How Explorium’s platform compares to other AI model training and deployment platforms.

Explorium has raised $19 million to unify AI model training and deployment. Their unique platform combines both traditional ML (machine learning) and unsupervised ML capabilities into a unified platform.

This article will compare the Explorium platform to other AI model training and deployment platforms currently in the market, focusing on user experience, scalability, and cost efficiency.

Explorium raises $19 million to unify AI model training and deployment

Explorium is a Leader in Unifying AI Model Training and Deployment.

Explorium is a data science platform that helps organizations unify the development and deployment of AI models. With its platform, organizations gain visibility and control throughout the end-to-end machine learning process, from data collection to model development to production deployment.

The Explorium Data Science Platform was designed from the ground up to accommodate each organisation’s individual needs. It allows companies to take advantage of all the latest machine learning technologies, including Natural Language Processing (NLP), Image Recognition (IR), Time Series Analysis (TSR), Recommendation Systems (RS) and more. Organisations can also deploy models quickly and reliably across datasets with any size or speed requirements.

Explorium helps organisations maximise ROI by optimising their data acquisition budgets, controlling their target accuracy levels and integrating their models with existing processes or projects, including modern systems like AWS or Microsoft Azure Services. In addition, its intuitive user interface makes managing complex data pipelines easy for non-techies and experienced data scientists alike.

Developers can also take advantage of multiple features for fast development cycles – such as curating datasets by hand or generating them automatically; customising metrics; adding feature engineering methodologies; tuning hyperparameters efficiently; deploying models faster than ever before and more – all within a few clicks!

Additionally, Explorium’s Unified Impact Modeling methodology allows users to measure & track model performance in real-time from model production through post-hoc insights on how it impacts other business departments – making it easier than ever before to monitor success & optimise decision making across different divisions of the organisation using AI technology.

Comparison with Other AI Model Training and Deployment Platforms

Explorium is an AI-driven data platform that promises to provide unified training and deployment of AI Models. Their platform stands out from popular training and deployment platforms due to its integrated data library, automated feature engineering and selection, and automated model optimization.

In this article, we will examine how Explorium’s platform compares to other AI model training and deployment platforms.

Feature Comparison

Explorium’s AI training and deployment platform is designed to provide a simple and unified experience, enabling users to train models faster, make more accurate predictions, perform experiments in the data easily, and control deployments. By combining an advanced AI model with a unique data discovery and preparation system, Explorium can provide businesses an automated process for building high-performing AI models.

This feature-rich platform makes testing different algorithms against datasets easy while offering tools that guide users through the entire Machine Learning process. Here are some features which make Explorium different from other model training and deployment platforms:

• Automated machine learning capabilities: Fully automate the entire development cycle from dataset preparation to modelling experimentation and deployment using built-in or third party algorithms.
• Dynamic Data Discovery: Explorium’s data discovery engine helps businesses quickly build comprehensive datasets from internal or external sources including databases, streaming applications (IoT), web services (APIs) etc.
• Enhanced Model Experiments & Deployment: With varying parameters & hyperparameters for each testing run, businesses have maximum flexibility for optimising their model based on purpose & scale of intended operation
• Real Time Prediction Inference: Get real time predictions with no batch processing lag with deployed models such as classification & regression tasks.
• Monitoring ML Pipeline Accuracy: With dynamic accuracy monitoring over time – enabled by a sophisticated diagram builder – businesses can identify sudden changes in their pipeline behaviour quickly & act accordingly.

Usability Comparison

Explorium and its competitors have significant differences in usability when it comes to AI model training and deployment. However, Explorium offers the most intuitive platform for data scientists, with a user-friendly interface for the most important operations related to machine learning.

As a comparison, other AI models may lack certain features or require additional setup before production. For example, competitor services may limit data exploration or require manual configuration of model training pipelines. Explorium also has features absent from other platforms, such as automated feature engineering and API integration capabilities, making it even easier to use.

Explorium is also more usable from an operational perspective because it allows multiple users to collaborate on model development, which is critical when deploying AI models into production settings. With this functionality, all project users can access the same data sets, models, and predictions at any point in time, allowing for quick iteration and seamless integration into a product’s existing workflows.

