Data science and machine learning platforms are now essential components of today's IT landscape. These two domains of computer science are continuously expanding, and if you want to stay current, you must get familiar with the platforms on which they operate. You may find over a hundred platforms concerning these two fields, and there is no end. Each platform has distinct characteristics that are appropriate for the data science and machine learning applications it supports.

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Contemporary data science and machine learning platforms offer numerous opportunities and pave the way for renowned data scientists, machine learning experts, and deep learning specialists. With the right platform, these experts can unlock their data prospects and interpret the fast-growing IT world.

Today, we'll discuss what machine learning platforms are, how you can select one for your purpose, and learn about a few of the most famous currently available ones, such as IBM, Databricks, DataRobot, etc.

What is Data Science and Machine Learning?
As per QKS Group, a data science and machine learning platform are an integrated system/hub built on both code-based libraries and low-code/no-code tools. This platform enables collaboration among data scientists and other stakeholders like data engineers and business analyst across different stages of the data science lifecycle, such as business understanding, data access and preparation, visualization, experimentation, model building, and insight generation. The platform facilitates machine learning engineering tasks, covering data pipeline development, feature engineering, deployment, testing and predictive analysis. The platform gives options between local clients, browsers, or completely managed cloud services to businesses depending upon their requirements.

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Top 5 Data Science and Machine Learning Platforms
Dataiku
Dataiku is a platform for Everyday AI that allows data specialists and domain experts to work together to integrate data into their daily operations, from sophisticated analytics to generative AI. They collaborate in designing, developing, and deploying AI capabilities across various sectors. Organizations using Dataiku empower their employees to excel, resulting in the AI that will drive their business into the future.
Databricks
Databricks is a multinational corporation that focuses on data and artificial intelligence. The Databricks Data Intelligence Platform is at the heart of Databricks, allowing large enterprises to harness data and AI to power a wide range of business applications. It is built on a lakehouse to give an consistent platform for all data and governance, and it is driven by a Data Intelligence Engine that recognizes the uniqueness of each organization's data. Databricks simplifies and accelerates business data and AI goals by combining data, analytics, and AI on a single platform.
DataRobot
DataRobot provides AI that increases effect while minimizing corporate risk. Our AI apps and platform interface with fundamental business processes, allowing teams to create, deliver, and manage AI at scale. DataRobot enables practitioners to offer predictive and creative AI while also helping leaders safeguard their AI assets. DataRobot is relied on by organizations throughout the world to provide AI that makes sense for their business today and in the future.

IBM
IBM is a prominent company focusing on technology and development. Its primary aim is to promote technological advancement and enhance infrastructure through targeted development and consulting services. It fosters originality and innovation to facilitate the transformation of theoretical concepts into tangible realities, thereby boosting global functionality. IBM drives change by creating groundbreaking solutions that restructure and reinvent the world.

MathWorks
MathWorks is a software development company that specializes in the design of mathematical computer tools. It offers critical tools in the field of technical computing, such as MATLAB, a programming environment used for algorithm creation, data analysis, visualization, and numerical calculation. Simulink, another software solution, serves as a graphical platform for simulating and designing dynamic and embedded systems across different domains.

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How to Choose the Right Platform?
When it comes to choosing the right data science and machine learning platform, businesses often rely on market intelligence reports to make informed decisions. QKS Group, a global advisory and consulting firm, provides strategic insights into various technology markets through reports like "Market Share: Data Science and Machine Learning Platform, 2023, Worldwide" and "Market Forecast: Data Science and Machine Learning Platform, 2024-2028, Worldwide." These reports offer in-depth analysis of market trends, competitive landscapes, and technological advancements, aiding businesses in identifying platforms that are widely adopted and forecasting future market developments.

Making the Right Choice
The market for Data Science and Machine Learning (DSML) platforms is experiencing remarkable growth worldwide, driven by the increasing adoption of artificial intelligence (AI) and machine learning (ML) across various industries. Organizations are leveraging these platforms to gain insights from their data, automate processes, and make data-driven decisions.

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The global market is characterized by strong competition among leading tech giants such as Google, Microsoft, Amazon, IBM, and emerging players like DataRobot, Databricks, and H2O.ai.

Conclusion
To remain competitive in the digital age, businesses must evaluate a variety of data science and machine learning techniques and technologies on a global scale. Each data science program has its characteristics and is tailored for specific user groups. Consequently, you must determine whether to utilize open-source, cloud-based, or integrated systems. Assessing your organization's requirements is crucial for professionals at different management levels.

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Finally, don't be scared to investigate and research while you work on your data science initiatives. Throughout the adventure, you will learn enough about the various platforms and their intrinsic characteristics. Choosing the right platform simplifies data science and machine learning.