Best Machine Learning Software of 2024

We make it easy for you to add superhuman computer vision into your applications, devices, and processes to give yourself an unassailable competitive edge.

Arize's machine-learning observability platform automatically detects and diagnoses problems and improves models. Machine learning systems are essential for businesses and customers, but often fail to perform in real life. Arize is an end to-end platform for observing and solving issues in your AI models. Seamlessly enable observation for any model, on any platform, in any environment. SDKs that are lightweight for sending production, validation, or training data. You can link real-time ground truth with predictions, or delay. You can gain confidence in your models' performance once they are deployed. Identify and prevent any performance or prediction drift issues, as well as quality issues, before they become serious. Even the most complex models can be reduced in time to resolution (MTTR). Flexible, easy-to use tools for root cause analysis are available.

Datatron provides tools and features that are built from scratch to help you make machine learning in production a reality. Many teams realize that there is more to deploying models than just the manual task. Datatron provides a single platform that manages all your ML, AI and Data Science models in production. We can help you automate, optimize and accelerate your ML model production to ensure they run smoothly and efficiently. Data Scientists can use a variety frameworks to create the best models. We support any framework you use to build a model (e.g. TensorFlow and H2O, Scikit-Learn and SAS are supported. Explore models that were created and uploaded by your data scientists, all from one central repository. In just a few clicks, you can create scalable model deployments. You can deploy models using any language or framework. Your model performance will help you make better decisions.

We are the only company that can autonomously tune applications across multiple applications. Opsani rightsizes an application automatically so that your cloud application runs faster and is more efficient. Opsani COaaS optimizes cloud workload performance using the latest AI and Machine Learning. It continuously reconfigures and tunes with every code release and load profile change. This is done while seamlessly integrating with one app or across your service delivery platform, while also scaling autonomously across thousands of services. Opsani makes it possible to solve all three problems autonomously and without compromise. Opsani's AI algorithms can help you reduce costs by up to 71% Opsani optimization continually evaluates trillions upon trillions of configuration possibilities and pinpoints the most effective combinations of resources, parameter settings, and other parameters.

IBM Watson Machine Learning, a full-service IBM Cloud offering, makes it easy for data scientists and developers to work together to integrate predictive capabilities into their applications. The Machine Learning service provides a set REST APIs that can be called from any programming language. This allows you to create applications that make better decisions, solve difficult problems, and improve user outcomes. Machine learning models management (continuous-learning system) and deployment (online batch, streaming, or online) are available. You can choose from any of the widely supported machine-learning frameworks: TensorFlow and Keras, Caffe or PyTorch. Spark MLlib, scikit Learn, xgboost, SPSS, Spark MLlib, Keras, Caffe and Keras. To manage your artifacts, you can use the Python client and command-line interface. The Watson Machine Learning REST API allows you to extend your application with artificial intelligence.

A platform that offers a variety of machine learning algorithms to meet data mining and analysis needs. Machine Learning Platform for AI offers end-to-end machine-learning services, including data processing and feature engineering, model prediction, model training, model evaluation, and model prediction. Machine learning platform for AI integrates all these services to make AI easier than ever. Machine Learning Platform for AI offers a visual web interface that allows you to create experiments by dragging components onto the canvas. Machine learning modeling is a step-by-step process that improves efficiency and reduces costs when creating experiments. Machine Learning Platform for AI offers more than 100 algorithm components. These include text analysis, finance, classification, clustering and time series.

Machine learning models can be deployed at scale with greater accuracy. With more models in production, R&D can be turned into ROI. Seldon reduces time to value so models can get to work quicker. Scale with confidence and minimize risks through transparent model performance and interpretable results. Seldon Deploy cuts down on time to production by providing production-grade inference servers that are optimized for the popular ML framework and custom language wrappers to suit your use cases. Seldon Core Enterprise offers enterprise-level support and access to trusted, global-tested MLOps software. Seldon Core Enterprise is designed for organizations that require: - Coverage for any number of ML models, plus unlimited users Additional assurances for models involved in staging and production - You can be confident that their ML model deployments will be supported and protected.

