Alternatives to Amazon SageMaker Canvas
Compare Amazon SageMaker Canvas alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to Amazon SageMaker Canvas in 2024. Compare features, ratings, user reviews, pricing, and more from Amazon SageMaker Canvas competitors and alternatives in order to make an informed decision for your business.
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TiMi
TIMi
With TIMi, companies can capitalize on their corporate data to develop new ideas and make critical business decisions faster and easier than ever before. The heart of TIMi’s Integrated Platform. TIMi’s ultimate real-time AUTO-ML engine. 3D VR segmentation and visualization. Unlimited self service business Intelligence. TIMi is several orders of magnitude faster than any other solution to do the 2 most important analytical tasks: the handling of datasets (data cleaning, feature engineering, creation of KPIs) and predictive modeling. TIMi is an “ethical solution”: no “lock-in” situation, just excellence. We guarantee you a work in all serenity and without unexpected extra costs. Thanks to an original & unique software infrastructure, TIMi is optimized to offer you the greatest flexibility for the exploration phase and the highest reliability during the production phase. TIMi is the ultimate “playground” that allows your analysts to test the craziest ideas! -
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Amazon SageMaker
Amazon
Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. SageMaker removes the heavy lifting from each step of the machine learning process to make it easier to develop high quality models. Traditional ML development is a complex, expensive, iterative process made even harder because there are no integrated tools for the entire machine learning workflow. You need to stitch together tools and workflows, which is time-consuming and error-prone. SageMaker solves this challenge by providing all of the components used for machine learning in a single toolset so models get to production faster with much less effort and at lower cost. Amazon SageMaker Studio provides a single, web-based visual interface where you can perform all ML development steps. SageMaker Studio gives you complete access, control, and visibility into each step required. -
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Amazon SageMaker Data Wrangler reduces the time it takes to aggregate and prepare data for machine learning (ML) from weeks to minutes. With SageMaker Data Wrangler, you can simplify the process of data preparation and feature engineering, and complete each step of the data preparation workflow (including data selection, cleansing, exploration, visualization, and processing at scale) from a single visual interface. You can use SQL to select the data you want from a wide variety of data sources and import it quickly. Next, you can use the Data Quality and Insights report to automatically verify data quality and detect anomalies, such as duplicate rows and target leakage. SageMaker Data Wrangler contains over 300 built-in data transformations so you can quickly transform data without writing any code. Once you have completed your data preparation workflow, you can scale it to your full datasets using SageMaker data processing jobs; train, tune, and deploy models.
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Amazon SageMaker Autopilot
Amazon
Amazon SageMaker Autopilot eliminates the heavy lifting of building ML models. You simply provide a tabular dataset and select the target column to predict, and SageMaker Autopilot will automatically explore different solutions to find the best model. You then can directly deploy the model to production with just one click or iterate on the recommended solutions to further improve the model quality. You can use Amazon SageMaker Autopilot even when you have missing data. SageMaker Autopilot automatically fills in the missing data, provides statistical insights about columns in your dataset, and automatically extracts information from non-numeric columns, such as date and time information from timestamps. -
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Amazon SageMaker Clarify
Amazon
Amazon SageMaker Clarify provides machine learning (ML) developers with purpose-built tools to gain greater insights into their ML training data and models. SageMaker Clarify detects and measures potential bias using a variety of metrics so that ML developers can address potential bias and explain model predictions. SageMaker Clarify can detect potential bias during data preparation, after model training, and in your deployed model. For instance, you can check for bias related to age in your dataset or in your trained model and receive a detailed report that quantifies different types of potential bias. SageMaker Clarify also includes feature importance scores that help you explain how your model makes predictions and produces explainability reports in bulk or real time through online explainability. You can use these reports to support customer or internal presentations or to identify potential issues with your model. -
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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. Quickly move between steps of the ML lifecycle to fine-tune your models. Replay training experiments, tune model features and other inputs, and compare results, without leaving SageMaker Studio. -
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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. -
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Amazon SageMaker provides all the tools and libraries you need to build ML models, the process of iteratively trying different algorithms and evaluating their accuracy to find the best one for your use case. In Amazon SageMaker you can pick different algorithms, including over 15 that are built-in and optimized for SageMaker, and use over 150 pre-built models from popular model zoos available with a few clicks. SageMaker also offers a variety of model-building tools including Amazon SageMaker Studio Notebooks and RStudio where you can run ML models on a small scale to see results and view reports on their performance so you can come up with high-quality working prototypes. Amazon SageMaker Studio Notebooks help you build ML models faster and collaborate with your team. Amazon SageMaker Studio notebooks provide one-click Jupyter notebooks that you can start working within seconds. Amazon SageMaker also enables one-click sharing of notebooks.
