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Data Science platforms have been a pathbreaking entry in the digital age. Data Science helps predict different models using computer programming. The main component of Data Science is data. Data Science is the next level of analytics. It steers the enterprises. Data Science allows Data scientists to extract valuable information from the available data. The information helps the management to take calculated decisions for the company.
Several companies take full advantage of Data Science to understand the market. This helps them to provide the right product or services to their customers. With the advancement of technology, Data Scientist needs to stay updated with the latest changes in Data Science. The Data Scientist should be aware of the best Data Science learning platform that would help them in providing benefits to their company. This article will discuss the best Data Science learning platforms.
What are Data Science platforms?
Before diving into different Data Science platforms, one should understand the Data Science platform. The Data Science platform is the foundation that involves different technologies for machine learning.
Data Science platforms help Data Scientists analyse the vast amount of data. It helps to plan strategies and provide complete detailed information to the company's executives. The analysed data gives data-driven information that helps to understand the market easily.
Different types of Data Science platforms
There are different types of Data Science platforms. It is very important to understand them. The Data Scientists should choose them as per their needs. Data Science provides detailed insights from the available raw data. So, a good Data Science platform is required to analyse the data. Let's look at some of the best Data Science learning platforms.
- Amazon SageMaker - Amazon SageMaker has different integrated tools to work on the machine learning outflow. It provides all the tools for the smooth running of the machine learning application. This is one of the best platforms to learn Data Science. There are different types of Amazon SageMaker. Let's look at them in brief.
- SageMaker Ground Truth - It builds and manages highly accurate training datasets swiftly.
- SageMaker Debugger - It helps to provide warnings. It provides solutions when general issues are generated. It captures real-time data during training.
- Autopilot - It detects raw data, and applies the required processors. This analyses the data through the best algorithms. The Autopilot fine-tunes the data with different models. It keeps track of their performance, and ranks them.
- SageMaker Experiments - It organises and keeps track of the iterations in machine learning models. This is done by automatically collecting the configurations, input parameters, and results. Finally, it saves them as 'Experiments.'
- Anaconda platform - Anaconda is a free Data Science platform suitable for python and R languages. It is an open-source platform. This platform is popular for hosting over 1500 Data Science packages. It is used by over 15 million people globally. The Anaconda Navigator helps search different packages on the Anaconda cloud. It is easy to install and update. The platform is available on macOS, Linux, and Windows.
- cnvrg.io - This provides an end-to-end Data Science platform to generate and execute AI models of different scales. The cnvrg helps Data scientists to build, manage, and automate ML from research to production. The platform organises all data in a single place. This helps for better collaboration with the team. It has real-time visualisation to track different models. This also helps in building production-ready machine pipelines within some clicks.
- Neptune - This platform is an experimental management tool. It keeps the ML experiments on track and manages the metadata. It has a stable UI, is highly flexible, and is compatible with different frameworks. The platform stores, organises and has different developmental metadata. It has robust experiment tracking and visualisation capability. Neptune has a beautiful UI that organises and runs in different groups. It saves the customised dashboard. The dashboard can be easily shared with the team.
- H2o.ai platform - H2o.ai is a popular open-source Data Science platform. It makes working on ML easier. Experienced Data scientists highly recommend this. There are different H2o suites available.
- Deepwater - This is an amalgamation of MXNet, Caffe, and TensorFlow
- Steam - This is used to build and deploy applications and APIs. This is a paid version.
- H2O - This helps build and produce different data models to analyse.
- Sparkling water - This is used with integration with Apache Spark.
- Driverless AI - This is popular among non-technical people. It helps to prepare the data, tune parameters, and determine solutions for business problems. This is easy to use.
- Rapidminer - Rapidminer is largely used in different commercial applications, research, education, rapid prototyping, training, and application development. It is one of the popular Data Science platforms providing predictive and advanced analytics. This is a paid platform. But users with 1 logical processor with the AGPL license can get it for free. Rapidminer provides five different products.
- Rapidminer studio - This is the basic platform where all ML applications work.
- Rapidminer server - This is used for application-specific servers for better performance.
- Rapidminer Turbo prep - This provides a user-friendly UI where the data is visible in the centre and front.
- Rapidminer Radoop - This is used with Hadoop technology. This is the perfect amalgamation with Hadoop.
- Rapidminer Automodel - This is an extension of the studio. It enhances the building and validates different models.
- MLflow - ML flow is a popular open-source platform. The Data scientists can work on the machine learning lifecycle. The lifecycle includes experiment, reproduce, deployment, and central model registry. The MLflow has four main functions. They are tracking, project, models, and model Registry. The ML flow is perfectly suited for individuals and teams of any number. The tools in this platform are compatible with different ML languages, making it one of the best Data Science learning platforms.
- Spell - Spell is usually used to train and deploy ML codes easily and swiftly. The platform provides Kubernetes-based infrastructure. This is used to operate ML experiments, store their data, and automate the entire MLOps lifecycle. Spell platform provides a flexible environment for PyTorch, Tensorflow, Fast.ai, and others. Installing different codes using pip, conda, and apt is easy. Different runs can be integrated using different workflows to manage the training pipelines. Spell automatically generates model metrics.
- TensorFlow - TensorFlow is another popular Data Science platform used to implement ML pipeline production. The platform provides a detailed framework and shared libraries to combine the common elements. This is required to define, launch, and keep an eye on the ML system. It provides a stable and powerful ecosystem for Python, C++ APIs and other languages. TensorFlow has a robust add-on library. Models like TensorFlow Probability, Ragged Tensors, BERT, and Tensor2Tensor are some models in which Data Scientists can experiment. This platform is suitable for beginners. This platform provides all the necessary flexibility and features to learn and experiment.
Data Science is the need of today. With such a vast amount of data available, it is important to process them to understand the market's demands. Using the different tools of Data Science, it would benefit the company in providing the right solution. This would make their customer's life easier. With the rising demand for Data Scientists, several Data Science online courses are available. For interested candidates looking for Data Science certification, visit StarAgile to get trained by experts and get proper industry exposure.