ML Ops

Collaborate, Co-create, Cultivate!

ML Ops

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Effortlessly build Analytical Models with a Visual Designer!
No-code, Low-code, and Drag-and-drop visual model-builder
Build, train-test-deploy, publish, and manage multiple RubiModel versions
Integrate Python & R-based models and custom coding


Features	Functions
Python and R support for custom coding Model validation and drift over time Create lift table for interval and categorical targets Flexibility to choose a specific or default model version Build a robust model by leveraging advanced configurations for each algorithm Publish & reuse the selected models Maintain multiple versions of models	Analytics Engineering Pipeline Data Exploration Statistical Analysis & Hypothesis Testing Data Pre-processing Data Wrangling Model Development & Life cycle Model Engineering Model Training Model Experimentation Model Monitoring Regression Classification Clustering Anomaly detection

Boost your Analytical Creativity with coherent Predictive Models!
Wizard-like interface for quick, simple models/predictors
Compare multiple algorithms for supervised and unsupervised tasks.
Get instant insights and assess model performance through detailed metrics.Augmented Machine Learning – AutoML


Features	Functions
Instant insights from your data Clean and enrich data interactively Supports multiple types of profiling Web-based development environment for programming Deployment to Production in one click API powered support for your apps Create, train & deploy advanced custom ML models Sophisticated and thorough model assessment Deep Learning at your fingertips Highly scalable, distributed analytical processing Enterprise level security with fine-grained access rights	Classification, Clustering & Regression Algorithms Anomaly detection Multiple types of statistical analysis like anomaly detection, correlation, descriptive and inferential statistics Data Wrangling functions like Label Encoder, Normalization, and PCA Rare events detection and handling Template Table and Template File functionality Multiple sampling types Deep Learning Support Vertical and horizontal autoscaling, also up and down autoscaling for sudden workloads GPU-based scale model training Support for Keras and Tensorflow backend, integrated with Tensorboard

Efficiently Generate Scientific Forecasts!
Be future-ready. Streamline and automate the forecasting process!.No manual intervention. Seamlessly explore and analyze the time series data.
Visualize insights over time and deploy models to production!.Incorporate forecasting as an ongoing & repeated process into the organization’s planning workflow!


Features	Functions
Easy-to-use GUI New Project wizard to generate automatic forecasts in batch Scalability and modeling options to choose the level of forecasting automation Automated time-series feature engineering to discover predictive signals Time-series characterization Segmentation of the data into subsets Structural manipulation of the collective time-series (hierarchy exploration)	Time-series Forecasting Data management services like data accumulation, selecting timed intervals, and start and end dates Time-series Visualization – Decomposition plots Modeling using ARIMA, Auto ARIMA, Seasonal ARIMA, LSTM, Exponential Smoothing, and many more algorithms Time-series diagnosis using Auto-correlation Function Plots Post- and pre-processing functionalities Smoothening methods

Extract Deeper Insights from Textual Data!
RubiText is a set of linguistic, statistical, and ML techniques such as Word Frequency Analysis, Pattern Recognition, Tagging/Annotation, Information Extraction, Link & Association Analysis, and Predictions. Such methods can make a more profound sense of your textual data and deliver insights for effective decision-making.


Features	Functions
High performance text mining procedures Term Profiling User-friendly flexible interface Integrated document filtering Visual analysis of results Interactive text importing interface Document-level sentiment insights Sophisticated dimension reduction techniques Advanced filtering through weighting Integrated spell checking Transformation of qualitative data into compact formats	Text Mining Textual Analytics Textual pre-processing like Speech Tagging, Stemming, Lemmatization and Tokenization Text Vectorization consisting of Count Vectorizer and TF-IDF Frequency Analysis including Collocations, Advanced Entity and Relation Extraction Sentiment Detection using basic sentiment analysis Text Classification algorithms like Naïve Bayes, Maximum Entropy and SVM Text Clustering algorithms based on centroid, density, hierarchy and incremental learning Corpus/Dictionary Management for sentiment polarity and stop-word corpus

Data Consumers

Those who turn raw data into meaningful information and deep insights can be called the ‘alchemists’ of the 21st century. Data consumers are mathematicians who use statistics, logical mindset, and insights to transform businesses. With a keen eye for the quality of data, they are highly sensitive toward its practical application.

Our Envisioned Personas for Data Consumers

Data Scientist

Statistician

Sell all Use Cases

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Objective
Model, summarize and visualize data by handling various tasks involving data wrangling, visualization,and processing large amounts of data.

Knowledge Map

Big data infrastructure, machine learning, data mining, and statistical pattern recognition.
Application of learning algorithms to building smart robots, text understanding, computer vision, medical informatics, audio, database mining
Best practices in machine learning

Certiﬁcation Code: RS_ML_RML

ML Ops

Data Consumers

Data Consumers

Our Envisioned Personas for Data Consumers

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