In addition to its greater usability over competitor services, Explorium supports an ever-increasing list of cloud providers, making it easy to deploy AI models in production regardless of where your data resides or how complex your infrastructure needs become. This makes Explorium an ideal choice for companies looking for enterprise-ready scalability while not compromising on user experience when building complex AI-based applications.

Cost Comparison

Cost is a major factor when comparing AI model training and deployment platforms. Many existing solutions require significant upfront investments to use their services, which may not be feasible for smaller organisations.

Explorium, on the other hand, offers a pay-as-you-go model which enables customers to pay only for the features they use. This makes it an attractive option for companies of all sizes, as they can start with a small monthly fee and scale up as needed without making huge upfront investments.

Additionally, Explorium’s prices are more in line with industry standards than many of its competitors. Finally, Explorium’s pricing structure rewards companies that use the platform more frequently by offering discounts for larger volumes of usage or subscriptions over longer periods.

Explorium’s Strengths

Explorium has recently raised $19 million to unify AI model training and deployment. Through their platform, Explorium provides capabilities to help businesses quickly train, deploy, and monitor AI models.

This article will discuss the strengths of Explorium’s platform compared to other AI model training and deployment platforms.

Automated Feature Engineering

Explorium’s automated feature engineering capabilities provide customers a great advantage as they seek insights and uncover hidden signals in their data across various industries. Automated feature engineering automates the experimentation of different algorithms and combinations of features, enabling companies to rapidly build AI models that can accurately predict based on the data they possess. This helps ensure that customer AI models are built quickly, without sacrificing accuracy.

Explorium’s automated feature engineering technology is powered by proprietary algorithms developed by its experienced Machine Learning engineers who understand how to identify and optimise for key predictive factors. Using this technology, customers can immediately extract meaningful insights from their existing data, instead of having to spend time manually processing large datasets or building custom features themselves. Additionally, the platform’s intelligent feature engineering assistant allows users to understand each transformation applied to the dataset to create trustworthy predictive models faster and with fewer errors than manual approaches.

By including automated feature engineering in its platform, Explorium can ensure that its customers’ AI models are always up-to-date and relevant, resulting in more accurate predictions every time. In addition, sophisticated model evaluation techniques such as cross-validation further improve accuracy and eliminate common pitfalls caused by overfitting or bias within datasets.

Automated Model Training

Explorium’s automated model training process enables data science teams to easily discover and leverage relationships between different data sources to identify the most powerful signals. Features can rapidly be generated from the identified signals by automatically combining, transforming and cleansing the data. This eliminates a lot of manual work for data scientists and allows them to focus their time on exploring complex relationships hidden within their raw or structured datasets.

Additionally, Explorium can build markers of outlier trends that alert models quickly when something suspicious occurs, helping organisations avoid malicious activities before they become a problem. Through this platform teams can explore highly sophisticated features while also being able to explain the important relationships between their features and target variables in a unified manner.

Additionally, model training can be done faster due to an efficient use of processing power through machine learning model parallelism that allows Explorium’s users’ access to high-end hardware capabilities without spending huge amounts on them as well as an intuitive optimization pipeline that automates processes such as hyperparameter tuning, cross-validation and evaluation metrics extraction. Customers are also provided with an intuitive visual dashboard including monitoring metrics such as accuracy curves over time, allowing them to easily assess how the model performs on any task or dataset.

Automated Model Deployment

Explorium provides a suite of tools to automate the deployment process of AI models. This allows customers to quickly and securely deploy their models, without worrying about the issues with code configuration and environment setup on multiple platforms. Explorium’s pioneering deployment automation helps organisations quickly and accurately get AI applications from development in siloed teams to production-readiness at scale.

Explorium’s automated model deployment features include:

• Continuous integration/continuous delivery (CI/CD) tooling support for model builds, deployments, rollouts, etc.
• Model versioning for tracking changes in experimentation
• Automated cross-platform deployment monitoring capabilities that allow users to track how well their AI application performs in different production environments over time
• Cloud orchestration support through declaring service endpoints before deployment so they can be easily monitored after release
• Standardised authentication and authorization policies that provide a secure environment for training and deploying machine learning algorithms

Overall, Explorium’s automated model deployment makes it easier for organisations of all sizes to deploy their AI models quickly and accurately with minimal setup costs.