In the previous blog, we discussed the need for democratizing traditional data science. We saw how the advent of low-code Machine Learning(ML) solutions for businesses is a step forward in making the same possible. We also shed light on the importance of ML-Ops (a set of best practices in ML to deploy and manage ML systems and components in an agile and standardized manner) in making this happen and identified the prerequisites for low-code ML projects to be successful across enterprises.
In principle, low-code applications are intended to combine recurring ML components, templates, and microservices to eliminate repetitive code structures and reduce the algorithm weight. They are intended to serve business audiences with little or no IT experience so that they can build, train, and deploy ML models faster. However, despite their appeal, no-code/low-code ML solutions still constitute a major hurdle for most enterprises.
To begin with, it is impossible for low-code applications to entirely replace data scientists – well, at least for now. Secondly, even though the market is awash with platforms that claim to offer low-code ML platforms, the challenges that deter users from attaining success with data in traditional data science also persist in off-the-shelf, low-code, ML development platforms currently available in the market. Such challenges include data representation issues, talent and technology gaps, and infrastructure constraints, as already discussed in the previous blog.
This is where MECBot comes in.
Overview of MECBot
A Leading Data Excellence Platform for All Enterprise AI Needs
Powered by innovations in AI, Machine Learning, Natural Language Processing (NLP), and Deep Learning, MECBot solves all your data challenges in an end-to-end manner and enables insight-driven decision-making without any dependence on the underlying databases or the structure of the data. It is the go-to data analytics platform for several leading Fortune 1000 clients across the globe in Banking, Insurance, Retail, Sports, Healthcare, and more.
How It Works
MECBot’s Intelligent ML Development Platform
Accelerated Decision Intelligence Powered by Customizable, Flexible, and Simplified ML Application Building
MECBot acts as a smart assistant for business users and data teams to help them make sense of their data and address their queries as and when they arise. It is a self-service environment for users to rapidly build ML applications. Application development in MECBot is domain-driven and templated into broad business use cases like market segmentation, customer retention, lead scoring, estimating willingness to pay, customer loyalty, churn rate optimization, Marketing ROI(MROI) optimization, and more.
Users simply have to log in, configure their data pipelines, and select applicable ML templates to create custom models in a matter of minutes. The User Interface(UI) is visual, intuitive, and self-explanatory, with easy-to-use, drag-and-drop modules. This means that enterprises can unlock the superpowers of ML without toiling through the tedious process of writing heavy machine learning algorithms to predict outcomes.
MECBot’s Key Features
MECBot is Geared to Ensure Automatic Resource Allocation and Cost Optimization On-the-go
With MECBot by your side, you can finally bid goodbye to your infrastructure woes and ever-spiraling costs. Packed with advanced monitoring capabilities, MECBot can auto-scale elastically to dynamically match the demand on your system at any given point in time. Whether it is optimizing memory resources, CPU/GPU utilization, or redistributing the system load, MECBot has your back all the time.
This means that users can build, run, and manage ML without scale constraints since MECBot automates the deployment of infrastructure resources based on the needs of the models being trained, deployed, or executed. With its inherently distributed systems that automatically streamline ML workloads at each stage of the model lifecycle, resource management can happen in the background without manual supervision – whether on the cloud, on-premise or in a hybrid setup.
Quickly and Easily Identify the Best-fit Models Based on Decision-Making Needs and Cost Implications
MECBot doesn’t force you to start from ground zero. Instead, the moment you enter MECBot, it becomes your trusted partner in identifying the right ML model based on the available data, and the specific decision-making requirements, and the use cases at hand. Being well-stocked with libraries of reference models for specific use case scenarios and recommendations for the most suitable model in a given scenario, MECBot acts as an ML model marketplace with pre-defined templates and modules with a catalog of anticipated outcomes, competing models, and what-if scenarios to help business users quickly identify the best-fit model.
MECBot makes models and their outcomes explainable, observable, and responsible of its own accord. Furthermore, using plugins and APIs, users can easily add more features, enable integrations with external tools, and perform visualization of insights outside MECBot’s native environment.