Kubernetes is a highly scalable platform for model inference that uses standards-based models. Trusted AI. KServe, a Kubernetes standard model inference platform, is designed for highly scalable applications. Provides a standardized, performant inference protocol that works across all ML frameworks. Modern serverless inference workloads supported by autoscaling, including a scale up to zero on GPU. High scalability, density packing, intelligent routing with ModelMesh. Production ML serving is simple and pluggable. Pre/post-processing, monitoring and explainability are all possible. Advanced deployments using the canary rollout, experiments and ensembles as well as transformers. ModelMesh was designed for high-scale, high density, and often-changing model use cases. ModelMesh intelligently loads, unloads and transfers AI models to and fro memory. This allows for a smart trade-off between user responsiveness and computational footprint.

NVIDIA Triton™, an inference server, delivers fast and scalable AI production-ready. Open-source inference server software, Triton inference servers streamlines AI inference. It allows teams to deploy trained AI models from any framework (TensorFlow or NVIDIA TensorRT®, PyTorch or ONNX, XGBoost or Python, custom, and more on any GPU or CPU-based infrastructure (cloud or data center, edge, or edge). Triton supports concurrent models on GPUs to maximize throughput. It also supports x86 CPU-based inferencing and ARM CPUs. Triton is a tool that developers can use to deliver high-performance inference. It integrates with Kubernetes to orchestrate and scale, exports Prometheus metrics and supports live model updates. Triton helps standardize model deployment in production.

Your ML model can be served in minutes in any cloud. Unified model packaging format that allows online and offline delivery on any platform. Our micro-batching technology allows for 100x more throughput than a regular flask-based server model server. High-quality prediction services that can speak the DevOps language, and seamlessly integrate with common infrastructure tools. Unified format for deployment. High-performance model serving. Best practices in DevOps are incorporated. The service uses the TensorFlow framework and the BERT model to predict the sentiment of movie reviews. DevOps-free BentoML workflow. This includes deployment automation, prediction service registry, and endpoint monitoring. All this is done automatically for your team. This is a solid foundation for serious ML workloads in production. Keep your team's models, deployments and changes visible. You can also control access via SSO and RBAC, client authentication and auditing logs.

Our Natural Language Processing (NLP) APIs offer best-in-class accuracy at a reasonable rate and cover all aspects NLP under one roof. You can save weeks of time creating and training language models. Our best-in-class APIs will help you get your app developed. We provide the foundations for creating your own apps, such as chatbots, sentiment analysis, and more. Text classification across multiple domains and in more than 100 languages. Perform sentiment analysis. Your business grows when we grow. We have simplified pricing so that you can easily scale your business as it grows. This is ideal for developers who create apps or build proof of concept. Go to the Dashboard to get your API Key. This key should be placed in the header of any API calls. To get started with coding, you can use our SDK in the language that you prefer. You can also refer to the 18 auto-generated code blocks.

It is a frustratingly slow process that requires development resources and know-how. Most models will never see the light of day. In minutes, you can ship full-stack applications. You can deploy models immediately, automatically generate API endpoints and quickly create UI using drag-and-drop components. To put models into production, you don't have to be a DevOps Engineer. Baseten allows you to instantly manage, monitor, and serve models using just a few lines Python. You can build business logic around your model, and sync data sources without any infrastructure headaches. Start with sensible defaults and scale infinitely with fine-grained controls as needed. You can read and write to your existing data sources or our built-in Postgres databases. Use headings, callouts and dividers to create engaging interfaces for business users.

Krista is an intelligent automation platform that does not require any programming knowledge. It orchestrates your people and apps to optimize business results. Krista integrates machine learning and other apps faster than you could imagine. Krista was designed to automate business outcomes and not back-office tasks. Optimizing outcomes requires that you span departments and apps, deploy AI/ML for autonomous decision making, leverage your existing task automation, and enable constant change. Krista digitizes entire processes to deliver organization-wide, bottom line impact. Automating your business faster and reducing the IT backlog is a good idea. Krista significantly reduces TCO when compared to your existing automation platform.

