Low-code ML is Here to Stay - Here’s How MECBot Can Help You Make the Most of It

In the last blog, we introduced mecbot.ml - a novel, innovative drag-and-drop platform built on top of MECBot that fosters the development of Machine Learning(ML) applications for businesses with just a few clicks. MECBot’s plug-and-play ML solution is a fully managed Machine Learning service where models can be either Supervised, Unsupervised, or Reinforcement-Learning models.

Accelerated Decision Intelligence with Mecbot

However, despite low-code ML being all the rage right now, not all organizations stand to gain from it due to specific challenges and constraints that come with the data at hand. In analytics, garbage in = garbage out. This means that businesses that do not have ML-ready data pipelines configured and squarely in place are not in a position to capitalize on auto-ML applications to the fullest extent.

With MECBot, unlike most other low-code ML solutions, not having an ML-ready data pipeline configured does not mean that businesses have hit a dead-end. Instead, mecbot.io - our flagship data excellence platform - takes care of it in a seamless, integrative, and hassle-free manner. Since the democratization of ML using a low-code approach is our ultimate goal, it is important that data sourcing, preparation, and management do not hinder businesses from using our best-in-class ML development platform.

In other words, if a business already has an ML-ready data pipeline, the Plug-and-Play ML module of our award-winning product, MECBot, will help the business to unleash the power of ML for unprecedented business intelligence and real-time insights within hours, not days or months. If they don’t have an ML-ready data pipeline or have infrastructure constraints that need to be overcome, MECBot’s Data Preparation module will do it for them within less than half the benchmark time required by traditional solutions.

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In this blog, we share the key prerequisites for any business to be able to make the most of low-code ML using mecbot.ml

Let’s get started!

Low-code ML Prerequisites

#1 Businesses Need Clean, Pre-processed, and Unified Data to Make the Most of mecbot.ml

The biggest challenge that businesses face when trying to use any low-code ML development platform is the sheer volume of inconsistencies in the data value chain (the process of collection, publication, uptake, and impact of data). Inconsistent data formats and the lack of inter-domain and intra-domain relationships in traditional databases also complicate the scenario.

Consuming data from different sources may also lead to duplicates or inconsistent values that need to be validated and de-duplicated. It is no surprise, therefore, that data scientists have to spend over 80% of their precious time on preparing the data for analysis and ML models, instead of actually ideating and running the said models to generate valuable and timely insights.

Data Preparation

Image Courtesy: Forbes | Cleaning Big Data: Most Time-Consuming, Least Enjoyable Data Science Task, Survey Says | Souce URL: 

https://www.forbes.com/sites/gilpress/2016/03/23/data-preparation-most-time-consuming-least-enjoyable-data-science-task-survey-says/?sh=789a27dd6f63

To mitigate this problem, businesses and their data teams need an intelligent and dynamic pipeline engine that automates the following functions:

  • Ingest data from a variety of internal and external sources.
  • Clean, preprocess and massage the data.
  • Unify structured and unstructured data.
  • Define data relationships with Entity Domain Model.
  • Augment data by connecting with its domain of origin.
  • Convert all ingested data instantly into graph format without any coding by the user.
  • Enable Data Transformations by users.
  • Generate flattened views of data for discovery & visualization.
  • Preserve lineage, carry out smart cataloging & keep all data and views hydrated with new inputs.

#2 Businesses Need to Auto-generate Trusted Datasets So That The Results Derived from ML Models Are Accurate and Not Misleading.

The urgency to transform data into data governed insights in real-time is growing at an exponentially rapid pace. However, enterprises are struggling to attain trusted datasets that can be fed to ML models for accurate results.
A trusted data set has 3 key attributes:

  • Data Security – Integrity of data in motion, at rest, or in transit. 
  • Data Privacy – Role-based access to data & complete transparency. 
  • Speed and Scale – Managing large volumes of data at the scale of data generation by efficiently using hardware resources.

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Data security and data governance are of prime importance in auto-generating trusted data. This is made possible through data lineage, versioning, and a clear audit trail that shows how the data is getting transformed, who has changed it, when it was changed, and so on. Further, admin users must have the right to restore each of the previous versions of the data. It is also essential that repeatability is built-in into the system to ensure that once a model is stored, the entire pipeline is scheduled to run automatically without any human intervention.

89% of executives agree that inaccurate data impedes their ability to provide consistent customer satisfaction. Further, without proper governance of data, there is limited accountability and scattered ownership of data, which creates compliance risks as well as higher expenses and lost revenue.

#3 Businesses Need to Be Able to Create Flattened Data Views without Writing Any Complex SQL Joins.

The storage and retrieval of large volumes of complex data is a pain in the neck for data teams. When establishing data relationships at the storage and retrieval levels, complex SQL joins are one of the biggest stumbling blocks when it comes to making the most of low-code ML. At the heart of this problem is the lack of a dynamic, intelligent storage system that inherently connects data through relationships. Unresolved storage complexities also mean that data needs to be teleported or moved around from one tool to another in order to run ML models successfully. This further compromises the data integrity and its suitability for advanced modeling using ML.

Conclusion

FORMCEPT understands that unless data is managed well, accurate insights from downstream analytics cannot be obtained. MECBot provides all these features as part of its Augmented Data Management module. It ensures clean, end-to-end data pipeline creation which is repeatable and hydrates in real-time. Then, once all the data is available, MECBot’s in-built plugins and pipelines come in, so that powerful AI and ML algorithms can be created without worrying about the underlying infrastructure or complex configuration of different technologies. This way, only the best-fit ML algorithms can be selected to obtain the best and the most accurate results for the data in question and the decision-making need at hand. 

In the next blog, we will shed light on how MECBot takes care of all data preparation and pre-processing needs at the enterprise level to make businesses ready to unleash the power of ML.

Interested to know more about MECBot? Visit www.mecbot.ai. To know about the state-of-the-art technologies we use, check out our platform architecture here: https://formcept.com/platform

Wish to take a deep dive into what MECBot can do for your business? Request a demo here: https://formcept.com/contact-us