10 of the Best Platforms for Data Science and Machine Learning
The field of data science is experiencing great disruptions that are making the work of data scientists easier. There are several data science and machine-learning software products available for free and for purchase on the market today.
These products are ideal for data scientists who recognize the benefits of integrating machine-learning capabilities in their tasks. They also target data-driven organizations that want to cut on the costs of hiring expert data scientists.
What a Good Data Science and Machine-Learning Platform Should Have
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.
10 of the Best Data Science and Machine-learning Platforms
According to Gartner, organizations and data scientists rely on data science and machine-learning platforms to build and deploy data science models using an end-to-end approach. Several software vendors are currently unleashing out software products that match this description. However, not all the software products released by them are ideal for use in data-oriented organizations. Here’s a comprehensive list of ten of the best data science and machine-learning platforms.
With its headquarters in Irvine, CA, Alteryx Analytics provides data scientists with a machine-learning platform for building models in a workflow. To expand the capabilities of its machine-learning platform, the company acquired a data science enterprise (Yhat) that focuses on model management and deployment. Alteryx’s product vision aims at helping companies in cultivating a data analytics culture without necessarily hiring data scientists.
Located in Mountain View, CA, H2O.ai is for data scientists looking for a platform for deep machine-learning. The company offers H20 Deep Water for deep-learning, H2O Sparkling Water for those interested in Spark integration, H2O Steam and H2O Flow. H2O.ai continues to expand as an innovator and thought leader in data science and machine-learning unified platforms.
KNIME Analytics Platform
This data science and machine-learning platform currently has a user base of over 100,000 people globally. It is a product of KNIME, which has its headquarters in Zurich, Switzerland. As an open-source platform, KNIME Analyticsis useful in enterprises looking to boost their performance, security and collaboration. Cloud versions of this platform are available on Microsoft Azure and AWS.
The RapidMiner platform is a product of Boston based company known as RapidMiner. This platform comes with RapidMiner Radoop for extending the platform’s execution capabilities to a Hadoop environment, RapidMiner Studio for model development and RapidMiner Server that enables data scientists to share, collaborate on and maintain models. RapidMiner excels in introducing new performance and productivity capabilities to model development and execution.
SAS, a data science and analytics software vendor, has its headquarters in Cary, NC. The company’s SAS Visual Analytics and SAS Enterprise Miner are useful for machine-learning, visual data mining and visual statistics. These products allow a wide range of users to access analytics tools. Its software products are available for purchase.
MathWorks’ MATLAB and Simulink
As a privately-held venture based in Natick, MA, MathWorks is reputable for its two software products (MATLAB and Simulink). The company has a strong user base and customer relations team. Its products are for high-end financial use and data engineering contexts. The company solidified its presence in the data science and machine-learning software marketplace by keeping up with market developments.
TIBCO Software, a Palo Alto-based software vendor, made its debut in the data science and machine-learning software marketplace through its famed acquisition of Statistica from Quest Software. The software vendor also acquired Alpine Data. The Statistica platform is useful in product refinement, advanced prototyping and business exploration. It is reputable for these use cases and has a huge and mature user base.
Databricks Unified Analytics Platform
This Apache Spark-based platform offers proprietary features for real-time enablement, performance, operations, reliability and security on Amazon Web Services (AWS). It is a software product of Databricks, which has its head office in San Francisco, CA. The Databricks Unified Analytics Platform targets the open source community.
Domino Data Science Platform
This platform is an end-to-end solution for proficient data scientists looking for open-source collaboration tools for model development and deployment. It is a product of San Francisco based company known as Domino. The platform enables expert data scientists to access, prepare, explore and visualize data.
Microsoft’s Azure Machine-learning Studio
As one of the world’s largest software vendors, Microsoft also maintains its presence in the data science and machine-learning markets through its Azure software products. These products include Azure Machine-learning (that is inclusive of Azure Machine-learning Studio), Power BI, Azure Data Lake, Azure HDInsight, Azure Stream Analytics and Azure Data Factory. Its cloud-based Azure Machine-learning Studio is ideal for data scientists who want to build test and execute predictive analytics solutions on their data.
Since it is the responsibility of data scientists to come up with effective solutions for data science problems, it is up to them to choose the best tools to help them 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.
eTeam has built robust data science and machine learning solutions for our clients, ranging from predictive analysis, chatbots, individualized e-stores and fraud detection systems. If you’re interested in finding out how we can help your business with machine learning and data analysis, let’s get in touch and set up a quick call.
Originally posted at eTeam’s blog.