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With Amazon SageMaker Model Monitor, you can select the data you would like to monitor and analyze without the need to write any code. SageMaker Model Monitor lets you select data from a menu of options such as prediction output, and captures metadata such as timestamp, model name, and endpoint so you can analyze model predictions based on the metadata. You can specify the sampling rate of data capture as a percentage of overall traffic in the case of high volume real-time predictions, and the data is stored in your own Amazon S3 bucket. You can also encrypt this data, configure fine-grained security, define data retention policies, and implement access control mechanisms for secure access. Amazon SageMaker Model Monitor offers built-in analysis in the form of statistical rules, to detect drifts in data and model quality. You can also write custom rules and specify thresholds for each rule.
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Amazon SageMaker JumpStart
Amazon
Amazon SageMaker JumpStart is a machine learning (ML) hub that can help you accelerate your ML journey. With SageMaker JumpStart, you can access built-in algorithms with pretrained models from model hubs, pretrained foundation models to help you perform tasks such as article summarization and image generation, and prebuilt solutions to solve common use cases. In addition, you can share ML artifacts, including ML models and notebooks, within your organization to accelerate ML model building and deployment. SageMaker JumpStart provides hundreds of built-in algorithms with pretrained models from model hubs, including TensorFlow Hub, PyTorch Hub, HuggingFace, and MxNet GluonCV. You can also access built-in algorithms using the SageMaker Python SDK. Built-in algorithms cover common ML tasks, such as data classifications (image, text, tabular) and sentiment analysis. -
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Amazon SageMaker Model Training reduces the time and cost to train and tune machine learning (ML) models at scale without the need to manage infrastructure. You can take advantage of the highest-performing ML compute infrastructure currently available, and SageMaker can automatically scale infrastructure up or down, from one to thousands of GPUs. Since you pay only for what you use, you can manage your training costs more effectively. To train deep learning models faster, SageMaker distributed training libraries can automatically split large models and training datasets across AWS GPU instances, or you can use third-party libraries, such as DeepSpeed, Horovod, or Megatron. Efficiently manage system resources with a wide choice of GPUs and CPUs including P4d.24xl instances, which are the fastest training instances currently available in the cloud. Specify the location of data, indicate the type of SageMaker instances, and get started with a single click.
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Amazon SageMaker Edge
Amazon
The SageMaker Edge Agent allows you to capture data and metadata based on triggers that you set so that you can retrain your existing models with real-world data or build new models. Additionally, this data can be used to conduct your own analysis, such as model drift analysis. We offer three options for deployment. GGv2 (~ size 100MB) is a fully integrated AWS IoT deployment mechanism. For those customers with a limited device capacity, we have a smaller built-in deployment mechanism within SageMaker Edge. For customers who have a preferred deployment mechanism, we support third party mechanisms that can be plugged into our user flow. Amazon SageMaker Edge Manager provides a dashboard so you can understand the performance of models running on each device across your fleet. The dashboard helps you visually understand overall fleet health and identify the problematic models through a dashboard in the console. -
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Access, manipulate, analyze and present information in visual formats using a powerful combination of SAS technologies. With SAS Visual Machine Learning, you can broaden your analytics with machine learning and deep learning capabilities that are accessible across your organization for better visualization and reporting. Visualize and discover relevant relationships in your data. Create and share interactive reports and dashboards, and use self-service analytics to quickly assess probable outcomes for smarter, more data-driven decisions. Explore data and build or adjust predictive analytical models with this solution running in SAS® Viya®. Data scientists, statisticians, and analysts can collaborate and iteratively refine models for each segment or group to make decisions based on accurate insights. Solve complex analytical problems with a comprehensive visual interface that handles all tasks in the analytics life cycle.
