated natively with Phoenix data types. With SHC, Spark can execute batch jobs to read/write data from/into Phoenix tables. Phoenix can also read/write data from/into HBase tables created by SHC. For example, users can run a complex SQL query on top of an HBase table created by Phoenix inside Spark, perform a table join against an Dataframe which reads the data from a Hive table, or integrate with Spark Streaming to implement a more complicated system. In this talk, apart from explaining why SHC is of great use, we will also demo how SHC works, how to use SHC in secure/non-secure clusters, how SHC works with multiple secure HBase clusters, etc. This talk will also benefit people who use Spark and other data sources (besides HBase) as it inspires them with ideas of how to support high performance data source access at the Spark DataFrame level.

Apache Spark and Tensorflow as a Service with Jim DowlingSpark Summit

In Sweden, from the Rise ICE Data Center at www.hops.site, we are providing to reseachers both Spark-as-a-Service and, more recently, Tensorflow-as-a-Service as part of the Hops platform. In this talk, we examine the different ways in which Tensorflow can be included in Spark workflows, from batch to streaming to structured streaming applications. We will analyse the different frameworks for integrating Spark with Tensorflow, from Tensorframes to TensorflowOnSpark to Databrick’s Deep Learning Pipelines. We introduce the different programming models supported and highlight the importance of cluster support for managing different versions of python libraries on behalf of users. We will also present cluster management support for sharing GPUs, including Mesos and YARN (in Hops Hadoop). Finally, we will perform a live demonstration of training and inference for a TensorflowOnSpark application written on Jupyter that can read data from either HDFS or Kafka, transform the data in Spark, and train a deep neural network on Tensorflow. We will show how to debug the application using both Spark UI and Tensorboard, and how to examine logs and monitor training.

Optimal Strategies for Large Scale Batch ETL Jobs with Emma TangDatabricks

The ad tech industry processes large volumes of pixel and server-to-server data for each online user’s click, impression, and conversion data. At Neustar, we process 10+ billion events per day, and all of our events are fed through a number of Spark ETL batch jobs. Many of our Spark jobs process over 100 terabytes of data per run, each job runs to completion in around 3.5 hours. This means we needed to optimize our jobs in specific ways to achieve massive parallelization while keeping the memory usage (and cost) as low as possible. Our talk is focused on strategies dealing with extremely large data. We will talk about the things we learned and the mistakes we made. This includes: – Optimizing memory usage using Ganglia – Optimizing partition counts for different types of stages and effective joins – Counterintuitive strategies for materializing data to maximize efficiency – Spark default settings specific to large scale jobs, and how they matter – Running Spark using Amazon EMR with more than 3200 cores – Review different types of errors and stack traces that occur with large-scale jobs and how to read and handle them – How to deal with large number of map output status when there are 100k partitions joining with 100k partitions – How to prevent serialization buffer overflow as well as map out status buffer overflow. This can easily happen when data is extremely large – How to effectively use partitioners to combine stages and minimize shuffle.

Scaling Your Skillset with Your Data with Jarrett Garcia (Nielsen)Spark Summit

More data is being created than ever before and the rate at which it is being created is not slowing. This means that new techniques are being developed and used to tackle big data challenges. At Nielsen, big data means opportunities for new products like digital measurement, but also exposed a skills gap for dealing with big data. This session will cover Nielsen’s successful implementation of Spark and Databricks which has allowed Nielsen to scale its products and its Data Scientists’ skillsets.

Storage Engine Considerations for Your Apache Spark Applications with Mladen ...Spark Summit

You have the perfect use case for your Spark applications – whether it be batch processing or super fast near-real time streaming — Now, where to store your valuable data!? In this talk we take a look at four storage options; HDFS, HBase, Solr and Kudu. With so many to choose from, which will fit your use case? What considerations should be taken into account? What are the pros and cons, what are the similarities and differences and how do they fit in with your Spark application? Learn the answers to these questions and more with a look at design patterns and techniques, and sample code to integrate into your application immediately. Walk away with the confidence to propose the right architecture for your use cases and the development know-how to implement and deliver with success.

