Best Machine Learning Software of 2024

Our easy-to-use monitor builder allows you to create customized monitors for your machinelearning models. Get alerts for issues such as concept drift, model performance degradation and bias. Aporia can seamlessly integrate with any ML infrastructure. It doesn't matter if it's a FastAPI server built on top of Kubernetes or an open-source deployment tool such as MLFlow, or a machine-learning platform like AWS Sagemaker. Zoom in on specific data segments to track the model's behavior. Unexpected biases, underperformance, drifting characteristics, and data integrity issues can be identified. You need the right tools to quickly identify the root cause of problems in your ML models. Our investigation toolbox allows you to go deeper than model monitoring and take a deep look at model performance, data segments or distribution.

We offer the highest quality SLA (>95%) even for the most complicated workflows. Our team can assist with everything from the implementation of a solid quality rubric to raising edge case. We are an ethical AI company that has provided economic opportunities to over 52,000 people in underserved and marginalized areas. ML Assisted annotation allowed for efficiency improvements of up to 3-4x per class annotation. We are able to quickly adapt to ramp-ups and focus shifts. Secure work environments are ensured by ISO-certified delivery centers, biometric authentication, 2FA user authentication, and ISO-certified delivery centers. You can quickly re-prioritize tasks, give quality feedback, and monitor production models. All data types are supported. You can do more with less. We combine machine learning with humans to filter data and select images that are relevant to your use cases. Based on your initial guidelines, you will receive sample results. We will work with you to identify and recommend best annotation practices.

Even if you have the best developers, every test makes them slower. 80% of your software testing is pointless. The problem is that you don't know which 20%. We use your data to find the right 20% so you can ship faster. We offer shrink-wrapped predictive testing selection. This machine learning-based method is used by companies like Facebook and can be used by all companies. We support multiple languages, test runners and CI systems. Bring Git. Launchable uses machine-learning to analyze your source code and test failures. It doesn't rely solely on code syntax analysis. Launchable can easily add support for any file-based programming language. This allows us to scale across projects and teams with different languages and tools. We currently support Python, Ruby and Java, JavaScript and Go, as well as C++ and C++. We regularly add new languages to our support.

Our platform allows you to create AI products, train, fine-tune, and deploy models on the cloud. You don't have to worry about scaling, infrastructure, cost management, or other technical issues. Prebuilt, fully customizable modular components make it easy to train, fine tune, and deploy models. The science, not the engineering, should be your focus. Lightning components organize code to run on the cloud and manage its own infrastructure, cloud cost, and other details. 50+ optimizations to lower cloud cost and deliver AI in weeks, not months. Enterprise-grade control combined with consumer-level simplicity allows you to optimize performance, reduce costs, and take on less risk. Get more than a demo. In days, not months, you can launch your next GPT startup, diffusion startup or cloud SaaSML service.

Advanced embedded machine learning applications can be built without a PhD. To create custom datasets, collect sensor, audio, and camera data directly from devices, files or cloud integrations. Automated labeling tools, from object detection to audio segmentation, are available. Our cloud infrastructure allows you to set up and execute reusable scripted tasks that transform large amounts of input data. Integrate custom data sources, CI/CD tool, and deployment pipelines using open APIs. With ready-to-use DSPs and ML algorithms, you can accelerate the development of custom ML pipelines. Every step of the process, hardware decisions are made based on flash/RAM and device performance. Keras APIs allow you to customize DSP feature extraction algorithms. You can also create custom machine learning models. Visualized insights on model performance, memory, and datasets can fine-tune your production model. Find the right balance between DSP configurations and model architecture. All this is budgeted against memory constraints and latency constraints.

