RapidMiner Assignment Help
RapidMiner is an open-source Data Science Platform that is used to bring artificial intelligence and quickly analyse to an enterprise. RapidMiner is built for analytics teams, researchers and non-programmers to be able to unify the entire data science lifecycle stages from the data prep to have a predictive model deployment prepared. RapidMiner provides support to port the Machine Learning Models that are created to other platforms like web apps, android, iOS ectara, which enables us to have a unified data science lifecycle. RapidMiner is a drag-and-drop graphical interface with an option of scheduling tools.
The process used in RapidMiner helps in simplifying the various tasks related to data mining and analysis. It is able to load data from any format, like Hadoop, RDBMS, CSV, etc, then following to pre-process and prepare the data on the standards used in the industry, for instance, grouping items based on categories, joining of tables or handling missing data. After which it helps in creating Machine Learning and Artificial Intelligence models, for example, Random Forest, Gradient Boost, clustering, based on the selection to finally providing visualisation for the output. Once the steps for creating the model is complete, the option for deployment can be easily be done on to the cloud or the production environment.
Thus, RapidMiner provides ways that help in:
- Providing lighting fast business impact
- A simplified method for an in-depth process for data analysis for everyone
- Full transparency and control for creating a machine learning model
- An end-to-end platform for collaboration
- Being open-source and having to multiply extensible
- Providing an ad hoc analysis and reporting
- Customisable Dashboards availability
- Shows the trend indicators
Some details on the services or products provided by RapidMiner are as follows:
- Visual Workflow Designer: This helps in increasing the productivity of the work across the entire data analytics team. The drag + drop visual interface helps in providing automation for creating the predictive models that speed up the process. There are about 1500+ algorithms and functions to use in the library to ensure that the best possible model is created for any scenario. There are pre-built templates that constructed or created for common scenarios, for example, fraud detection, predictive maintenance. For beginners to the tool or analysis, there is “Wisdom of Crowds” present in the Visual Workflow to help to provide suggestions for every step.
- Connect to Any Data Source: RapidMiner Studio allows or provides a platform for the user to work with any type of data no matter the location or format. It is able to create instance connections with databases, data warehouses, cloud storage and many more. These connections once made are easily reusable at any point in time; also, the connection can be easily shared with those who need access to the data.
- Automated In-database Processing: RapidMiner Studio has an option to run a data prep and ETL (extract, transform, load) inside a database to help in keeping the data in an optimised form for the possibility of advanced analytics. Thus, this helps in the possibility to write a SQL query and retrieve the data without being too complex. RapidMiner Studio helps in harnessing the power of very or highly scalable database clusters. RapidMiner Studio has or provides supports to MySQL, PostgreSQL and Google BigQuery.
- Data Visualization & Exploration: With the help of robust statistical overviews and over 30 interactive visualisations in RapidMiner Studio be able to explore and evaluate the data. The exploring and evaluation of the data can be in terms of health, completeness, quality or it can be to understand the patterns, trends and distribution of the data. Through RapidMiner Studio there is a quick find and fix of common data quality problems, like missing values and outliers.
- Data Prep & Blending: In RapidMiner Studio, the hassle related to preparing data for predictive modeling is essential to eliminate. With the combination use of RapidMiner Turbo Prep, there is a fully interactive point click for data preparation experience that is offered. It provides the ability to extract, join, filter and grouping of data across multiple scores. This helps in creating repeatable data preparation and the ETL processes that were set when to happen and shared with other people working on a similar set of data.
- Visual & Automated Machine Learning: With the combination use of the RapidMiner Auto Model, it is possible to create models in 5 clicks for machine learning and do not have worry for writing the code. There are hundreds of options of machine learning algorithms be it supervised or unsupervised. There are basic and advanced Machine Learning Techniques, like regression, clustering, time-series, text analytics and deep learning, available to implement. RapidMiner Studio is able to provide the process of building the models to be sensitive or take into consideration constraints like time and costs to optimise the prediction to meet the desired impact. There is the ability to use both automated and manual feature engineering to meet and optimise the model accuracy.
- Model Validation: In RapidMiner Studio, there is the possibility to understand the true performance of the model even before deploying it into production. It provides a process of eliminating of overfitting the model through a unique approach that helps in preventing the model training and pre-processing from leaking into the application of the model. It adds proven techniques to the model with a single click of the mouse.
- Explainable Models Not Black Boxes: In each step of data preparation, modeling and validation process is a document for complete transparency and is explained in a manner that is easy to understand.