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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. -
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Amazon SageMaker makes it easy to deploy ML models to make predictions (also known as inference) at the best price-performance for any use case. It provides a broad selection of ML infrastructure and model deployment options to help meet all your ML inference needs. It is a fully managed service and integrates with MLOps tools, so you can scale your model deployment, reduce inference costs, manage models more effectively in production, and reduce operational burden. From low latency (a few milliseconds) and high throughput (hundreds of thousands of requests per second) to long-running inference for use cases such as natural language processing and computer vision, you can use Amazon SageMaker for all your inference needs.
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Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, share, and manage features for machine learning (ML) models. Features are inputs to ML models used during training and inference. For example, in an application that recommends a music playlist, features could include song ratings, listening duration, and listener demographics. Features are used repeatedly by multiple teams and feature quality is critical to ensure a highly accurate model. Also, when features used to train models offline in batch are made available for real-time inference, it’s hard to keep the two feature stores synchronized. SageMaker Feature Store provides a secured and unified store for feature use across the ML lifecycle. Store, share, and manage ML model features for training and inference to promote feature reuse across ML applications. Ingest features from any data source including streaming and batch such as application logs, service logs, clickstreams, sensors, etc.
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Dataiku DSS
Dataiku
Bring data analysts, engineers, and scientists together. Enable self-service analytics and operationalize machine learning. Get results today and build for tomorrow. Dataiku DSS is the collaborative data science software platform for teams of data scientists, data analysts, and engineers to explore, prototype, build, and deliver their own data products more efficiently. Use notebooks (Python, R, Spark, Scala, Hive, etc.) or a customizable drag-and-drop visual interface at any step of the predictive dataflow prototyping process – from wrangling to analysis to modeling. Profile the data visually at every step of the analysis. Interactively explore and chart your data using 25+ built-in charts. Prepare, enrich, blend, and clean data using 80+ built-in functions. Leverage Machine Learning technologies (Scikit-Learn, MLlib, TensorFlow, Keras, etc.) in a visual UI. Build & optimize models in Python or R and integrate any external ML library through code APIs. -
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PI.EXCHANGE
PI.EXCHANGE
Easily connect your data to the engine, either through uploading a file or connecting to a database. Then, start analyzing your data through visualizations, or prepare your data for machine learning modeling with the data wrangling actions with repeatable recipes. Get the most out of your data by building machine learning models, using regression, classification or clustering algorithms - all without any code. Uncover insights into your data, using the feature importance, prediction explanation, and what-if tools. Make predictions and integrate them seamlessly into your existing systems through our connectors, ready to go so you can start taking action.Starting Price: $39 per month -
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Amazon SageMaker Studio Lab
Amazon
Amazon SageMaker Studio Lab is a free machine learning (ML) development environment that provides the compute, storage (up to 15GB), and security, all at no cost, for anyone to learn and experiment with ML. All you need to get started is a valid email address, you don’t need to configure infrastructure or manage identity and access or even sign up for an AWS account. SageMaker Studio Lab accelerates model building through GitHub integration, and it comes preconfigured with the most popular ML tools, frameworks, and libraries to get you started immediately. SageMaker Studio Lab automatically saves your work so you don’t need to restart in between sessions. It’s as easy as closing your laptop and coming back later. Free machine learning development environment that provides the computing, storage, and security to learn and experiment with ML. GitHub integration and preconfigured with the most popular ML tools, frameworks, and libraries so you can get started immediately. -
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Altair Knowledge Studio
Altair
Data scientists and business analysts use Altair to generate actionable insight from their data. Knowledge Studio is a market-leading easy to use machine learning and predictive analytics solution that rapidly visualizes data as it quickly generates explainable results - without requiring a single line of code. A recognized analytics leader, Knowledge Studio brings transparency and automation to machine learning with features such as AutoML and explainable AI without restricting how models are configured and tuned, giving you control over model building. Knowledge Studio is designed to enable collaboration across the business. Data scientists and business analysts can complete complex projects in minutes or hours, not weeks or months. Results are easily understood and explained. The ease of use and automation of steps of the modeling process enable data scientists to efficiently develop more machine learning models faster than coding or using other tools. -