Amazon DevOps Guru, powered by machine learning (ML), is a service that makes it easy to improve operational performance and availability of applications. DevOps Guru detects abnormal operating patterns and helps you to identify them before they impact your customers. To identify abnormal application behavior, such as increased latency, error rates or resource limitations, DevOps Guru employs ML models that are based on data collected over years by Amazon.com Operational Excellence and Amazon.com. It helps to detect critical errors that could cause service interruptions. The DevOps Guru automatically alerts you when it detects a critical issue. It provides context and details about the root cause and the possible consequences.

Fiddler is a pioneer in enterprise Model Performance Management. Data Science, MLOps, and LOB teams use Fiddler to monitor, explain, analyze, and improve their models and build trust into AI. The unified environment provides a common language, centralized controls, and actionable insights to operationalize ML/AI with trust. It addresses the unique challenges of building in-house stable and secure MLOps systems at scale. Unlike observability solutions, Fiddler seamlessly integrates deep XAI and analytics to help you grow into advanced capabilities over time and build a framework for responsible AI practices. Fortune 500 organizations use Fiddler across training and production models to accelerate AI time-to-value and scale and increase revenue.

Datoin removes barriers to entry into Machine Learning by using Graphical Interface and No Code approach. It's designed to quickly turn your vision into reality. Re-using blocks over and over is the best way to reduce costs. The Datoin's Block Superstore has a wide range of blocks, including enterprise software connectors and ETL tools, machine-learning libraries, NLP libraries, cloud service integration, SaaS APIs, and machine learning libraries. The best thing about Datoin is that the blocks are constantly being added to the store as we cover more use cases. Pre-built machine learning models make it easy to get started quickly and eliminate the need for training. We have created and built blocks that solve common problems across all industries and functional areas. Edit existing apps to quickly test them if you are unsure of specific functionality or efficacy.

Machine learning applications can be deployed to production in minutes instead of months. Automate the transformation of raw data and generate training data sets. Also, you can serve features for online inference at large scale. Replace bespoke data pipelines by robust pipelines that can be created, orchestrated, and maintained automatically. You can increase your team's efficiency and standardize your machine learning data workflows by sharing features throughout the organization. You can serve features in production at large scale with confidence that the systems will always be available. Tecton adheres to strict security and compliance standards. Tecton is neither a database nor a processing engine. It can be integrated into your existing storage and processing infrastructure and orchestrates it.

Your offline data can be used to make real-time predictions, without the need for custom pipelines. Data consistency is achieved between offline training and online prediction, eliminating train-serve bias. Standardize data engineering workflows within a consistent framework. Feast is used by teams to build their internal ML platforms. Feast doesn't require dedicated infrastructure to be deployed and managed. Feast reuses existing infrastructure and creates new resources as needed. You don't want a managed solution, and you are happy to manage your own implementation. Feast is supported by engineers who can help with its implementation and management. You are looking to build pipelines that convert raw data into features and integrate with another system. You have specific requirements and want to use an open-source solution.

Butler is a platform that allows developers to turn AI into simple APIs. In minutes, you can create, train, and deploy AI Models. No AI experience is required. Butler's user interface is easy to use and allows you to create a complete labeled data set. You can forget about the tedious labeling. Butler automatically selects and trains the right ML model for you. There is no need to spend hours researching which models are the most effective. Butler offers a wide range of customization options that allow you to tailor your model to meet your needs. Don't waste time constructing custom models from scratch or modifying pre-defined models. Any image or document that is not structured can be parsed to extract key data fields and tables. With lightning fast document parsing APIs, you can free your users from the tedious task of manually entering data. Information can be extracted from text, including names, terms, and places. Your product should be able to understand your users as well as you.

To detect and respond to data drift, track model performance for better business outcomes. Arthur's transparency and explainability APIs help to build trust and ensure compliance. Monitor for bias and track model outcomes against custom bias metrics to improve the fairness of your models. {See how each model treats different population groups, proactively identify bias, and use Arthur's proprietary bias mitigation techniques.|Arthur's proprietary techniques for reducing bias can be used to identify bias in models and help you to see how they treat different populations.} {Arthur scales up and down to ingest up to 1MM transactions per second and deliver insights quickly.|Arthur can scale up and down to ingest as many transactions per second as possible and delivers insights quickly.} Only authorized users can perform actions. Each team/department can have their own environments with different access controls. Once data is ingested, it cannot be modified. This prevents manipulation of metrics/insights.

