Best Machine Learning Software
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Compare the Top Machine Learning Software as of May 2025
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What is Machine Learning Software?
Machine learning software enables developers and data scientists to build, train, and deploy models that can learn from data and make predictions or decisions without being explicitly programmed. These tools provide frameworks and algorithms for tasks such as classification, regression, clustering, and natural language processing. They often come with features like data preprocessing, model evaluation, and hyperparameter tuning, which help optimize the performance of machine learning models. With the ability to analyze large datasets and uncover patterns, machine learning software is widely used in industries like healthcare, finance, marketing, and autonomous systems. Overall, this software empowers organizations to leverage data for smarter decision-making and automation. Compare and read user reviews of the best Machine Learning software currently available using the table below. This list is updated regularly.
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1
Amazon CodeGuru
Amazon
Amazon CodeGuru is a developer tool powered by machine learning that provides intelligent recommendations for improving code quality and identifying an application’s most expensive lines of code. Integrate Amazon CodeGuru into your existing software development workflow where you will experience built-in code reviews to detect and optimize the expensive lines of code to reduce costs. Amazon CodeGuru Profiler helps developers find an application’s most expensive lines of code along with specific visualizations and recommendations on how to improve code to save money. Amazon CodeGuru Reviewer uses machine learning to identify critical issues and hard-to-find bugs during application development to improve code quality. -
2
Giskard
Giskard
Giskard provides interfaces for AI & Business teams to evaluate and test ML models through automated tests and collaborative feedback from all stakeholders. Giskard speeds up teamwork to validate ML models and gives you peace of mind to eliminate risks of regression, drift, and bias before deploying ML models to production.Starting Price: $0 -
3
Keepsake
Replicate
Keepsake is an open-source Python library designed to provide version control for machine learning experiments and models. It enables users to automatically track code, hyperparameters, training data, model weights, metrics, and Python dependencies, ensuring that all aspects of the machine learning workflow are recorded and reproducible. Keepsake integrates seamlessly with existing workflows by requiring minimal code additions, allowing users to continue training as usual while Keepsake saves code and weights to Amazon S3 or Google Cloud Storage. This facilitates the retrieval of code and weights from any checkpoint, aiding in re-training or model deployment. Keepsake supports various machine learning frameworks, including TensorFlow, PyTorch, scikit-learn, and XGBoost, by saving files and dictionaries in a straightforward manner. It also offers features such as experiment comparison, enabling users to analyze differences in parameters, metrics, and dependencies across experiments.Starting Price: Free -
4
Synerise
Synerise
Synerise is an AI-driven Customer Data & Experience Platform (CDXP). Comprehensive, data-driven solution that centralizes and utilizes customer data to enhance marketing and engagement. Leveraging advanced artificial intelligence, Synerise aggregates data from various sources, creating detailed, real-time customer profiles. Key Strengths of Synerise Synerise excels in several key areas that set it apart from other platforms: - Real-time capabilities. Powered by TerrariumDB, our proprietary database engine designed specifically for behavioural intelligence, real-time computing - AI Engine. The quality of AI algorithms confirmed by successful participation in: Rakuten Data Challenge 2020; Twitter RecSys AI Challenge 2021; KDD Cup 2021; Booking.com AI Challenge 2021 - Time-To-Market. Confirmed by numerous successful implementations across various clients from various industries. -
5
Interplay
Iterate.ai
Interplay Platform is a patented low-code platform with 475 pre-built connectors (enterprise, AI, IoT, Startup Technologies). It's used as middleware and as a rapid app building platform by big companies like Circle K, Ulta Beauty, and many others. As middleware, it operates Pay-by-Plate (frictionless payments at the gas pump) in Europe, Weapons Detection (to predict robberies), AI-based Chat, online personalization tools, low price guarantee tools, computer vision applications such as damage estimation, and much more. It also helps companies to go to market with their digital solutions 10X to 17X faster than in old ways. -
6
Obviously AI
Obviously AI
The entire process of building machine learning algorithms and predicting outcomes, packed in one single click. Not all data is built to be ready for ML, use the Data Dialog to seamlessly shape your dataset without wrangling your files. Share your prediction reports with your team or make them public. Allow anyone to start making predictions on your model. Bring dynamic ML predictions into your own app using our low-code API. Predict willingness to pay, score leads and much more in real-time. Obviously AI puts the world’s most cutting-edge algorithms in your hands, without compromising on performance. Forecast revenue, optimize supply chain, personalize marketing. You can now know what happens next. Add a CSV file OR integrate with your favorite data sources in minutes. Pick your prediction column from a dropdown, we'll auto build the AI. Beautifully visualize predicted results, top drivers and simulate "what-if" scenarios.Starting Price: $75 per month -
7
Noogata
Noogata