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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 -
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Kraken
Big Squid
Kraken is for everyone from analysts to data scientists. Built to be the easiest-to-use, no-code automated machine learning platform. The Kraken no-code automated machine learning (AutoML) platform simplifies and automates data science tasks like data prep, data cleaning, algorithm selection, model training, and model deployment. Kraken was built with analysts and engineers in mind. If you've done data analysis before, you're ready! Kraken's no-code, easy-to-use interface and integrated SONAR© training make it easy to become a citizen data scientist. Advanced features allow data scientists to work faster and more efficiently. Whether you use Excel or flat files for day-to-day reporting or just ad-hoc analysis and exports, drag-and-drop CSV upload and the Amazon S3 connector in Kraken make it easy to start building models with a few clicks. Data Connectors in Kraken allow you to connect to your favorite data warehouse, business intelligence tools, and cloud storage.Starting Price: $100 per month -
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Supervisely
Supervisely
The leading platform for entire computer vision lifecycle. Iterate from image annotation to accurate neural networks 10x faster. With our best-in-class data labeling tools transform your images / videos / 3d point cloud into high-quality training data. Train your models, track experiments, visualize and continuously improve model predictions, build custom solution within the single environment. Our self-hosted solution guaranties data privacy, powerful customization capabilities, and easy integration into your technology stack. A turnkey solution for Computer Vision: multi-format data annotation & management, quality control at scale and neural networks training in end-to-end platform. Inspired by professional video editing software, created by data scientists for data scientists — the most powerful video labeling tool for machine learning and more. -
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Salford Predictive Modeler (SPM)
Minitab
The Salford Predictive Modeler® (SPM) software suite is a highly accurate and ultra-fast platform for developing predictive, descriptive, and analytical models. The Salford Predictive Modeler® software suite includes the CART®, MARS®, TreeNet®, Random Forests® engines, as well as powerful new automation and modeling capabilities not found elsewhere. The SPM software suite’s data mining technologies span classification, regression, survival analysis, missing value analysis, data binning and clustering/segmentation. SPM algorithms are considered to be essential in sophisticated data science circles. The SPM software suite‘s automation accelerates the process of model building by conducting substantial portions of the model exploration and refinement process for the analyst. We package a complete set of results from alternative modeling strategies for easy review. -
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Sagify
Sagify
Sagify complements AWS Sagemaker by hiding all its low-level details so that you can focus 100% on Machine Learning. Sagemaker is the ML engine and Sagify is the data science-friendly interface. You just need to implement 2 functions, a train and a predict in order to train, tune and deploy hundreds of ML models. Manage your ML models from one place without dealing with low level engineering tasks. No more flaky ML pipelines. Sagify offers 100% reliable training and deployment on AWS. Train, tune and deploy hundreds of ML models by implementing just 2 functions. -
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Amazon SageMaker Ground Truth
Amazon Web Services
Amazon SageMaker allows you to identify raw data such as images, text files, and videos; add informative labels and generate labeled synthetic data to create high-quality training data sets for your machine learning (ML) models. SageMaker offers two options, Amazon SageMaker Ground Truth Plus and Amazon SageMaker Ground Truth, which give you the flexibility to use an expert workforce to create and manage data labeling workflows on your behalf or manage your own data labeling workflows. data labeling. If you want the flexibility to create and manage your own personal and data labeling workflows, you can use SageMaker Ground Truth. SageMaker Ground Truth is a data labeling service that makes data labeling easy and gives you the option of using human annotators via Amazon Mechanical Turk, third-party providers, or your own private staff.Starting Price: $0.08 per month -
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Vidora Cortex
Vidora
Attempting to build Machine Learning Pipelines internally often takes longer and costs more than planned. And worse, Gartner shows that more than 80% of AI Projects will fail. With Cortex, we help teams get up and running with machine learning faster and cheaper than alternatives, all while putting data to use to improve business outcomes. Empower every team with the ability to create their own AI Predictions. No longer will you need to wait to hire a team and build out costly infrastructure. With Cortex you can create predictions from the data you already have, all through an easy to use web interface. Now everyone is a Data Scientist! Cortex automates the process of turning raw data into Machine Learning Pipelines, eliminating the hardest and most time consuming aspects of AI. These predictions stay accurate and up to date by continuously ingesting new data and updating the underlying model automatically – no human intervention needed. -