Scribble Data allows organizations to enrich their data and transform it to enable reliable, fast decision-making for business problems. Data-driven decision support for your business. Data-to-decision platform that allows you to generate high-fidelity insights and automate decision-making. Machine learning and advanced analytics can solve your business decision-making problems. Enrich will do the heavy lifting so you can focus on the important tasks and Enrich will take care of the rest. You can use customized data-driven workflows to make data consumption easy and reduce dependence on machine learning and data science engineering teams. With feature engineering capabilities that can prepare large volumes of complex data at scale, you can go from concept to operational product in a matter of weeks.

WEKA 4 delivers unprecedented performance and runs impossible workloads everywhere, without compromise. Artificial Intelligence is opening up new business opportunities. Operationalizing AI requires the ability of processing large amounts of data from multiple sources in a short amount time. WEKA is a complete solution that can be used to accelerate DataOps tasks across the entire data pipeline, whether it is on-prem or the public cloud. Modern methods are required to store and analyze large data sets in life science, whether they are next-generation sequencing, imaging, and microscopy. This will allow for better insights and economics. WEKA reduces the time it takes to get insights. It eliminates performance bottlenecks in the Life Sciences data pipeline and significantly reduces the cost and complexity of managing large amounts of data. WEKA is a modern storage architecture that can manage the most demanding I/O-intensive workloads as well as latency-sensitive applications at exabyte scale.

NimbleBox: Helps teams ship ML features to their customers faster by enabling them to be built as AI companies.

MLReef allows domain experts and data scientists secure collaboration via a hybrid approach of pro-code and no-code development. Distributed workloads lead to a 75% increase in productivity. This allows teams to complete more ML project faster. Domain experts and data scientists can collaborate on the same platform, reducing communication ping-pong to 100%. MLReef works at your location and enables you to ensure 100% reproducibility and continuity. You can rebuild all work at any moment. To create interoperable, versioned, explorable AI modules, you can use git repositories that are already well-known. Your data scientists can create AI modules that you can drag and drop. These modules can be modified by parameters, ported, interoperable and explorable within your organization. Data handling requires a lot of expertise that even a single data scientist may not have. MLReef allows your field experts to assist you with data processing tasks, reducing complexity.

With just one line of code, you can deploy all major ML frameworks like Pytorch and Onnx. Do you not have your own models? Prebuilt models can be deployed to reduce latency and cost. You can fine-tune models for specific tasks to reduce latency and costs while increasing performance. It's easy to do and you don't have to worry about infrastructure. Integrate with the top ML observability platform to be alerted on feature or prediction drift, compare models versions, and resolve issues quickly. To resolve model performance problems, discover the root causes of prediction and feature drift. Find out which features contribute the most to your model's performance.

Reduce false positives by using machine learning (ML), to detect anomalies in business metrics. Grouping outliers that are similar can help you identify the root cause of any anomalies. Summarize root causes, and rank them according to severity. Integrate AWS databases, storage services and third-party SaaS apps seamlessly to monitor metrics and detect anomalies. Automate the sending of customized alerts and taking appropriate actions when anomalies are detected. Automatically detect anomalies in metrics and identify their root causes. Lookout for Metrics uses ML for diagnosing and detecting anomalies in business and operational data. It is difficult to detect unexpected anomalies using traditional methods that are manual and error-prone. Lookout for Metrics uses ML without the need for any artificial intelligence (AI). You can identify unusual variances in subscriptions and conversion rates so you can keep up with sudden changes.