Beyond unit tests: Testing for Spark/Hadoop Workflows with Shankar Manian Ana...Spark Summit

As a Spark developer, do you want to quickly develop your Spark workflows? Do you want to test your workflows in a sandboxed environment similar to production? Do you want to write end-to-end tests for your workflows and add assertions on top of it? In just a few years, the number of users writing Spark jobs at LinkedIn have grown from tens to hundreds, and the number of jobs running every day has grown from hundreds to thousands. With the ever increasing number of users and jobs, it becomes crucial to reduce the development time for these jobs. It is also important to test these jobs thoroughly before they go to production. Currently, there is no way users can test their spark jobs end-to-end. The only way is to divide the spark jobs into functions and unit test the functions. We’ve tried to address these issues by creating a testing framework for Spark workflows. The testing framework enables the users to run their jobs in an environment similar to the production environment and on the data which is sampled from the original data. The testing framework consists of a test deployment system, a data generation pipeline to generate the sampled data, a data management system to help users manage and search the sampled data and an assertion engine to validate the test output. In this talk, we will discuss the motivation behind the testing framework before deep diving into its design. We will further discuss how the testing framework is helping the Spark users at LinkedIn to be more productive.

Lessons from the Field: Applying Best Practices to Your Apache Spark Applicat...Databricks

This document discusses best practices for optimizing Apache Spark applications. It covers techniques for speeding up file loading, optimizing file storage and layout, identifying bottlenecks in queries, dealing with many partitions, using datasource tables, managing schema inference, file types and compression, partitioning and bucketing files, managing shuffle partitions with adaptive execution, optimizing unions, using the cost-based optimizer, and leveraging the data skipping index. The presentation aims to help Spark developers apply these techniques to improve performance.

An Adaptive Execution Engine for Apache Spark with Carson Wang and Yucai YuDatabricks

Catalyst is an excellent optimizer in SparkSQL, provides open interface for rule-based optimization in planning stage. However, the static (rule-based) optimization will not consider any data distribution at runtime. A technology called Adaptive Execution has been introduced since Spark 2.0 and aims to cover this part, but still pending in early stage. We enhanced the existing Adaptive Execution feature, and focus on the execution plan adjustment at runtime according to different staged intermediate outputs, like set partition numbers for joins and aggregations, avoid unnecessary data shuffling and disk IO, handle data skew cases, and even optimize the join order like CBO etc.. In our benchmark comparison experiments, this feature save huge manual efforts in tuning the parameters like the shuffled partition number, which is error-prone and misleading. In this talk, we will expose the new adaptive execution framework, task scheduling, failover retry mechanism, runtime plan switching etc. At last, we will also share our experience of benchmark 100 -300 TB scale of TPCx-BB in a hundreds of bare metal Spark cluster.

Building Custom ML PipelineStages for Feature Selection with Marc KaminskiSpark Summit

For predicting vehicle defects at BMW, a machine learning pipeline evaluating several thousand features was implemented. As important features can be useful for evaluating specific defects, a feature selection approach has been used. For further evaluating the importance of features, several feature selection techniques (filters and wrappers) have been implemented as ml PipelineStages for usage on dataframes for incorporation in a complete Spark ml Pipeline, including preprocessing and classification. The general steps for building custom Spark ml Estimators are presented. The API of the newly implemented feature selection techniques is demonstrated and results of a performance analysis are shown. Besides that, experiences gained and pitfalls that should be avoided are shared.

Apache Spark Performance Troubleshooting at Scale, Challenges, Tools, and Met...Databricks

This talk is about methods and tools for troubleshooting Spark workloads at scale and is aimed at developers, administrators and performance practitioners. You will find examples illustrating the importance of using the right tools and right methodologies for measuring and understanding performance, in particular highlighting the importance of using data and root cause analysis to understand and improve the performance of Spark applications. The talk has a strong focus on practical examples and on tools for collecting data relevant for performance analysis. This includes tools for collecting Spark metrics and tools for collecting OS metrics. Among others, the talk will cover sparkMeasure, a tool developed by the author to collect Spark task metric and SQL metrics data, tools for analysing I/O and network workloads, tools for analysing CPU usage and memory bandwidth, tools for profiling CPU usage and for Flame Graph visualization.

Agile and Modeling / MDE : friends or foes? (Agile Tour Nantes 2010)Jordi Cabot

n the talk I explore the relationships between software modeling and agile practices. For many agilists, the perception is that modeling is a useless activity that should not be part of the core agile practices. But, Is this really the case? Can agile benefit from modeling? Can modeling benefit from agile? Can modeling help companies understand the human and social aspects of agile methods and improve their chances of success when adopting them?

How To Become A Machine Learning Engineer? | Machine Learning Engineer Salary...Edureka!

** Machine Learning Master's Program: https://www.edureka.co/masters-program/machine-learning-engineer-training ** This Edureka PPT on "How to become a Machine Learning Engineer" covers all the basic aspects of becoming a certified Machine Learning Engineer. It establishes the concepts like roles, responsibilities, skills, salaries and even trends to get you up to speed with Machine learning. Follow us to never miss an update in the future. Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka

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