Data scientists need innovative and efficient turn-key solutions. For quantum engineers, powerful circuit building blocks. Turn-key implementations of algorithms for data scientists, financial analysts, engineers. Explore problems in binary optimization and machine learning on simulators and real hardware. You don't need to have any prior experience in quantum computing. To load classical data into quantum states, use NISQ data loader devices. Circuit building blocks are available for linear algebra with distance estimation or matrix multiplication circuits. You can create your own algorithms using our circuit building blocks. You can get a significant performance boost with D-Wave hardware. Also, the latest gate-based improvements will help you. Quantum data loaders and algorithms offer guaranteed speed-ups in clustering, classification, regression.

Mona is a flexible and intelligent monitoring platform for AI / ML. Data science teams leverage Mona’s powerful analytical engine to gain granular insights about the behavior of their data and models, and detect issues within specific segments of data, in order to reduce business risk and pinpoint areas that need improvements. Mona enables tracking custom metrics for any AI use case within any industry and easily integrates with existing tech stacks. In 2018, we ventured on a mission to empower data teams to make AI more impactful and reliable, and to raise the collective confidence of business and technology leaders in their ability to make the most out of AI. We have built the leading intelligent monitoring platform to provide data and AI teams with continuous insights to help them reduce risks, optimize their operations, and ultimately build more valuable AI systems. Enterprises in a variety of industries leverage Mona for NLP/NLU, speech, computer vision, and machine learning use cases. Mona was founded by experienced product leaders from Google and McKinsey&Co, is backed by top VCs, and is HQ in Atlanta, Georgia. In 2021, Mona was recognized by Gartner as a Cool Vendor in AI Operationalization and Engineering.

Machine learning has led to business and research breakthroughs in everything from network security to medical diagnosis. To make similar breakthroughs possible, we created the Tensor Processing unit (TPU). Cloud TPU is a custom-designed machine learning ASIC which powers Google products such as Translate, Photos and Search, Assistant, Assistant, and Gmail. Here are some ways you can use the TPU and machine-learning to accelerate your company's success, especially when it comes to scale. Cloud TPU is designed for cutting-edge machine learning models and AI services on Google Cloud. Its custom high-speed network provides over 100 petaflops performance in a single pod. This is enough computational power to transform any business or create the next breakthrough in research. It is similar to compiling code to train machine learning models. You need to update frequently and you want to do it as efficiently as possible. As apps are built, deployed, and improved, ML models must be trained repeatedly.

One development environment for all data science workflows. Natively analyze your data without the need to switch between services. Data to training at scale Models can be built and trained 5X faster than traditional notebooks. Scale up model development using simple connectivity to Vertex AI Services. Access to data is simplified and machine learning is made easier with BigQuery Dataproc, Spark and Vertex AI integration. Vertex AI training allows you to experiment and prototype at scale. Vertex AI Workbench allows you to manage your training and deployment workflows for Vertex AI all from one location. Fully managed, scalable and enterprise-ready, Jupyter-based, fully managed, scalable, and managed compute infrastructure with security controls. Easy connections to Google Cloud's Big Data Solutions allow you to explore data and train ML models.

Accelerate compute jobs such as machine learning and HPC. There are many GPUs available to suit different price points and performance levels. Flexible pricing and machine customizations are available to optimize your workload. High-performance GPUs available on Google Cloud for machine intelligence, scientific computing, 3D visualization, and machine learning. NVIDIA K80 and P100 GPUs, T4, V100 and A100 GPUs offer a variety of compute options to meet your workload's cost and performance requirements. You can optimize the processor, memory and high-performance disk for your specific workload by using up to 8 GPUs per instance. All this with per-second billing so that you only pay for what you use. You can run GPU workloads on Google Cloud Platform, which offers industry-leading storage, networking and data analytics technologies. Compute Engine offers GPUs that can be added to virtual machine instances. Learn more about GPUs and the types of hardware available.

OpenCV (Open Source Computer Vision Library), is an open-source machine learning and computer vision software library. OpenCV was created to provide a common infrastructure to support computer vision applications and accelerate machine perception in commercial products. OpenCV is a BSD-licensed product that makes it easy to modify and use the code by businesses. The library contains more than 2500 optimized algorithms. This includes a comprehensive set both of classic and modern computer vision and machine-learning algorithms. These algorithms can be used for recognizing faces, identifying objects, tracking camera movements, classifying human actions in videos and producing 3D point clouds from stereo-cameras. They can also be used to stitch images together to create a high resolution image of the entire scene, find similar images from a database, remove red eyes from images taken with flash, recognize scenery, and follow eye movements.