Datuum is an AI-powered data integration tool that offers a unique solution for organizations looking to streamline their data integration process. With our pre-trained AI engine, Datuum simplifies customer data onboarding by allowing for automated integration from various sources without coding. This reduces data preparation time and helps establish resilient connectors, ultimately freeing up time for organizations to focus on generating insights and improving the customer experience. At Datuum, we have over 40 years of experience in data management and operations, and we've incorporated our expertise into the core of our product. Our platform is designed to address the critical challenges faced by data engineers and managers while being accessible and user-friendly for non-technical specialists. By reducing up to 80% of the time typically spent on data-related tasks, Datuum can help organizations optimize their data management processes and achieve more efficient outcomes.

Any data source can be extracted and transformed with Natural Language Connect to your data source - everything from your DB to CRM to your billing system. Increase Productivity by 5-10x. Forget about wasting your time with clunky tools. Natural Language allows you to query complex data in seconds. You can move from DIY tools to AI-powered non-DIY tools. In a matter of seconds, you can create complex dashboards or reports. Layerup AI will do all the heavy lifting. Layerup AI not only provides instant answers to queries that would take 5-40 hours per month, but also acts as your personal data analyst 24/7 and can provide complex dashboards/charts you can embed anywhere.

Watsonx is a new enterprise-ready AI platform that will multiply the impact of AI in your business. The platform consists of three powerful components, including the watsonx.ai Studio for new foundation models, machine learning, and generative AI; the watsonx.data Fit-for-Purpose Store for the flexibility and performance of a warehouse; and the watsonx.governance Toolkit to enable AI workflows built with responsibility, transparency, and explainability. The foundation models allow AI to be fine-tuned to the unique data and domain expertise of an enterprise with a specificity previously impossible. Use all your data, no matter where it is located. Take advantage of a hybrid cloud infrastructure that provides the foundation data for extending AI into your business. Improve data access, implement governance, reduce costs, and put quality models into production quicker.

Artificio, an innovative automation tool created by Artificio Products Inc., is designed to revolutionize the data processing process and eliminate manual data input. This cutting-edge tool uses state-of-the art AI and machine learning models for extracting, segregating, validating, and integrating unstructured data from different sources including texts, images, and PDFs. Artificio enables businesses to unlock digital intelligence's full potential by converting unstructured data into structured data.

Make AI an integral part of your business. With the flexibility and power of a cloud native platform, you can build better, deploy faster and audit easier. Import models and search cataloged model from across your organization. Save time by quickly annotating data with model-in the-loop hinting. Flyte's integration with Chariot allows you to quickly create and launch custom workflows. Understand the full origin of your data, models and workflows. Deploy models wherever you need them. This includes edge and IoT applications. Data scientists are not the only ones who can get valuable insights from their data. With Chariot's low code interface, teams can collaborate effectively.

Deeploy allows you to maintain control over your ML models. You can easily deploy your models to our responsible AI platform without compromising transparency, control and compliance. Transparency, explainability and security of AI models are more important today than ever. You can monitor the performance of your models with confidence and accountability if you use a safe, secure environment. Over the years, our experience has shown us the importance of human interaction with machine learning. Only when machine-learning systems are transparent and accountable can experts and consumers provide feedback, overrule their decisions when necessary, and grow their trust. We created Deeploy for this reason.

AWS Trainium, the second-generation machine-learning (ML) accelerator, is specifically designed by AWS for deep learning training with 100B+ parameter model. Each Amazon Elastic Comput Cloud (EC2) Trn1 example deploys up to sixteen AWS Trainium accelerations to deliver a low-cost, high-performance solution for deep-learning (DL) in the cloud. The use of deep-learning is increasing, but many development teams have fixed budgets that limit the scope and frequency at which they can train to improve their models and apps. Trainium based EC2 Trn1 instance solves this challenge by delivering a faster time to train and offering up to 50% savings on cost-to-train compared to comparable Amazon EC2 instances.