Frequently used Machine Learning Tools in 2024

November 17th, 2020
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There are different types of software, programming languages, tools, platforms available in the market to develop and build ML applications. On a personal level, it is easier to practice and use the ones which are open sources and free. However, at the industry level, it totally depends on the client/organization who wants to build an ML application. The phase of implementation, the size of the organization, and the ability to invest in AI ML ideas, vendor partnerships mostly govern the machine learning tools which the organization will be using. Check the list of professional course.

Bases on popularity and ease of use, listed below are some of the machine learning tools which are use very frequently by most organizations. The list is not exhaustive though and with the advent of time, this is subject to change.

Machine Learning Tools

In the subsequent paragraphs, will try to highlight the pros and cons of some of the above tool/applications. The readers will have a good idea about the popularity curve of the above tools and will also get some information about the ones which are catching up fast in the race. This will finally help readers to circle in the tool/application they would like to concentrate on to begin their ML journey and for the intermediate/experts/professionals decide which are the next few ones that need to upskill in. machine learning course.

Frequently Used Machine Learning Tools

Apache Spark

It is a frequently use machine learning tools. Apache Spark is originally develope in 2009 at UC Berkeley. It is the largest open-source project in data processing and is a lightning-fast unified analytics engine for big data and machine learning. Apache Spark is being rapidly adopt by enterprises- both large and medium across a wide range of industries.

Yahoo, Netflix, and eBay have deployed Spark at a massive scale to provide services to their customers, collectively processing multiple petabytes of data on clusters of over 8,000 nodes. Apache Spark has quickly become the widest open source community in big data, with more than 1000 contributors from 250+ organizations. Power BI Course

  • Speed

Spark is considered to be 100 times faster than Hadoop for large scale data processing. It exploits in-memory computing and other optimizations. Spark holds the world record for large-scale on-disk sorting. For high-quality algorithms, it is 100 times faster than MapReduce, excels at iterative computation, enabling MLlib to run fast.

  • Ease of Use

Data transformation and connecting to API for structured and unstructured data could be done very easily. Also, for building models, Spark has MLlib which can leverage python and R based libraries. It can also be used in Java, Scala, Python, and R. While speaking about data sources, traditional databases and modern ones including HDFS, HBase, or local files could be easily plugged in.

A Unified Engine

Spark supports features like SQL queries, streaming data, machine learning, and graph processing. Users can create simple to complex workflows using the above libraries while doing any kind of analysis. Spark runs on Hadoop, Apache Mesos, Kubernetes, standalone, or in the cloud, against diverse data sources, standalone cluster mode, on EC2, etc. It has the ability to access data in HDFS, Apache Cassandra, Apache HBase, Apache Hive, and hundreds of other data sources.

Machine Learning Tools

Python - Machine Learning Tools

Python is also another machine learning tools. It is said to be a general-purpose interpreted, interactive, object-oriented, and high-level programming language. Guido van Rossum created Python during 1985- 1990. Python's source code is also available under the GNU General Public License (GPL). Machine learning Online Course.

Why learn Python?

Applied Data Science with Python is a high-level, interprete, interactive, and object-orienting scripting language and also highly readable. It has English keywords and is easy to acquire as a skill.

Many schools and universities have made Python mandatory in their curriculum. Working professionals whether its developers or business leader finds it important and useful. Key advantages of learning Python Course:

  1. Python is Interpreted − Processed at runtime by the interpreter and hence no need of compiling program before executing. Similar to PERL and PHP
  2. Python is Interactive
  3. Object-Oriented − Python supports object-oriented style i.e., encapsulates code within objects
  4. Python is a Beginner's Language − The learning curve for beginners is not steep compare to other languages

Characteristics of Python

Following are important characteristics of Python Programming

  1. Supports functional and structured programming methods
  2. Can be use as a scripting language
  3. Can be compile to byte-code for building large applications
  4. Provides very high-level dynamic data types
  5. Supports automatic garbage collection
  6. Easy integration with C, C++, Java, etc

Applications of Python

  1. Easy-to-learn − Has fewer keywords, simple structure, and a clearly defined syntax which makes it easier to learn
  2. Easy-to-read − Coding is more clearly define and hence easily understandable
  3. Easy-to-maintain − Source code is easy-to-maintain
  4. A broad library − Python's bulk of the library is very portable and cross-platform compatible across operating systems
  5. Interactive Mode − Python allows interactive testing and debugging
  6. Portable − Python can be use easily on a wide variety of hardware platforms
  7. Extendable − Low-level modules can be add to the Python interpreter which enables programmers to add to or customize their tools to be more efficient
  8. Databases − Provides interfaces to all major commercial databases
  9. GUI Programming − It supports GUI applications that create and ports to many system calls, libraries
  10. Scalable − Provides a better structure and support for large programs
  11. Open-source- Python is free to use and distribute, even for commercial use as it is developed under an OSI-approved open source license

You May Also Read- 20 Most Asked Machine Learning Interview Questions

Azure ML

It is also frequently use in machine learning tools. Azure Machine Learning is a cloud-base environment that can be use to train, deploy, automate, manage, and track ML models. Azure Machine Learning finds usefulness for any kind of machine learning, from classical machine learning to deep learning both supervised and unsupervised. Machine Learning certification course.

It provides the flexibility of Python and R coding with SDK as well as drag and drop in the studio, build, train, and track machine learning and deep-learning models in an Azure Machine Learning Workspace.

It offers two things

  1. Studio
  2. Service

Difference between Azure ML and Azure ML Studio

Azure ML StudioAzure ML Service
No codingCoding based environment.
Drag-and-drop environmentEnvironment for Python coding
In-built algorithms and data transformation toolsCan use any ML algorithms or library.
Use it when the predefined algorithms provide a solutionPreferred when predefined algorithms in ML Studio do not meet requirements.

Benefits of Azure machine learning service

  • Use Machine Learning as a Service

It's a pay-as-you-go service. Businesses do not require setting up complex or purchasing any big hardware or software while using Azure machine learning. Purchase the services and start developing Machine Learning applications instantaneously.

  • Easy & Flexible building interface

It offers drag and drops components that minimize the code development and straightforward configuration of properties. Businesses can build, test, and generate advanced analytics based on the data.

  • Wide range of supported algorithms

It offers well-known algorithms which is use to build models using drag and drop features. It does not require in-depth knowledge of data science and machine learning algorithms Any user who understands the suitability of the algorithms with respect to the data set should be able to easily maneuver. Importing training data and fine-tuning can be done numerous times on the data very easily. This is a very cost-effective feature from the enterprise level.

  • Easy implementation of web services

Just use the drag and drop feature on the data sets, algorithms to link them together to implement web services required for ML development. Once you have practiced with the environment, you just need to test, which would be ready for further use, then you need to click one button to create and publish the web service. Now, you can use the web service from any device bypassing valid credentials

  • Great documentation for Machine Learning Solutions

Microsoft Azure offers full stacks of documentation like quick starts, tutorials, references, and lots of examples that help businesses to easily build, deploy, manage and access the Machine Learning solutions effectively

Conclusion

Above mention tools are top frequently use machine learning tools. And, these are not limits to the above mentions. There are other few tools as well which are use by most of the machine learning engineers. Check Out the Machine Learning training .

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