You can now build what took years. Simple, customizable, scalable, secure, ML monitoring. Everything you need to deploy and maintain ML in production. Superwise integrates with any ML stack, and can connect to any number of communication tools. Want to go further? Superwise is API-first. All of our APIs allow you to access everything, and we mean everything. All this from the comfort of your cloud. You have complete control over ML monitoring. You can set up metrics and policies using our SDK and APIs. Or, you can simply choose a template to monitor and adjust the sensitivity, conditions and alert channels. Get Superwise or contact us for more information. Superwise's ML monitoring policy templates allow you to quickly create alerts. You can choose from dozens pre-built monitors, ranging from data drift and equal opportunity, or you can customize policies to include your domain expertise.

Machine learning can do amazing things, including understanding the world, driving cars, writing code, and making art. It's still very difficult to use. Research is usually published in a PDF format. There are also bits of code on GitHub and weights (if you're fortunate!) on Google Drive. It's difficult to apply that work to a real-world problem unless you're an expert. Machine learning is now accessible to everyone. Machine learning models should be shared by people who create them. People who want to use machine-learning should not need a PhD to share their machine learning models. Great power comes with great responsibility. We believe that this technology can be made safer and more understandable by using better tools and safeguards.

Towhee can automatically optimize your pipeline for production-ready environments by using our Python API. Towhee supports data conversion for almost 20 unstructured data types, including images, text, and 3D molecular structure. Our services include pipeline optimizations that cover everything from data decoding/encoding to model inference. This makes your pipeline execution 10x more efficient. Towhee integrates with your favorite libraries and tools, making it easy to develop. Towhee also includes a Python method-chaining API that allows you to describe custom data processing pipelines. Schemas are also supported, making it as simple as handling tabular data to process unstructured data.

Alpa aims automate large-scale distributed training. Alpa was originally developed by people at UC Berkeley's Sky Lab. Alpa's advanced techniques were described in a paper published by OSDI'2022. Google is adding new members to the Alpa community. A language model is a probabilistic distribution of probability over a sequence of words. It uses all the words it has seen to predict the next word. It is useful in a variety AI applications, including the auto-completion of your email or chatbot service. You can find more information on the language model Wikipedia page. GPT-3 is a large language model with 175 billion parameters that uses deep learning to produce text that looks human-like. GPT-3 was described by many researchers and news articles as "one the most important and interesting AI systems ever created." GPT-3 is being used as a backbone for the latest NLP research.

Apache PredictionIO®, an open-source machine-learning server, is built on top a state of the art open-source stack that allows data scientists and developers to create predictive engines for any type of machine learning task. It allows you to quickly create and deploy an engine as web service on production using customizable templates. Once deployed as a web-service, it can respond to dynamic queries immediately, evaluate and tune multiple engine variations systematically, unify data from multiple platforms either in batch or real-time for comprehensive predictive analysis. Machine learning modeling can be speeded up with pre-built evaluation methods and systematic processes. These measures also support machine learning and data processing libraries like Spark MLLib or OpenNLP. You can create your own machine learning models and integrate them seamlessly into your engine. Data infrastructure management simplified. Apache PredictionIO®, a complete machine learning stack, can be installed together with Apache Spark, MLlib and HBase.

Bittensor, an open-source protocol, powers a blockchain-based decentralized machine-learning network. Machine learning models are trained collaboratively, and rewarded by TAO based on the informational value that they provide to the collective. TAO also allows external access to the network, allowing users extract information while tuning its activities according to their needs. Our vision is to create an artificial intelligence market, a transparent, open and trustless environment where consumers and producers can interact. A novel, optimized approach to the development and distribution artificial intelligence technology that leverages the capabilities of a distributed ledger. Its facilitation of open ownership and access, decentralized governance and the ability of global computing power and innovation to be harnessed within an incentive framework.