Noogata’s AI blocks are built for professionals who need to quickly and easily turn their data into actionable insights to drive business opportunities, growth, and profit. Do more with AI and ML tools built for business users, not data scientists. Turbocharge your business intelligence and analytics tools, including your spreadsheets. Use dozens of pre-built, ready-to-go AI models to turn your data into insight. Connect and customize the blocks to tackle your most pressing business challenges. Connect your data platform and sources, or even Google Sheets or Excel. Create actionable insights, recommendations, and best practices. We know that business users have different data sources, requirements, and objectives so we have built the Noogata AI libraries and blocks to address your specific needs. Uncover the competitive landscape and improve online sales performance. Run analyses in minutes to gain insights into pricing, content strategy, and advertising recommendations. -
8
Baseten
Baseten
A frustratingly slow process requiring development resources or know-how, resulting in most models never seeing the light of day. Ship full-stack apps in minutes. Deploy models instantly, automatically generate API endpoints, and quickly build UI with drag-and-drop components. You shouldn’t need to become a DevOps engineer to get models into production. With Baseten, you can instantly serve, manage, and monitor models with a few lines of Python. Assemble business logic around your model and sync data sources without the infrastructure headaches. Start immediately with sensible defaults, and scale infinitely with fine-grained controls when you need them. Read and write to your existing data stores or with our built-in Postgres database. Create clear, engaging interfaces for business users with headings, callouts, dividers, and more. -
9
Krista
Krista
Krista is a nothing-like-code intelligent automation platform that orchestrates your people, apps, and AI so you can optimize business outcomes. Krista builds and integrates machine learning and apps more simply than you can imagine. Krista is purpose-built to automate business outcomes, not just back-office tasks. Optimizing outcomes requires spanning departments of people & apps, deploying AI/ML for autonomous decision-making, leveraging your existing task automation, and enabling constant change. By digitizing complete processes, Krista delivers organization-wide, bottom-line impact.Krista empowers your people to create and modify automations without programming. Democratizing automation increases business speed and keeps you from waiting in the dreaded IT backlog. Krista dramatically reduces TCO compared to your current automation platform. -
10
Launchable
Launchable
You can have the best developers in the world, but every test is making them slower. 80% of your software tests are pointless. The problem is you don't know which 80%. We find the right 20% using your data so that you can ship faster. We have shrink-wrapped predictive test selection, a machine learning-based approach being used at companies like Facebook so that it can be used by any company. We support multiple languages, test runners, and CI systems. Just bring Git to the table. Launchable uses machine learning to analyze your test failures and source code. It doesn't rely on code syntax analysis. This means it's trivial for Launchable to add support for almost any file-based programming language. It also means we can scale across teams and projects with different languages and tools. Out of the box, we currently support Python, Ruby, Java, JavaScript, Go, C, and C++, and we regularly add support for new languages. -
11
Hex
Hex
Hex brings together the best of notebooks, BI, and docs into a seamless, collaborative UI. Hex is a modern Data Workspace. It makes it easy to connect to data, analyze it in collaborative SQL and Python-powered notebooks, and share work as interactive data apps and stories. Your default landing page in Hex is the Projects page. You can quickly find projects you created, as well as those shared with you and your workspace. The outline provides an easy-to-browse overview of all the cells in a project's Logic View. Every cell in the outline lists the variables it defines, and cells that return a displayed output (chart cells, Input Parameters, markdown cells, etc.) display a preview of that output. You can click any cell in the outline to automatically jump to that position in the logic.Starting Price: $24 per user per month -
12
Emly Labs
Emly Labs
Emly Labs is an AI framework designed to make AI accessible for users at all technical levels through a user-friendly platform. It offers AI project management with tools for guided workflows and automation for faster execution. The platform encourages team collaboration and innovation, provides no-code data preparation, and integrates external data for robust AI models. Emly AutoML automates data processing and model evaluation, reducing human input. It prioritizes transparency, with explainable AI features and robust auditing for compliance. Security measures include data isolation, role-based access, and secure integrations. Additionally, Emly's cost-effective infrastructure allows on-demand resource provisioning and policy management, enhancing experimentation and innovation while reducing costs and risks.Starting Price: $99/month -
13
ML Kit
Google
ML Kit brings Google’s machine learning expertise to mobile developers in a powerful and easy-to-use package. Make your iOS and Android apps more engaging, personalized, and helpful with solutions that are optimized to run on device. ML Kit’s processing happens on-device. This makes it fast and unlocks real-time use cases like processing of camera input. It also works while offline and can be used for processing images and text that need to remain on the device. Take advantage of the machine learning technologies that power Google's own experiences on mobile. We combine best-in-class machine learning models with advanced processing pipelines and offer these through easy-to-use APIs to enable powerful use cases in your apps. Recognizes handwritten text and handdrawn shapes on a digital surface, such as a touch screen. Recognizes 300+ languages, emojis and basic shapes. -
14
AI Squared
AI Squared