- Get More From R & Python Code: there is an option for having scalable code deployment and collaboration between the coders and non-coders. There is a way to deploy the code-based models and code-containing models into a scalable platform. This helps in eliminating duplicate work and ensures that others are able to use the work created based on the uploaded code snippets in the RapidMiner repository for the use of a simplified visual workflow designer.
- Flexible Scoring & Model Operations: there is the option in the RapidMiner Studio to turn the predictive insights into a business impact. There is a quick deploy of the data into spreadsheets and other possible data visualisation tools and turn the models into production web services with the use of RapidMiner Servers. This can be integrated with enterprise scheduling tools.
- Automation & Process Control: In RapidMiner Studio, the options for building a sophisticated visual workflow and having the ability to automate the important tasks. There are process control operators to create workflows that repeat or loop over the task, branch flows and provides access to system resources. There is the option or function that supports a variety of scripting languages for custom integrations and automatons also providing the option to Schedule processes.
- Team Collaboration: in RapidMiner Server, there is one place available, i.e. a central repository, that allows teams to share, manage and secure data preparation and modeling processes. It allows the team to manage and share all the work and data science artifacts, like the connections, data, processes, models and other results, in the central repository. There is a configure granular permissions to have control on the access to a specific process or folder.
- Process Automation: In this platform, important tasks can be automated as often as needed. There can be schedule processes created to make sure that the data is prepped and cleaned, the model is retrained and the continuously score the data in real-time. The RapidMiner Server is integrated with external applications through the use of REST API.
- Lightning Fast Model Creation: There are dedicated Server hardware to radically speeds up the predictive model creation. It allows the user to take full advantage of the multi-core multiprocessor server architecture. The push of jobs from RapidMiner Studio to RapidMiner Server is a simple single click. The user has the ability to scale up and accelerate the Auto Model in RapidMiner Studio as the user can run hundreds of models parallelly on RapidMiner Server.
- Turn Insight into Action: The RapidMiner Server helps in operationalising the predictive models and turning the prescriptive actions into recommendations. RapidMiner Server can create production web services APIs in few mouse clicks. The RapidMiner Server deploys the models to RapidMiner Real-Time Scoring for high volume, low latency scoring. There is a monitoring process on the model performance over time to help in detecting degradation and retrain the model as when needed and required.
- Scalable, Reliable & Secure: the RapidMiner Server has a trusted modern architecture built for mission-critical data science applications. It provides an arbitrarily scale to help in serving any data science workload. It runs in a highly available active configuration to minimise the downtime risk. The RapidMiner Server is compliant with modern enterprise authentication, authorisation and encryption standards.
- Platform Deployment & Operation: With the use of RapidMiner Server it is easy to deploy and operate the task from anywhere with minimal effort. The task can be deployed according to the user’s wants and requirements, i.e. on-premise, in the cloud or on a hybrid setup. The RapidMiner Server can be easily installed either by running a pre-built image from Docker Hub or the use of spin-up ready-to-use VMs from Microsoft Azure or AWS marketplaces. The license for RapidMiner Server is flexible and can be deployed on the accords to the user’s needs.
- Code Free Machine Learning for Hadoop & Spark: RapidMiner Radoop is created and build to run predictive models in Hadoop without having to code in Spark. RapidMiner Radoop uses the RapidMiner Studio visual workflow designer to create predictive models. RapidMiner Radoop expands beyond the use of MLlib to tackle a broader set of use cases scenarios like time series and text analytics.
- Harness the Power of Hadoop Clusters: The RapidMiner Radoop run the data prep and conduct the Machine Learning jobs directly inside Hadoop. RapidMiner SparkRM helps enable all operations and data process flows in RapidMiner Studio to run in-parallel inside Hadoop. The task and jobs created due to RapidMiner Radoop are translated into Spark and Hive. There is no need or requirement for additional software in the Hadoop cluster environment when using the RapidMiner Radoop.
- Supports Hadoop Standards & Security: While using RapidMiner Radoop, it maximises the user investment in the Hadoop ecosystem. It re-uses the existing SparkR, PySpark, Pig and HiveQL code. It helps in reducing the risk and enforce regulatory compliance with the built-in Apache Sentry & Apache Ranger support. The RapidMiner Radoop also deploys the HDFS encryption to comply with data security policies
Other products and services are:
- RapidMiner Turbo Prep
- RapidMiner Auto Model
- RapidMiner Model Ops