Best Machine Learning Software for Startups - Page 12
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Compare the Top Machine Learning Software for Startups as of October 2025 - Page 12
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ONNX
ONNX
ONNX defines a common set of operators - the building blocks of machine learning and deep learning models - and a common file format to enable AI developers to use models with a variety of frameworks, tools, runtimes, and compilers. Develop in your preferred framework without worrying about downstream inferencing implications. ONNX enables you to use your preferred framework with your chosen inference engine. ONNX makes it easier to access hardware optimizations. Use ONNX-compatible runtimes and libraries designed to maximize performance across hardware. Our active community thrives under our open governance structure, which provides transparency and inclusion. We encourage you to engage and contribute. -
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Apache Mahout
Apache Software Foundation
Apache Mahout is a powerful, scalable, and versatile machine learning library designed for distributed data processing. It offers a comprehensive set of algorithms for various tasks, including classification, clustering, recommendation, and pattern mining. Built on top of the Apache Hadoop ecosystem, Mahout leverages MapReduce and Spark to enable data processing on large-scale datasets. Apache Mahout(TM) is a distributed linear algebra framework and mathematically expressive Scala DSL designed to let mathematicians, statisticians, and data scientists quickly implement their own algorithms. Apache Spark is the recommended out-of-the-box distributed back-end or can be extended to other distributed backends. Matrix computations are a fundamental part of many scientific and engineering applications, including machine learning, computer vision, and data analysis. Apache Mahout is designed to handle large-scale data processing by leveraging the power of Hadoop and Spark. -
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AWS Neuron
Amazon Web Services
It supports high-performance training on AWS Trainium-based Amazon Elastic Compute Cloud (Amazon EC2) Trn1 instances. For model deployment, it supports high-performance and low-latency inference on AWS Inferentia-based Amazon EC2 Inf1 instances and AWS Inferentia2-based Amazon EC2 Inf2 instances. With Neuron, you can use popular frameworks, such as TensorFlow and PyTorch, and optimally train and deploy machine learning (ML) models on Amazon EC2 Trn1, Inf1, and Inf2 instances with minimal code changes and without tie-in to vendor-specific solutions. AWS Neuron SDK, which supports Inferentia and Trainium accelerators, is natively integrated with PyTorch and TensorFlow. This integration ensures that you can continue using your existing workflows in these popular frameworks and get started with only a few lines of code changes. For distributed model training, the Neuron SDK supports libraries, such as Megatron-LM and PyTorch Fully Sharded Data Parallel (FSDP). -
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AWS Trainium
Amazon Web Services
AWS Trainium is the second-generation Machine Learning (ML) accelerator that AWS purpose built for deep learning training of 100B+ parameter models. Each Amazon Elastic Compute Cloud (EC2) Trn1 instance deploys up to 16 AWS Trainium accelerators to deliver a high-performance, low-cost solution for deep learning (DL) training in the cloud. Although the use of deep learning is accelerating, many development teams are limited by fixed budgets, which puts a cap on the scope and frequency of training needed to improve their models and applications. Trainium-based EC2 Trn1 instances solve this challenge by delivering faster time to train while offering up to 50% cost-to-train savings over comparable Amazon EC2 instances. -
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AtomBeam
AtomBeam
There’s no hardware to buy, no changes that need to be made to your network, and only a simple installation of a small library of software. By 2025, 75% of all enterprise-generated data, or 90 zettabytes, will be from IoT. To give a sense of scale, all of the storage capacity of every data center in the world today adds up to less than two zettabytes. Moreover, 98% of IoT data is unsecured, but all of it should be secured. Battery life for sensors is a major concern, with little relief on the horizon. Wireless data transmission range is a problem for many IoT users. We think that AtomBeam will impact IoT in the same way the electric light changed everyday living. Many key impediments to IoT adoption can be overcome with the simple addition of our compaction software. With our software alone, you can improve security, extend battery life, and increase transmission range. AtomBeam offers the opportunity for significant discounts on connectivity and cloud storage costs. -
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UpTrain
UpTrain
Get scores for factual accuracy, context retrieval quality, guideline adherence, tonality, and many more. You can’t improve what you can’t measure. UpTrain continuously monitors your application's performance on multiple evaluation criterions and alerts you in case of any regressions with automatic root cause analysis. UpTrain enables fast and robust experimentation across multiple prompts, model providers, and custom configurations, by calculating quantitative scores for direct comparison and optimal prompt selection. Hallucinations have plagued LLMs since their inception. By quantifying degree of hallucination and quality of retrieved context, UpTrain helps to detect responses with low factual accuracy and prevent them before serving to the end-users. -