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Xero.AI
Xero.AI
Building an AI-powered machine learning engineer that can handle all your data science and ML needs. Xero's artificial analyst is the future of data science and ML. Just ask Xara what you want to do with your data and she will do it for you. Explore your data and create custom visuals using natural language to help you better understand your data and generate insights. Clean and transform your data and extract new features in the most seamless way possible. Create, train, and test unlimited customizable machine learning models by simply asking XARA.Starting Price: $30 per month -
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Striveworks Chariot
Striveworks
Make AI a trusted part of your business. Build better, deploy faster, and audit easily with the flexibility of a cloud-native platform and the power to deploy anywhere. Easily import models and search cataloged models from across your organization. Save time by annotating data rapidly with model-in-the-loop hinting. Understand the full provenance of your data, models, workflows, and inferences. Deploy models where you need them, including for edge and IoT use cases. Getting valuable insights from your data is not just for data scientists. With Chariot’s low-code interface, meaningful collaboration can take place across teams. Train models rapidly using your organization's production data. Deploy models with one click and monitor models in production at scale. -
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Vaex
Vaex
At Vaex.io we aim to democratize big data and make it available to anyone, on any machine, at any scale. Cut development time by 80%, your prototype is your solution. Create automatic pipelines for any model. Empower your data scientists. Turn any laptop into a big data powerhouse, no clusters, no engineers. We provide reliable and fast data driven solutions. With our state-of-the-art technology we build and deploy machine learning models faster than anyone on the market. Turn your data scientist into big data engineers. We provide comprehensive training of your employees, enabling you to take full advantage of our technology. Combines memory mapping, a sophisticated expression system, and fast out-of-core algorithms. Efficiently visualize and explore big datasets, and build machine learning models on a single machine. -
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MyDataModels TADA
MyDataModels
Deploy best-in-class predictive analytics models TADA by MyDataModels helps professionals use their Small Data to enhance their business with a light, easy-to-set-up tool. TADA provides a predictive modeling solution leading to fast and usable results. Shift from days to a few hours into building ad hoc effective models with our 40% reduced time automated data preparation. Get outcomes from your data without programming or machine learning skills. Optimize your time with explainable and understandable models made of easy-to-read formulas. Turn your data into insights in a snap on any platform and create effective automated models. TADA removes the complexity of building predictive models by automating the generative machine learning process – data in, model out. Build and run machine learning models on any devices and platforms through our powerful web-based pre-processing features.Starting Price: $5347.46 per year -
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Datatron
Datatron
Datatron offers tools and features built from scratch, specifically to make machine learning in production work for you. Most teams discover that there’s more to just deploying models, which is already a very manual and time-consuming task. Datatron offers single model governance and management platform for all of your ML, AI, and Data Science models in production. We help you automate, optimize, and accelerate your ML models to ensure that they are running smoothly and efficiently in production. Data Scientists use a variety of frameworks to build the best models. We support anything you’d build a model with ( e.g. TensorFlow, H2O, Scikit-Learn, and SAS ). Explore models built and uploaded by your data science team, all from one centralized repository. Create a scalable model deployment in just a few clicks. Deploy models built using any language or framework. Make better decisions based on your model performance. -
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TAZI
TAZI
TAZI is highly focused on business outcome and ROI of AI predictions. TAZI can be used by any business user, whether it is a business intelligence analyst or a C-level executive. TAZI Profiler to immediately understand and gain insights on your ML-Ready data sources. TAZI Business Dashboards and Explanation model to understand and validate the AI models for production. Detect and predict different subsets of your operations for ROI optimization. Empowers you to check data quality and important statistics by automating the manual work usually involved in data discovery and preparation. Makes feature engineering easier with recommendations even for composite features and data transformations. -
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Alibaba Cloud Machine Learning Platform for AI
Alibaba Cloud
