10 Best Data Science and Machine Learning Platforms

Data science and machine learning market is more promising than ever. Most companies are now delving into the world of data science and machine learning. They are embracing the open-source, public cloud offerings to expand their reach and develop their expertise.
A good data science and machine-learning platform should offer data scientists the building blocks for creating a solution to a data science problem. It should also provide these experts with an environment where they can incorporate the solutions into products and business processes. The platform needs to provide data scientists with all the support they need when carrying out data and analytics tasks. These tasks encompass visualization, interactive exploration, deployment, performance engineering data preparation and data access.
It is the joy of data scientists to use a data science and machine-learning platform that enables them to work both online and offline.

With the introduction of cloud-based platforms, data scientists can now work with their data on any Internet-enabled device. They can also share components of their work with their colleagues or collaborate with them securely on certain tasks. Besides having cloud features, the data science and machine-learning platform should also run faster to provide accurate results.

What is the Best Data Science and Machine Learning Platforms?

Here, we have discussed the best data science and machine learning platforms.

1. MathWorks

MathWorks is the leading developer of mathematical computing software. Engineers and scientists worldwide rely on its products to accelerate the pace of discovery, innovation, and development.

MATLAB, the language of technical computing, is a programming environment for algorithm development, data analysis, visualization, and numeric computation. Simulink is a graphical environment for simulation and Model-Based Design for multidomain dynamic and embedded systems. MATLAB and Simulink are also fundamental teaching and research tools in the world’s universities and learning institutions.

Founded in 1984, MathWorks employs more than 5000 people in 16 countries, with headquarters in Natick, Massachusetts, USA.

2. alteryx

Alteryx powers analytics for all by providing the leading Analytics Automation Platform. Alteryx delivers easy end-to-end automation of data engineering, analytics, reporting, machine learning, and data science processes, enabling enterprises every-where to democratize data analytics across their organizations for a broad range of use cases. More than 8,000 customers globally rely on Alteryx to deliver high-impact business outcomes.

More than 320,000 global Alteryx users gather every day on the Alteryx Community to solve together.
Alteryx is headquartered in Irvine, Calif. and has 22 additional global offices: Redwood City, Calif.; Broomfield, Colo.; Ann Arbor, Mich.; Dallas; Chicago; New York; Boston; Toronto; London; São Paulo; Paris; Munich; Prague; Kyiv, Ukraine; Dubai; Tokyo; Singapore and Sydney.

3. ANACONDA

With more than 30 million users, Anaconda is one of the Best Data Science and Machine Learning Platforms and the world’s most popular data science platform and the foundation of modern machine learning.
Why is Anaconda the world’s most popular data science platform? Simply put, Anaconda can help anyone harness the power of open source for competitive advantage, groundbreaking innovation, and a better world.
Anaconda achieved Leader status in the Data Science and Machine Learning Platforms reports by receiving positive reviews from verified users comparing similar products in the relevant categories.

4. sas

SAS is the leader in business analytics software and services, and the largest independent vendor in the business intelligence market. Through innovative solutions, SAS helps customers at more than 70,000 sites improve performance and deliver value by making better decisions faster. Since 1976 SAS has been giving customers around the world THE POWER TO KNOW.
SAS has a firm hold on its Global 2000 enterprise base, which isn’t going away anytime soon. But it is challenged to compete for the hearts and minds of the next generation of data scientists, data engineers, and business analysts who have become empowered by self-service and drawn in by open source.

5. Databricks

Databricks is the lakehouse company. More than 7,000 organizations worldwide — including Comcast, Condé Nast, H&M, and over 40% of the Fortune 500 — rely on the Databricks Lakehouse Platform to unify their data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe. Founded by the original creators of Apache Spark™, Delta Lake and MLflow, Databricks is on a mission to help data teams solve the world’s toughest problems.
Databricks was founded in 2013 by the creators of Apache Spark, an open source framework for distributed computation across multiple machines many deep learning projects use today. The group of data and machine learning researchers first met at UC Berkeley.
Databricks is the latest company focused on data analysis and AI to experience rapid growth. That’s despite a drop in gross domestic product in the U.S. economy in the past year the likes of which, according to the U.S. Department of Commerce, has not been seen since the 1940s.