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WhyLabs
WhyLabs
Enable observability to detect data and ML issues faster, deliver continuous improvements, and avoid costly incidents. Start with reliable data. Continuously monitor any data-in-motion for data quality issues. Pinpoint data and model drift. Identify training-serving skew and proactively retrain. Detect model accuracy degradation by continuously monitoring key performance metrics. Identify risky behavior in generative AI applications and prevent data leakage. Protect your generative AI applications are safe from malicious actions. Improve AI applications through user feedback, monitoring, and cross-team collaboration. Integrate in minutes with purpose-built agents that analyze raw data without moving or duplicating it, ensuring privacy and security. Onboard the WhyLabs SaaS Platform for any use cases using the proprietary privacy-preserving integration. Security approved for healthcare and banks. -
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Shaip
Shaip
Shaip offers end-to-end generative AI services, specializing in high-quality data collection and annotation across multiple data types including text, audio, images, and video. The platform sources and curates diverse datasets from over 60 countries, supporting AI and machine learning projects globally. Shaip provides precise data labeling services with domain experts ensuring accuracy in tasks like image segmentation and object detection. It also focuses on healthcare data, delivering vast repositories of physician audio, electronic health records, and medical images for AI training. With multilingual audio datasets covering 60+ languages and dialects, Shaip enhances conversational AI development. The company ensures data privacy through de-identification services, protecting sensitive information while maintaining data utility. -
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Qualdo
Qualdo
We are a leader in Data Quality & ML Model for enterprises adopting a multi-cloud, ML and modern data management ecosystem. Algorithms to track Data Anomalies in Azure, GCP & AWS databases. Measure and monitor data issues from all your cloud database management tools and data silos, using a single, centralized tool. Quality is in the eye of the beholder. Data issues have different implications depending on where you sit in the enterprise. Qualdo is a pioneer in organizing all data quality management issues through the lens of multiple enterprise stakeholders, presenting a unified view in a consumable format. Deploy powerful auto-resolution algorithms to track and isolate critical data issues. Take advantage of robust reports and alerts to manage your enterprise regulatory compliance. -
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Zama
Zama
Improve patient care while maintaining privacy by allowing secure, confidential data sharing between healthcare providers. Facilitate secure financial data analysis for risk management and fraud detection, keeping client information encrypted and safe. Create targeted advertising and campaign insights in a post-cookie era, ensuring user privacy through encrypted data analysis. Enable data collaboration between different agencies, while keeping it confidential from each other, enhancing efficiency and data security, without revealing secrets. Give the ability to create user authentication applications without having to reveal their identities. Enable governments to create digitized versions of their services without having to trust cloud providers. -
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Hive AutoML
Hive
Build and deploy deep learning models for custom use cases. Our automated machine learning process allows customers to create powerful AI solutions built on our best-in-class models and tailored to the specific challenges they face. Digital platforms can quickly create models specifically made to fit their guidelines and needs. Build large language models for specialized use cases such as customer and technical support bots. Create image classification models to better understand image libraries for search, organization, and more. -
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Eternity AI
Eternity AI
Eternity AI is building an HTLM-7B, a machine learning model that knows what the internet is and how to access it to generate responses. Humans don't make decisions based on 2-year-old data. For a model to think like a human, it needs to get access to real-time knowledge and everything about how humans behave. Members of our team have previously published white papers and articles on topics related to on-chain vulnerability coordination, GPT database retrieval, decentralized dispute resolution, etc. -
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Adept
Adept