An end-to-end platform that provides various machine learning algorithms to meet your data mining and analysis requirements. Machine Learning Platform for AI provides end-to-end machine learning services, including data processing, feature engineering, model training, model prediction, and model evaluation. Machine learning platform for AI combines all of these services to make AI more accessible than ever. Machine Learning Platform for AI provides a visualized web interface allowing you to create experiments by dragging and dropping different components to the canvas. Machine learning modeling is a simple, step-by-step procedure, improving efficiencies and reducing costs when creating an experiment. Machine Learning Platform for AI provides more than one hundred algorithm components, covering such scenarios as regression, classification, clustering, text analysis, finance, and time series.Starting Price: $1.872 per hour -
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Daria
XBrain
Daria’s advanced automated features allow users to quickly and easily build predictive models, significantly cutting back on days and weeks of iterative work associated with the traditional machine learning process. Remove financial and technological barriers to build AI systems from scratch for enterprises. Streamline and expedite workflows by lifting weeks of iterative work through automated machine learning for data experts. Get hands-on experience in machine learning with an intuitive GUI for data science beginners. Daria provides various data transformation functions to conveniently construct multiple feature sets. Daria automatically explores through millions of possible combinations of algorithms, modeling techniques and hyperparameters to select the best predictive model. Predictive models built with Daria can be deployed straight to production with a single line of code via Daria’s RESTful API. -
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ScoopML
ScoopML
Easy-to-Use Build advanced predictive models without math & coding - in just a few clicks. Complete Experience. From cleaning data to building models to making predictions, we provide you all. Trustworthy. Know the 'why' behind AI decisions and drive business with actionable insights. Data Analytics in minutes, without writing code. The total process of building ML algorithms, explaining results, and predicting outcomes in one single click. Machine Learning in 3 Steps. Go from raw data to actionable analytics without writing a single line of code. Upload your data. Ask questions in plain english. Get the best performing model for your data and Share your results. Increase Customer Productivity. We help Companies to leverage no code Machine learning to improve their Customer Experience. -
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MindsDB
MindsDB
Open-Source AI layer for databases. Boost efficiency of your projects by bringing Machine Learning capabilities directly to the data domain. MindsDB provides a simple way to create, train and test ML models and then publish them as virtual AI-Tables into databases. Integrate seamlessly with most of databases on the market. Use SQL queries for all manipulation with ML models. Improve model training speed with GPU without affecting your database performance. Get insights on why the ML model reached its conclusions and what affects prediction confidence. Visual tools that allows you to investigate model performance. SQL and Python queries that return explainability insights in a code. What-if analysis to evaluate confidence based on different inputs. Automate the process of applying machine learning with the state-of-the-art Lightwood AutoML library. Build custom solutions with Machine Learning in your favorite programming language. -
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Deepnote
Deepnote
Deepnote is building the best data science notebook for teams. In the notebook, users can connect their data, explore, and analyze it with real-time collaboration and version control. Users can easily share project links with team collaborators, or with end-users to present polished assets. All of this is done through a powerful, browser-based UI that runs in the cloud. We built Deepnote because data scientists don't work alone. Features: - Sharing notebooks and projects via URL - Inviting others to view, comment and collaborate, with version control - Publishing notebooks with visualizations for presentations - Sharing datasets between projects - Set team permissions to decide who can edit vs view code - Full linux terminal access - Code completion - Automatic python package management - Importing from github - PostgreSQL DB connectionStarting Price: Free -
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Metacoder
Wazoo Mobile Technologies LLC
Metacoder makes processing data faster and easier. Metacoder gives analysts needed flexibility and tools to facilitate data analysis. Data preparation steps such as cleaning are managed reducing the manual inspection time required before you are up and running. Compared to alternatives, is in good company. Metacoder beats similar companies on price and our management is proactively developing based on our customers' valuable feedback. Metacoder is used primarily to assist predictive analytics professionals in their job. We offer interfaces for database integrations, data cleaning, preprocessing, modeling, and display/interpretation of results. We help organizations distribute their work transparently by enabling model sharing, and we make management of the machine learning pipeline easy to make tweaks. Soon we will be including code free solutions for image, audio, video, and biomedical data.Starting Price: $89 per user/month -
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Orange
University of Ljubljana