6. SaturnCloud

Saturn Cloud number 06 in our Best Data Science and Machine Learning Platforms list, it is striving to make Python data science accessible to everyone, with scalable computing resources and production deployments, all on one service. The company has thousands of users, including teams at Mount Sinai Health System, Nestle’s Vital Proteins, and Trimark, who use the service to run data science workloads in the cloud with scalability and ease. Saturn Cloud is a venture-backed company with customers all over the globe.
Another definition: Saturn Cloud, a Python data science company, announced that it is launching a free cloud-hosted data science and machine learning platform. The service includes hosted notebooks that run on CPUs or T4 and V100 GPUs, including managed Dask clusters for parallelizing code for faster models. These new capabilities make it easy for data scientists to scale up and down, scale across GPUs, share work and dashboards, productionize workflows, and much more.

For data scientists working on local machines, Saturn Cloud offers an easy path moving to the cloud with no cost, setup or infrastructure work. For data scientists using other major cloud-hosted services, Saturn Cloud is unique in that it offers instant access plus multi-node multi-cluster computing tools. The company has also made it easy to use tools such as PyTorch, LightGBM, RAPIDS, and many other Python data science libraries. Additionally, Saturn Cloud offers solutions for enterprise, including Hosted Pro and VPC deployments on AWS and other cloud providers. The paid Hosted version is pay-as-you-go pricing, without any fixed subscriptions costs.

Saturn Cloud has partnered with Amazon Web Services, Snowflake Computing, and NVIDIA to drive the adoption of Python data science tools such as Dask. These partnerships make it easy to add Saturn Cloud to existing data science infrastructure for users and organizations, where native product integrations make connections seamless across the platforms.

7. Deepnote

Deepnote is building the best data science notebook for teams. In the notebook, users can connect their data, explore and analyze it with real-time collaboration and versioning, and easily share and present the polished assets to end users. All of this is done through a powerful, browser-based UI that runs in the cloud.

Features include:

– Sharing notebooks and projects via URL

– Inviting others to view, comment and collaborate, with version control

– Publishing notebooks with visualizations for presentations

– Sharing datasets between projects

– Full linux terminal access

– Code completion

– Automatic python package management

– Importing from github

– PostgreSQL DB connection

8. Rapidminer

For those driven to accelerate the pace of transformation, RapidMiner is the enterprise-ready data science platform that amplifies the collective impact of your people, expertise and data for break-through competitive advantage. RapidMiner’s data science platform supports all analytics users across the full AI lifecycle. The RapidMiner Academy and Center of Excellence methodology ensures customers are successful, no matter their experience or resource levels. Since 2007, more than 1 million professionals and 40,000 organizations in over 150 countries have relied on RapidMiner to bring data science closer to their business. Explore our blog and connect with us on Twitter and LinkedIn.

RapidMiner been designated as leaders in the “Gartner Magic Quadrant for Data Science Platforms“ for the past 6 years straight and the “Forrester Wave Multimodal Predictive Analytics and Machine Learning Solutions” for 2 years running. More than 30,000 organizations in over 150 countries rely on RapidMiner to increase revenue, cut costs and, reduce risk.

9. TensorFlow

TensorFlow is one of the most popular deep learning frameworks available. It’s used for everything from cutting-edge machine learning research to building new features for the hottest start-ups in Silicon Valley. In this course, learn how to install TensorFlow and use it to build a simple deep learning model. After he shows how to get TensorFlow up and running, instructor Adam Geitgey demonstrates how to create and train a machine learning model, as well as how to leverage visualization tools to analyze and improve your model. Finally, he explains how to deploy models locally or in the cloud. When you wrap up this course, you’ll be ready to start building and deploying your own models with TensorFlow.

10. Qlik

Qlik’s vision is a data-literate world, where everyone can use data and analytics to improve decision-making and solve their most challenging problems. A private SaaS company, Qlik offers an Active Intelligence platform, delivering end-to-end, real-time data integration and analytics cloud solutions to close the gaps between data, insights and action. By transforming data into Active Intelligence, businesses can drive better decisions, improve revenue and profitability, and optimize customer relationships. Qlik serves more than 38,000 active customers in over 100 countries.

Conclusion

Since it is the responsibility of data scientists to come up with effective solutions for data science problems, it is up to you to choose the best tools to help you in this activity. When it comes to choosing a data science and machine-learning platform, expert data scientists need to be cautious. They also need to choose a platform provided by a company with their shared vision. And your platform needs to conform to the needs of your organization or business. As you do a little research on the best choice for you, keep in mind what will provide real value for your organization and stay away from getting features just because. If the platform doesn’t integrate with your existing environment, it’s only going to be a distraction.