Adept is an ML research and product lab building general intelligence by enabling humans and computers to work together creatively. Designed and trained specifically for taking actions on computers in response to your natural language commands. ACT-1 is our first step towards a foundation model that can use every software tool, API and website that exists. Adept is building an entirely new way to get things done. It takes your goals, in plain language, and turns them into actions on the software you use every day. We believe that AI systems should be built with users at the center — where machines work together with people in the driver's seat, discovering new solutions, enabling more informed decisions, and giving us more time for the work we love. -
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3LC
3LC
Light up the black box and pip install 3LC to gain the clarity you need to make meaningful changes to your models in moments. Remove the guesswork from your model training and iterate fast. Collect per-sample metrics and visualize them in your browser. Analyze your training and eliminate issues in your dataset. Model-guided, interactive data debugging and enhancements. Find important or inefficient samples. Understand what samples work and where your model struggles. Improve your model in different ways by weighting your data. Make sparse, non-destructive edits to individual samples or in a batch. Maintain a lineage of all changes and restore any previous revisions. Dive deeper than standard experiment trackers with per-sample per epoch metrics and data tracking. Aggregate metrics by sample features, rather than just epoch, to spot hidden trends. Tie each training run to a specific dataset revision for full reproducibility. -
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Ensemble Dark Matter
Ensemble
Train accurate ML models on limited, sparse, and high-dimensional data without extensive feature engineering by creating statistically optimized representations of your data. By learning how to extract and represent complex relationships in your existing data, Dark Matter improves model performance and speeds up training without extensive feature engineering or resource-intensive deep learning, enabling data scientists to spend less time on data and more time-solving hard problems. Dark Matter significantly improved model precision and f1 scores in predicting customer conversion in the online retail space. Model performance metrics improved across the board when trained on an optimized embedding learned from a sparse, high-dimensional data set. Training XGBoost on a better representation of the data improved predictions of customer churn in the banking industry. Enhance your pipeline, no matter your model or domain. -
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Simplismart
Simplismart
Fine-tune and deploy AI models with Simplismart's fastest inference engine. Integrate with AWS/Azure/GCP and many more cloud providers for simple, scalable, cost-effective deployment. Import open source models from popular online repositories or deploy your own custom model. Leverage your own cloud resources or let Simplismart host your model. With Simplismart, you can go far beyond AI model deployment. You can train, deploy, and observe any ML model and realize increased inference speeds at lower costs. Import any dataset and fine-tune open-source or custom models rapidly. Run multiple training experiments in parallel efficiently to speed up your workflow. Deploy any model on our endpoints or your own VPC/premise and see greater performance at lower costs. Streamlined and intuitive deployment is now a reality. Monitor GPU utilization and all your node clusters in one dashboard. Detect any resource constraints and model inefficiencies on the go. -
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Invert
Invert
Invert offers a complete suite for collecting, cleaning, and contextualizing data, ensuring every analysis and insight is based on reliable, organized data. Invert collects and standardizes all your bioprocess data, with powerful, built-in products for analysis, machine learning, and modeling. Clean, standardized data is just the beginning. Explore our suite of data management, analysis, and modeling tools. Replace manual workflows in spreadsheets or statistical software. Calculate anything using powerful statistical features. Automatically generate reports based on recent runs. Add interactive plots, calculations, and comments and share with internal or external collaborators. Streamline planning, coordination, and execution of experiments. Easily find the data you need, and deep dive into any analysis you'd like. From integration to analysis to modeling, find all the tools you need to manage and make sense of your data. -
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AI Verse
AI Verse
When real-life data capture is challenging, we generate diverse, fully labeled image datasets. Our procedural technology ensures the highest quality, unbiased, labeled synthetic datasets that will improve your computer vision model’s accuracy. AI Verse empowers users with full control over scene parameters, ensuring you can fine-tune the environments for unlimited image generation, giving you an edge in the competitive landscape of computer vision development. -
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Azure AI Foundry
Microsoft
