Home Product Solutions Pricing Docs Blogs Contact Us

A better way towards Machine Learning

The last no-code AI platform you will ever need. Simplismart makes it easier for researchers and businesses to build and deploy machine learning models. It lets you manage your machine learning lifecycle within minutes without any coding. It combines the power of low-level APIs and AutoML to give you an intuitive, transparent and flexible model-building experience.

SimpliIngest

Simplismart offers a range of data ingestion and analysis features that allow you to easily import and process data from various sources, including local storage, Amazon S3, Google Cloud Storage, and more.

The platform is designed to make it easy for you to import, clean, and analyze your data, no matter where it's stored or in its format.

With Simplismart, you can easily import data from various file formats, including CSV, JSON, parquet, and Excel, as well as from databases and cloud storage systems.

SimpliTrain

Train a deep learning model on any dataset you can get your hands on using our declarative interfaces and editable auto-generated configurations. We support 14+ data types, including but not limited to text, image, audio, video, geospatial and more.

Try out different architectures to train the model and build your AutoML. Run multiple experiments using distributed training.

The platform automatically detects the data types in your dataset, intelligently infers the best configuration to train, and assembles a custom deep-learning model on the fly.

SimpliTrack

Simplismart offers a tracking feature that allows you to easily monitor and analyze the performance of your machine-learning models as they train. With this feature, you can see key metrics and parameters for all of your experiments in one place, making it easy to compare the performance of different models and identify any trends or patterns.

One of the critical features of Simplismart's experiment tracking is the ability to view graph visualizations of your model's performance. These graphs allow you to see how your model's accuracy, loss, and other vital metrics change over time, helping you to understand the progress of your training and identify any issues.

SimpliEvaluate

Our product allows you to evaluate your custom models on various datasets, giving you the flexibility to test their performance and accuracy under different conditions. This feature helps you understand how your models behave in different scenarios, giving you the insights you need to optimize their performance and improve their reliability.

With the ability to evaluate your models on different datasets, you can quickly and easily identify any weaknesses or areas for improvement. It helps you identify any issues with your models and take corrective action, ensuring they deliver the best possible results.

SimpliExplain

Simplismart's model interpretation and explanation feature allow you to understand the inner workings of your deep learning models and gain insights into how they are making predictions.

You can visualize the decisions made by your model at different levels of abstraction, allowing you to see how various features and patterns in your data are influencing the model's predictions. It can help you to identify any biases or errors in your model and make adjustments to improve its performance.

In addition to visualization tools, Simplismart offers various algorithms and techniques for interpreting and explaining deep learning models. These include feature importance, attribution maps, SHAP values, and sensitivity analysis, which can help you to understand the relationships between your data and the model's predictions.

SimpliDeploy

Simplismart's autoscaling feature allows you to automatically adjust the resources allocated to your model deployments based on your latency and throughput service level agreements (SLAs). It ensures that your models can meet your application's performance requirements while maximizing efficiency and minimizing costs.

With Simplismart's autoscaling feature, you can specify the minimum and maximum number of resources allocated to your model deployments, as well as the target latency and throughput you want to achieve. The platform will then automatically adjust the number of resources allocated to your models based on real-time usage data, ensuring that your models meet your performance requirements.

SimpliObserve

Simplismart's observability feature allows you to monitor and analyze the performance and behaviour of your deep learning models in real time. With this feature, you can gain insights into your models' use, identify any issues or errors, and make adjustments to improve their performance.

Simplismart's observability feature includes both functional and non-functional monitoring capabilities. Functional monitoring allows you to track the input and output of your models, as well as the intermediate calculations and decisions they make. It can help you understand how your models process data and make predictions and identify any issues or errors affecting their performance.

Non-functional monitoring, on the other hand, allows you to track the resources and infrastructure your models are running on. It includes information on CPU and GPU usage, memory and storage utilization, and network performance. Non-functional monitoring can help you identify any bottlenecks or resource constraints affecting your models' performance and make adjustments to optimize their efficiency.

Simplismart

Simplismart makes it easier for researchers and businesses to build and deploy machine learning models. It lets you manage your machine learning lifecycle within minutes without any coding.

Company

About Blog Contact

Products

Features Pricing Use Cases

Resources

Documentation Support Community