Applying Machine learning to Big data:Challenges and Solutions

What is Big data?

Big data refers to a large amount of data which is difficult to collect,process and store using traditional engines.

Characteristics of Big Data:

Big data is characterized by 5 V's Volume, Variety, Velocity, Value and Veracity as shown in Fig1

In simple words

• Volume mainly refers to all types of the data which is generated from different sources and continuously expands over time. The data

• Variety refers to the different types of data collected from several sources,such as: video, image, text, audio, and data logs, and either structured,semi-structured, or unstructured format.

• Velocity refers to the frequency of creation, collection, analyzing, and storing.

• Value is an essential characteristic of big data. It is not the data that we process or store. It is valuable and reliable data that we store, process, and also analyze.

• Veracity means how much the data is reliable. It has many ways to filter or translate the data. Veracity is the process of being able to handle and manage data efficiently. Big Data is also essential in business development.

What is Machine Learning?

Machine learning is a field within artificial intelligence that allows machines to learn on their own from existing information to make predictions or/and decisions

Challenges in Big data with machine learning:

Here are some challenges that are faced when applying machine learning with Big data.

1. Data Storage and Processing:

2. Data Quality

3. Feature Engineering

4. Scalability

5. Model Selection

6. Model interpretability

7. Privacy and security

1. Data Storage and Processing: Storing and processing large amounts of data can be a major challenge.

2.  Data Quality: Ensuring the quality of big data can be difficult, as it often comes from multiple sources and may be incomplete, inconsistent or inaccurate. This can lead to poor model performance and inaccurate predictions.

3.Feature Engineering: With big data, it can be difficult to identify the most relevant features for building a model. Feature engineering is the process of selecting, transforming and creating new features, which can be a time-consuming and challenging task.

4. Scalability: As the volume of data increases, the scalability of machine learning algorithms becomes a major concern.

5 Model Selection: With big data, it can be difficult to select the most appropriate model for a given task. There are many different machine learning algorithms to choose from, each with their own strengths and weaknesses. It can be challenging to identify the best algorithm for a given problem.

6.Model interpretability: with big data and complex models, it is difficult to interpret the model's decision and understand how the model arrived at its predictions.

7. Privacy and security : when handling big data, the privacy and security of the data becomes a concern. It is important to ensure that sensitive data is protected and that the models are not used for malicious purposes.

Solutions for the challenges:

1. Sampling: Instead of working with the entire dataset, you can use a random sample of the data to train and test your models. This can help reduce the computational cost and improve the performance of your models.

2. Feature Selection: Identify the most relevant features for your problem and remove any irrelevant or redundant features. This can help improve model interpretability and reduce overfitting.

3. Data Preparation: Make sure your data is clean, consistent, and in the appropriate format before building your model. This can help improve model performance and reduce the likelihood of errors.

4. Data Partitioning: Split the data into smaller chunks, so that the data can be processed in parallel. This can help improve the scalability of your models.

5. Data Preprocessing: Use techniques such as normalization, scaling, and encoding to preprocess your data. This can help improve the performance of your models.

6. Algorithm selection: Pick an appropriate algorithm that can handle large datasets and is suitable for the problem you are trying to solve.

7. Distributed computing: Use distributed computing technologies such as Hadoop, Spark, and cloud computing to process large datasets. This can help improve the scalability and performance of your models.

8. Ensemble Methods: use ensemble methods, such as bagging, boosting, and stacking, to improve the performance of your models. Ensemble methods can help reduce overfitting, improve model interpretability, and increase the robustness of your models.

Apache spark & Apache Hadoop

Apache Hadoop is a open-source framework handles large datasets in a distributed fashion. The Hadoop ecosystem is highly fault-tolerant and does not depend upon hardware to achieve high availability.

Apache Spark is an open-source tool. It is focused on processing data in parallel across a cluster, but the biggest difference is that it works in memory. It is designed to use RAM for caching and processing the data.

Use of Machine Learning Libraries(MLlib and Mahout)

    MLlib is Spark’s distributed machine learning library .MLlib targets large-scale learning settings that benefit from data parallelism or model-parallelism to store and operate on data or models. It regroups parallel algorithms that run well on clusters. Some other classic Machine Learning (ML) algorithms are not integrated because they were not designed for parallel models . The idea behind Apache spark MLlib is to make machine learning scalable and useful. Generally, it provides several tools for example ML Algorithms (including Classification, Clustering, and Collaborative Filtering). Pipelines (for evaluating and tuning ML Pipelines), Persistence (for saving and loading algorithms and pipelines) and Utilities (for data management and optimization algorithms). MLlib is written in Scala language and it includes Java, Scala, and Python APIs, and is released as part of the Spark project. It has a large open-source community that is why it has grown rapidly.

    Apache Mahout is a free and open source project of the Apache Software Foundation . Mahout’s goal is to build scalable machine learning libraries. Mahout is scalable to reasonably large data sets. The core algorithms for clustering, classification and batch based collaborative filtering are implemented on top of Apache Hadoop using the MapReduce paradigm. Mahout Framework provides different tools to find automatically meaningful and convenient patterns to generate Big Data. Mahout is implemented on top of Apache Hadoop using the Map Reduce paradigm. At the moment, Mahout provides principally four use cases: Recommendation, Clustering, Classification, Frequent item set mining. Mahout provides Java libraries and Java collections for various kinds of mathematical operations

Conclusion:

     Machine learning libraries such as MLlib and Mahout provide pre-built algorithms and tools that can be used to perform machine learning tasks on large datasets, without the need for extensive coding and development.