Azure AI Foundry is a unified application platform for your entire organization in the age of AI. Azure AI Foundry helps bridge the gap between cutting-edge AI technologies and practical business applications, empowering organizations to harness the full potential of AI efficiently and effectively. Azure AI Foundry is designed to empower your entire organization—developers, AI engineers, and IT professionals—to customize, host, run, and manage AI solutions with greater ease and confidence. This unified approach simplifies the development and management process, helping all stakeholders focus on driving innovation and achieving strategic goals. Azure AI Foundry Agent Service is a powerful component designed to facilitate the seamless operation of AI agents throughout the entire lifecycle—from development and deployment to production. -
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SquareML
SquareML
SquareML is a no-code machine learning platform designed to democratize access to advanced data analytics and predictive modeling, particularly in the healthcare sector. It enables users, regardless of technical expertise, to harness machine learning capabilities without extensive coding knowledge. The platform specializes in data ingestion from multiple sources, including electronic health records, claims databases, medical devices, and health information exchanges. Key features include a no-code data science lifecycle, generative AI models for healthcare, unstructured data conversion, diverse machine learning models for predicting patient outcomes and disease progression, a library of pre-built models and algorithms, and seamless integration with various healthcare data sources. SquareML aims to streamline data processes, enhance diagnostic accuracy, and improve patient care outcomes by providing AI-powered insights. -
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Amazon EC2 Capacity Blocks for ML enable you to reserve accelerated compute instances in Amazon EC2 UltraClusters for your machine learning workloads. This service supports Amazon EC2 P5en, P5e, P5, and P4d instances, powered by NVIDIA H200, H100, and A100 Tensor Core GPUs, respectively, as well as Trn2 and Trn1 instances powered by AWS Trainium. You can reserve these instances for up to six months in cluster sizes ranging from one to 64 instances (512 GPUs or 1,024 Trainium chips), providing flexibility for various ML workloads. Reservations can be made up to eight weeks in advance. By colocating in Amazon EC2 UltraClusters, Capacity Blocks offer low-latency, high-throughput network connectivity, facilitating efficient distributed training. This setup ensures predictable access to high-performance computing resources, allowing you to plan ML development confidently, run experiments, build prototypes, and accommodate future surges in demand for ML applications.
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Amazon EC2 UltraClusters
Amazon
Amazon EC2 UltraClusters enable you to scale to thousands of GPUs or purpose-built machine learning accelerators, such as AWS Trainium, providing on-demand access to supercomputing-class performance. They democratize supercomputing for ML, generative AI, and high-performance computing developers through a simple pay-as-you-go model without setup or maintenance costs. UltraClusters consist of thousands of accelerated EC2 instances co-located in a given AWS Availability Zone, interconnected using Elastic Fabric Adapter (EFA) networking in a petabit-scale nonblocking network. This architecture offers high-performance networking and access to Amazon FSx for Lustre, a fully managed shared storage built on a high-performance parallel file system, enabling rapid processing of massive datasets with sub-millisecond latencies. EC2 UltraClusters provide scale-out capabilities for distributed ML training and tightly coupled HPC workloads, reducing training times. -
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Amazon EC2 Trn2 Instances
Amazon
Amazon EC2 Trn2 instances, powered by AWS Trainium2 chips, are purpose-built for high-performance deep learning training of generative AI models, including large language models and diffusion models. They offer up to 50% cost-to-train savings over comparable Amazon EC2 instances. Trn2 instances support up to 16 Trainium2 accelerators, providing up to 3 petaflops of FP16/BF16 compute power and 512 GB of high-bandwidth memory. To facilitate efficient data and model parallelism, Trn2 instances feature NeuronLink, a high-speed, nonblocking interconnect, and support up to 1600 Gbps of second-generation Elastic Fabric Adapter (EFAv2) network bandwidth. They are deployed in EC2 UltraClusters, enabling scaling up to 30,000 Trainium2 chips interconnected with a nonblocking petabit-scale network, delivering 6 exaflops of compute performance. The AWS Neuron SDK integrates natively with popular machine learning frameworks like PyTorch and TensorFlow. -
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AWS Elastic Fabric Adapter (EFA)
United States
Elastic Fabric Adapter (EFA) is a network interface for Amazon EC2 instances that enables customers to run applications requiring high levels of inter-node communications at scale on AWS. Its custom-built operating system (OS) bypass hardware interface enhances the performance of inter-instance communications, which is critical to scaling these applications. With EFA, High-Performance Computing (HPC) applications using the Message Passing Interface (MPI) and Machine Learning (ML) applications using NVIDIA Collective Communications Library (NCCL) can scale to thousands of CPUs or GPUs. As a result, you get the application performance of on-premises HPC clusters with the on-demand elasticity and flexibility of the AWS cloud. EFA is available as an optional EC2 networking feature that you can enable on any supported EC2 instance at no additional cost. Plus, it works with the most commonly used interfaces, APIs, and libraries for inter-node communications. -
