December 18, 2022

Machine Learning

Machine Learning

The Impact of Machine Learning

What matters to machine learning analysts is the ability to get the right information from the data and use all the know-how and tools to get an answer. Machine learning at RightData aims to do just that with a speedy and flexible approach to machine learning. The impact – better learning, faster decision-making, more valuable outcomes.

Machine learning (ML) is simply the capability of a machine to imitate intelligent human behavior and represents a subfield of the greater field of artificial intelligence (AI). In fact, ML systems can be used to perform complex tasks similar to how humans solve problems. ML also allows software applications to learn and predict outcomes without being explicitly programmed to do so – the algorithms use historical data as input to predict new output values and turn data into information, and information into knowledge.

Over the last decade, advances in data storage and processing have made it possible for all organizations to make major advances in the field of data science. Using statistical methods, what came next were ML algorithms – recipes for training data – used to make classifications or predictions. This enabled organizations to uncover key insights from massive volumes from a variety of data sources, as well as decision-making impacting key growth metrics and outcomes. In short, the impact of ML is faster decision-making and better outcomes.


RightData meets the machine learning challenge today with its Dextrus ML Studio. This software approach enables data science to meet ML challenges; and simplifies typical data tasks such as data preparation, feature engineering and selection, and algorithm selection and evaluation.

In addition, ML Studio brings IT teams closer to the business users by utilizing a low-code/no- code techniques for ML automation as well as managing repetitive tasks that helps make data

scientists more productive. Data practitioners can now make aggressive use of automated data science to empower ML and leverage data scientists for even greater efficiency.

The Different Types of Machine Learning

These can be categorized by how an algorithm learns. For example, IBM recently outlined these approaches providing “learning” using machines:

  • Supervised learning uses labeled datasets to train algorithms to classify data or predict outcomes accurately. As input data is fed into the model, the model adjusts its parameters until it has been fitted appropriately. Supervised learning helps organizations solve a variety of real-world problems at scale, such as classifying spam in a separate folder from your inbox.
  • Unsupervised learning uses machine learning algorithms to analyze and cluster unlabeled datasets. These algorithms discover hidden patterns or data groupings without the need for human intervention. This method’s ability to discover patterns in information make it ideal for exploratory data analysis, cross-selling strategies, customer segmentation, and image and pattern recognition.
  • Semi-supervised learning offers a happy medium between supervised and unsupervised learning. During training, it uses a smaller labeled data set to label the larger unlabeled dataset and guide the classification. Semi-supervised learning can solve the problem of not having enough labeled data for a supervised learning algorithm and also helps if it is too costly to label enough
  • Reinforcement learning is a machine learning model that is similar to supervised learning, but the model is trained to make a sequence of decisions. This model learns as it goes by using trial and error and a sequence of successful outcomes is reinforced to develop the best recommendation for a given problem.

In the diagram below, proft.me aptly lays out a comprehensive ML landscape, but the type of algorithm
data scientists choose depends on type of data and the type of learning outcome for the business goal.

Probably the best understanding may come from a Towards Data Science referenced diagram below, showing the ontology of the approaches as they relate to solutions. This shows the practical approach to the main models as well. Note the ontology of how problems are solved relative to chosen machine learning approach. This is where the data scientist starts – determining what approach is best for the learning outcome needed.

The 6 Core Tasks in Machine Learning

Data scientist approach problem solving in a systematic way and these core steps can be used as
repetetive and iterative tasks. This forms the basis of doing the data work necessary to get to a machine
“learned” answer.

1. Data Collection

The first step is to collect/gather the data. This generally can come from any source and is part of a data
workflow process.

2. Data Preparation

Data needs to be prepared or “wrangled” for cleansing and preparing the unorganized, missing, or noise
from the data into an optimal format, extracting notable features and performing dimensionality reduction.

3. Train Model

We then move to the modeling stage where the machine learning algorithm leverages sophisticated mathematical algorithms to learn from the historic data. This is where the choices made for the correct
algorithm begin to come into action.

4. Evaluation

Validation/Testing for the model is done to see how well it performs – a vital part of seeing how the trained model works under testing.

5. Parameter Tuning

As the ML progresses, we need to improve the model by fine-tuning parameters to maximize their

6. Prediction

Finally, the trained model is used to answer the questions. So, this prediction step is where we get to see the point of all this work – an outcome or decision – where the value of machine learning is realized.

When the Dextrus software platform was conceived, the goal was to unify the data wrangling and machine learning experience for both data scientists and business users. This has been realized now where every data practitioner can learn from their precious data products – all within the same data machine learning workflow.

ML Studio was built into the Dextrus platform as a no-code solution, providing an easy-to-use component that simplifies machine learning tasks for quicker outcomes. Dextrus enables users to build machine learning intelligence to grow their business as well as helping build classification models that allow software applications to become more accurate at predicting outcomes. More info can be seen at

Further, Dextrus can be used in a wide variety of applications and can solve real-world problems such as customer behavior prediction, document classification, spam filtering, product categorization, customer churn prediction, and credit card fraud detection. It is a powerful and flexible machine learning software. The data flow moves from the source, right into Dextrus, where with only a few clicks, enables the user to build a model covering the core tasks in machine learning. Below is the workflow of Machine Learning in ML studio. Note the four elements of data preprocessing, selecting estimator, tuning estimator, and a new data predictor.

Lifecycle of Machine Learning in Dextrus ML Studio

The Future of Machine Learning

The future of machine learning, in a word, would be easier. It might not have seemed plausible a few years ago that something so powerful and complex could be utilized for decisions every day across all aspects of learning. Today, machine learning makes it possible to move faster with more accurate outcomes – all with the impact of greater customer satisfaction and competitive advantage. Data scientists are fast becoming multi-skilled with both statistical and machine learning talents.

Use cases continue to grow, with innovation at the machine learning level. One major future need will surround the data management or wrangling, where the need for accurate trusted data will be paramount as ML systems are fed for outcomes.

At the human level, it is all about unifying data scientists and business users for the even greater data revolution underway today. The combination of data wrangling and machine learning, on an integrated platform is happening at a quick pace.

Machine learning is vast and without the tools and know-how, it is exceedingly difficult to get accurate results from the data. With Dextrus ML Studio, the whole machine learning process is simplified with the aim of no-code to build models and solve real business problems quickly.

About the Authors

Suresh Saguturu serves as Vice President Of Product Development and Customer Success at RightData Inc. With extensive data consulting experience with top firms such as Coca-Cola, Bank of America, and Nike, Suresh has demonstrated both architecture acumen and project leadership, including deep expertise for SAP and S/4 HANA systems. Suresh holds many data certifications and a bachelor’s degree from Acharya Nagarjuna University. suresh@getrightdata.com

Bindu Bharatha serves as Senior Data Scientist and is developing RightData’s machine learning platform, Dextrus ML Studio. She has been instrumental in the development of data science components and architecture surrounding machine learning strategies. She holds a Master’s degree in Data Science and Business Analytics from Wayne State University. bindu.bharatha@getrightdata.com

About RightData

RightData is a trusted total software company that empowers end-to-end capabilities for modern data, analytics, and machine learning using modern data lakehouse and data mesh frameworks. The combination of Dextrus software for data integration and the RDt for data quality and observability provides a comprehensive DataOps approach. With a commitment to a no-code approach and a user friendly user interface, RightData increases speed to market and provides significant cost savings to its customers. www.getrightdata.com