MECBot Integrates ML-Ops to Ensure Integrity, Reusability, and Agility of ML Applications
To build enterprise-grade ML applications that evolve at the pace of the market is easier said than done. But, MECBot makes it a cakewalk. ML-Ops standards are implemented at each stage of the lifecycle of all ML models to make this happen. By ensuring interoperability between applications, it takes reusing of codes to the next level, saving users countless hours in the process. MECBot revolutionizes the way intelligent applications are built by enabling rapid development and deployment through automated lifecycle management using ML-Ops.
More Reasons to Choose MECBot
The Entity Domain Model(EDM) is at the Heart of MECBot
A unique aspect of MECBot that differentiates it from the herd is that it puts your business first. MECBot is based on the Entity Domain Model(EDM) which has two key components – ‘Entity’ and ‘Domain’. ‘Domain’ refers to a specific business area – like Banking, Insurance, or Retail. A logical group of attributes forms an ‘Entity’, and a group of entities constitutes a ‘Domain’.
By adopting the Business Domain Entity-Model approach, MECBot essentially makes redundant data structures a thing of the past. This means that business models can be directly created by CXOs, Data Scientists, or Data Analysts, or all of them collaboratively, while MECBot directly pulls the data from the configured sources and maps it to the specified EDM.
MECBot Simplifies The Storage and Retrieval of Large Volumes of Complex Data Using Graph Technology
MECBot overcomes the key challenges of traditional databases by deploying Knowledge Graphs. In a Knowledge Graph, data takes the back seat, while relationships between the data are first-class citizens. By linking data points in the form of a graph to generate coherent insights, MECBot ensures that data relationships underlie the ingestion and preprocessing stages. This way, it converts all ingested data instantly into linked-data format with just a few clicks.
Therefore, in MECBot it is possible to view products, customers, suppliers, materials, and so on in a single, integrated, and unified view. Graph technology also helps to expand databases by allowing data to be combined across different sources under one view, while at the same time making the data more accountable.
MECBot Auto-generates Trusted Datasets for ML Modelling
Successful adoption of data-driven decision-making depends on whether the data being fed into analytics models can be trusted. In MECBot, such Trusted Datasets come with 100% data transparency and traceability within the data management environment. Transparency in MECBot is achieved through preserving data lineage, auto-versioning, end-to-end cataloging, providing role-based access to users, and the added feature of real-time logs on access and modifications by other users made available to admins and super-admins.
Unless data is managed well, accurate insights from downstream analytics cannot be obtained. This is because in analytics, garbage in = garbage out. Hence, MECBot provides all the above features as part of its Data Preparation module, which ensures clean, end-to-end data pipeline creation which is repeatable and hydrates in real-time.
For businesses that already have clean, unified, preprocessed, and ML-ready data pipelines, MECBot’s drag-and-drop ML Module is a smart, intelligent, and turbo-charged ML development platform where complex and powerful ML algorithms can be created based on business use cases without worrying about the underlying infrastructure or the complex configuration of various technologies. Further, users can run performance and cost comparisons to identify the best-fit ML algorithms matching the decision-making needs at hand.
Try Out MECBot’s Intelligent ML Development Platform Today
MECBot’s plug-and-play ML solution is a fully managed Machine Learning service. Models can be either Supervised, Unsupervised, or Reinforcement-Learning models. Internally, all these models are based on Spark MLlib, and we support most of the models that are listed here.
If you already have an ML-ready data pipeline, the Plug-and-Play ML module of our award-winning product, MECBot, will help you to unleash the power of ML for unprecedented business intelligence and real-time insights within hours, not days or months. If you 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 you within less than half the benchmark time required by traditional solutions.
The input data can come from MECBot’s FactorDB or S3, and users can configure all models through APIs without worrying about the underlying infrastructure. Never worry about the size of the dataset again, as all the supported models are dockerized and orchestrated by Kubernetes on MECBot. Each model in MECBot has two plugins:
- A plugin for training the models, and
- A plugin for running the models (prediction).
MECBot also provides Observability (monitoring) support wherein the user can understand how much memory, disk, CPU, etc. are being consumed by the model. In short, MECBot enables Data Scientists, Citizen Data Scientists, Data Analysts, and Domain Experts to run multiple ML models on their data and choose the best one that fits their data, without worrying about the scalability, the infrastructure, or the configuration of various tools.