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MLBox
Axel ARONIO DE ROMBLAY
MLBox is a powerful Automated Machine Learning python library. It provides the following features fast reading and distributed data preprocessing/cleaning/formatting, highly robust feature selection and leak detection, accurate hyper-parameter optimization in high-dimensional space, state-of-the art predictive models for classification and regression (Deep Learning, Stacking, LightGBM), and prediction with models interpretation. MLBox main package contains 3 sub-packages: preprocessing, optimization and prediction. Each one of them are respectively aimed at reading and preprocessing data, testing or optimizing a wide range of learners and predicting the target on a test dataset. -
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Ludwig
Uber AI
Ludwig is a low-code framework for building custom AI models like LLMs and other deep neural networks. Build custom models with ease: a declarative YAML configuration file is all you need to train a state-of-the-art LLM on your data. Support for multi-task and multi-modality learning. Comprehensive config validation detects invalid parameter combinations and prevents runtime failures. Optimized for scale and efficiency: automatic batch size selection, distributed training (DDP, DeepSpeed), parameter efficient fine-tuning (PEFT), 4-bit quantization (QLoRA), and larger-than-memory datasets. Expert level control: retain full control of your models down to the activation functions. Support for hyperparameter optimization, explainability, and rich metric visualizations. Modular and extensible: experiment with different model architectures, tasks, features, and modalities with just a few parameter changes in the config. Think building blocks for deep learning. -
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AutoKeras
AutoKeras
An AutoML system based on Keras. It is developed by DATA Lab at Texas A&M University. The goal of AutoKeras is to make machine learning accessible to everyone. AutoKeras supports several tasks with an extremely simple interface. -
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MLlib
Apache Software Foundation
Apache Spark's MLlib is a scalable machine learning library that integrates seamlessly with Spark's APIs, supporting Java, Scala, Python, and R. It offers a comprehensive suite of algorithms and utilities, including classification, regression, clustering, collaborative filtering, and tools for constructing machine learning pipelines. MLlib's high-quality algorithms leverage Spark's iterative computation capabilities, delivering performance up to 100 times faster than traditional MapReduce implementations. It is designed to operate across diverse environments, running on Hadoop, Apache Mesos, Kubernetes, standalone clusters, or in the cloud, and accessing various data sources such as HDFS, HBase, and local files. This flexibility makes MLlib a robust solution for scalable and efficient machine learning tasks within the Apache Spark ecosystem. -
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Volcano Engine
Volcano Engine
Volcengine is ByteDance’s cloud platform delivering a full spectrum of IaaS, PaaS, and AI services under its Volcano Ark ecosystem through global, multi‑region infrastructure. It provides elastic compute instances (CPU, GPU, and TPU), high‑performance block and object storage, virtual networking, and managed databases, all designed for seamless scalability and pay‑as‑you‑go flexibility. Integrated AI capabilities offer natural language processing, computer vision, and speech recognition via prebuilt models or custom training pipelines, while a content delivery network and Engine VE SDK enable adaptive‑bitrate streaming, low‑latency media delivery, and real‑time AR/VR rendering. The platform’s security framework includes end‑to‑end encryption, fine‑grained access control, and automated threat detection, backed by compliance certifications. -
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evoML
TurinTech AI
evoML accelerates the creation of production-quality machine learning models by streamlining and automating the end-to-end data science workflow, transforming raw data into actionable insights in days instead of weeks. It automates crucial steps, automatic data transformation that detects anomalies and handles imbalances, feature engineering via genetic algorithms, parallel model evaluation across thousands of candidates, multi-objective optimization on custom metrics, and GenAI-based synthetic data generation for rapid prototyping under data-privacy constraints. Users fully own and customize generated model code for seamless deployment as APIs, databases, or local libraries, avoiding vendor lock-in and ensuring transparent, auditable workflows. EvoML empowers teams with intuitive visualizations, interactive dashboards, and charts to identify patterns, outliers, and anomalies for use cases such as anomaly detection, time-series forecasting, and fraud prevention.
