AI

Toolkit

Model Serving

Deploy models as scalable APIs and enable seamless integration into your applications. These tools offer high-performance model-serving capabilities, ensuring fast and reliable predictions for your AI-driven applications.

Featured Apps and Tools

Other Model Serving Apps and Tools

TensorFlow is an open-source machine learning library that supports multiple programming languages including Python and Javascript. TensorFlow provides various tools for building and bringing deep learning models to production. The library became more popular in its second version(TensorFlow 2.x) because of integrating Keras as its high-level API. Keras is an open-source library that provides a simple to use Python interface for neural networks.

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PyTorch is an open source machine learning library based on the Torch library, used for applications such as computer vision and natural language processing, primarily developed by Facebook‚Äôs AI Research lab

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Aible is a set of tools that enables you to try out a lot of scenarios to see how different assumptions would impact the business. Aible also adjusts its AIs based on your expectations about how the data in the future would be different than the original AI training data. Then, it creates a portfolio of AI models that would be optimal in each of those and other scenarios, and deploys the models automatically.

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Cortex is a Kubernetes-based serverless platform built for AWS. It allows you to build data processing pipelines that can scale to handle large structured or unstructured data sets. You can also deploy web scraping, file processing, and other compute-intensive workloads without managing instances, autoscaling groups, or load balancers.

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BentoML is an open-source platform for serving, managing, and deploying high-performance machine learning models. It supports all major ML frameworks and works with the most popular DevOps and infrastructure tools.

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Polyaxon is a platform for automating and reproducing deep learning and machine learning applications. It has a full set of tools for MLOps. Polyaxon offers powerful features built directly into the core software which can be customized and configured based on the needs of each individual deployment.

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Seldon is a suite of tools that allow individuals, teams, and organizations to have the freedom to package and serve models built in any ML tool. Its suite of tools is composed of the following: Seldon core: An open-source Kubernetes deployment platform that makes it easier and faster to deploy ML models and experiments at scale on Kubernetes. Seldon Deploy: Enterprise solution for managing ML models in production. It allows ML deployment with peak efficiency, minimal risk, and the shortest time-to-value. Alibi: A Machine model inspection and interpretation library.

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Algorithmia is a machine learning model deployment and management solution that automates the MLOps for an organization.

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More AI Toolkit categories

Building POC application

Simplify your development process while showcasing the practicality of your AI projects. With our recommended tools you can create, test, and deploy proof-of-concept applications

Code Versioning

Optimize collaboration among your team members, ensure code integrity, and simplify the deployment process. These tools provide efficient code version control, making it easier to manage complex AI and Machine Learning projects.

Data Exploration and Analysis

Visualize and analyze your datasets, identify patterns, and make informed decisions. These tools offer a range of features, from data visualization to statistical analysis, empowering you to explore and understand your data in-depth.

Data Labelling and Annotation

Accelerate the training of your machine learning models by providing high-quality labeled datasets. These tools offer intuitive interfaces, annotation options, and collaboration features, ensuring accurate and consistent data labeling for your AI projects.

Data Versioning

Keep track of changes made to your datasets, improve reproducibility, and simplify collaboration. These tools enable you to manage and version your data effectively, enhancing the reliability and accuracy of your AI and Machine Learning workflows.

Development IDE

Improve your productivity, streamline your coding process, and leverage advanced debugging and testing features. These IDEs offer a seamless development experience, providing the necessary tools for building and deploying AI applications.

Experiment Tracking

Stay organized, monitor model performance, and reproduce results easily. These tools offer experiment-tracking features, allowing you to record, compare, and analyze your experiments, improving the efficiency and effectiveness of your AI workflows.

Feature Engineering

Extract, transform, and select features to enhance the performance of your machine learning models. These tools offer a range of feature engineering techniques, empowering you to create meaningful and predictive features from your data.

Simplify feature reuse, ensure consistency, and accelerate model development. These tools provide centralized repositories for storing and accessing features, enabling easy collaboration and improving the productivity of your AI projects.

Model Debbugging and Visualization

Gain insights into model behavior, identify issues, and improve model performance. These tools offer advanced debugging and visualization capabilities, helping you understand and optimize your models for better predictions and outcomes.

Model Monitoring

Detect anomalies, ensure model reliability, and take proactive actions. These tools offer real-time monitoring features, allowing you to track model performance and make data-driven decisions to maintain optimal model performance.

Model Packaging

Simplify model deployment, ensure consistency, and enable seamless integration into your applications. These tools offer comprehensive model packaging features, making it easier to share and distribute your trained models.

Simplify model versioning, track model lineage, and facilitate collaboration. These tools provide centralized repositories for storing and managing your models, enhancing reproducibility and enabling efficient model sharing.

Deploy models as scalable APIs and enable seamless integration into your applications. These tools offer high-performance model-serving capabilities, ensuring fast and reliable predictions for your AI-driven applications.

Streamline the training and optimization of your machine learning models. These tools offer advanced training algorithms, distributed computing capabilities, and efficient resource utilization, empowering you to train models faster and more effectively.

Model Tuning and HPO

Automate the search for optimal hyperparameters, improve model accuracy, and save time and resources. These tools offer intelligent algorithms and efficient search strategies, enabling you to fine-tune your models for optimal performance.

Workflow Orchestration

Automate complex workflows, ensure reproducibility, and optimize resource utilization. These tools offer intuitive interfaces, workflow scheduling, and dependency management features, empowering you to streamline and scale your AI projects.

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