Empower data scientists and application developers to collaborate on ML projects. Build, load, optimize and test models and integrations before publishing to end-users for integration into live applications. Reduce data science workload and improve decision-making by storing and sharing ML models across the organization. Publish updates to automatically push changes to models in production. Drive efficiency by instantly providing ML-powered insights within any web-based business application. Our self-service, drag-and-drop browser extension enables analysts and business users to integrate models into any web-based application with zero code. -
15
Amazon SageMaker Debugger
Amazon
Optimize ML models by capturing training metrics in real-time and sending alerts when anomalies are detected. Automatically stop training processes when the desired accuracy is achieved to reduce the time and cost of training ML models. Automatically profile and monitor system resource utilization and send alerts when resource bottlenecks are identified to continuously improve resource utilization. Amazon SageMaker Debugger can reduce troubleshooting during training from days to minutes by automatically detecting and alerting you to remediate common training errors such as gradient values becoming too large or too small. Alerts can be viewed in Amazon SageMaker Studio or configured through Amazon CloudWatch. Additionally, the SageMaker Debugger SDK enables you to automatically detect new classes of model-specific errors such as data sampling, hyperparameter values, and out-of-bound values. -
16
Amazon SageMaker Studio
Amazon
Amazon SageMaker Studio is an integrated development environment (IDE) that provides a single web-based visual interface where you can access purpose-built tools to perform all machine learning (ML) development steps, from preparing data to building, training, and deploying your ML models, improving data science team productivity by up to 10x. You can quickly upload data, create new notebooks, train and tune models, move back and forth between steps to adjust experiments, collaborate seamlessly within your organization, and deploy models to production without leaving SageMaker Studio. Perform all ML development steps, from preparing raw data to deploying and monitoring ML models, with access to the most comprehensive set of tools in a single web-based visual interface. Amazon SageMaker Unified Studio is a comprehensive, AI and data development environment designed to streamline workflows and simplify the process of building and deploying machine learning models. -
17
Amazon SageMaker Pipelines
Amazon
Using Amazon SageMaker Pipelines, you can create ML workflows with an easy-to-use Python SDK, and then visualize and manage your workflow using Amazon SageMaker Studio. You can be more efficient and scale faster by storing and reusing the workflow steps you create in SageMaker Pipelines. You can also get started quickly with built-in templates to build, test, register, and deploy models so you can get started with CI/CD in your ML environment quickly. Many customers have hundreds of workflows, each with a different version of the same model. With the SageMaker Pipelines model registry, you can track these versions in a central repository where it is easy to choose the right model for deployment based on your business requirements. You can use SageMaker Studio to browse and discover models, or you can access them through the SageMaker Python SDK. -
18
UnionML
Union
Creating ML apps should be simple and frictionless. UnionML is an open-source Python framework built on top of Flyte™, unifying the complex ecosystem of ML tools into a single interface. Combine the tools that you love using a simple, standardized API so you can stop writing so much boilerplate and focus on what matters: the data and the models that learn from them. Fit the rich ecosystem of tools and frameworks into a common protocol for machine learning. Using industry-standard machine learning methods, implement endpoints for fetching data, training models, serving predictions (and much more) to write a complete ML stack in one place. Data science, ML engineering, and MLOps practitioners can all gather around UnionML apps as a way of defining a single source of truth about your ML system’s behavior. -
19
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. -
20
Robin.io
Robin.io
ROBIN is the industry’s first hyper-converged Kubernetes platform for big data, databases, and AI/ML. The platform provides a self-service App-store experience for the deployment of any application, anywhere – runs on-premises in your private data center or in public-cloud (AWS, Azure, GCP) environments. Hyper-converged Kubernetes is a software-defined application orchestration framework that combines containerized storage, networking, compute (Kubernetes), and the application management layer into a single system. Our approach extends Kubernetes for data-intensive applications such as Hortonworks, Cloudera, Elastic stack, RDBMS, NoSQL databases, and AI/ML apps. Facilitates simpler and faster roll-out of critical Enterprise IT and LoB initiatives, such as containerization, cloud-migration, cost-consolidation, and productivity improvement. Solves the fundamental challenges of running big data and databases in Kubernetes. -
21
Core ML
Apple
Core ML applies a machine learning algorithm to a set of training data to create a model. You use a model to make predictions based on new input data. Models can accomplish a wide variety of tasks that would be difficult or impractical to write in code. For example, you can train a model to categorize photos or detect specific objects within a photo directly from its pixels. After you create the model, integrate it in your app and deploy it on the user’s device. Your app uses Core ML APIs and user data to make predictions and to train or fine-tune the model. You can build and train a model with the Create ML app bundled with Xcode. Models trained using Create ML are in the Core ML model format and are ready to use in your app. Alternatively, you can use a wide variety of other machine learning libraries and then use Core ML Tools to convert the model into the Core ML format. Once a model is on a user’s device, you can use Core ML to retrain or fine-tune it on-device.
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