Open source machine learning and data visualization. Build data analysis workflows visually, with a large, diverse toolbox. Perform simple data analysis with clever data visualization. Explore statistical distributions, box plots and scatter plots, or dive deeper with decision trees, hierarchical clustering, heatmaps, MDS and linear projections. Even your multidimensional data can become sensible in 2D, especially with clever attribute ranking and selections. Interactive data exploration for rapid qualitative analysis with clean visualizations. Graphic user interface allows you to focus on exploratory data analysis instead of coding, while clever defaults make fast prototyping of a data analysis workflow extremely easy. Place widgets on the canvas, connect them, load your datasets and harvest the insight! When teaching data mining, we like to illustrate rather than only explain. And Orange is great at that. -
41
Scale Data Engine
Scale AI
Scale Data Engine helps ML teams build better datasets. Bring together your data, ground truth, and model predictions to effortlessly fix model failures and data quality issues. Optimize your labeling spend by identifying class imbalance, errors, and edge cases in your data with Scale Data Engine. Significantly improve model performance by uncovering and fixing model failures. Find and label high-value data by curating unlabeled data with active learning and edge case mining. Curate the best datasets by collaborating with ML engineers, labelers, and data ops on the same platform. Easily visualize and explore your data to quickly find edge cases that need labeling. Check how well your models are performing and always ship the best one. Easily view your data, metadata, and aggregate statistics with rich overlays, using our powerful UI. Scale Data Engine supports visualization of images, videos, and lidar scenes, overlaid with all associated labels, predictions, and metadata. -
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Amazon HealthLake
Amazon
Extract meaning from unstructured data with integrated Amazon Comprehend Medical for easy search and querying. Make predictions on health data using Amazon Athena queries, Amazon SageMaker ML models, and Amazon QuickSight analytics. Support interoperable standards such as the Fast Healthcare Interoperability Resources (FHIR). Run medical imaging applications in the cloud to increase scale and reduce costs. Amazon HealthLake is a HIPAA-eligible service offering healthcare and life sciences companies a chronological view of individual or patient population health data for query and analytics at scale. Analyze population health trends, predict outcomes, and manage costs with advanced analytics tools and ML models. Identify opportunities to close gaps in care and deliver targeted interventions with a longitudinal view of patient journeys. Apply advanced analytics and ML to newly structured data to optimize appointment scheduling and reduce unnecessary procedures. -
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PolyAnalyst
Megaputer Intelligence
PolyAnalyst is a data analysis software used by large organizations across several industries (Insurance, Manufacturing, Finance, etc.). Some of its most notable features and capabilities include its use of a visual composer for complex data analysis modeling rather than coding/programming. It couples structured and poly-structured forms of data for unified analysis (ie multiple-choice questions and open-ended responses) and it can process text data in over 16+ different languages. PolyAnalyst has many features that meet comprehensive data analysis needs, such as loading data, cleansing and preparing data for analysis, deploying machine learning and supervised analysis techniques, and building reports that non-analysts can use to uncover insights. -
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Materials Zone
Materials Zone
From materials data to better products, faster! Accelerates R&D, scale-up, and optimizes manufacturing QC and supply chain decisions. Discover new materials, use ML guidance to forecast outcomes, and achieve faster and improved results. Build a model on your way to production. Test the model's limits behind your products to design cost-efficient and robust production lines. Use models to predict future failures based on supplied materials informatics and production line parameters. The Materials Zone platform aggregates data from independent entities, materials providers, factories, or manufacturing facilities, communicating between them through a secured platform. By using machine learning (ML) algorithms on your experimental data, you can discover new materials with desired properties, generate ‘recipes’ for materials synthesis, build tools to analyze unique measurements automatically, and retrieve insights. -
45
Analance
Ducen
Combining Data Science, Business Intelligence, and Data Management Capabilities in One Integrated, Self-Serve Platform. Analance is a robust, salable end-to-end platform that combines Data Science, Advanced Analytics, Business Intelligence, and Data Management into one integrated self-serve platform. It is built to deliver core analytical processing power to ensure data insights are accessible to everyone, performance remains consistent as the system grows, and business objectives are continuously met within a single platform. Analance is focused on turning quality data into accurate predictions allowing both data scientists and citizen data scientists with point and click pre-built algorithms and an environment for custom coding. Company – Overview Ducen IT helps Business and IT users of Fortune 1000 companies with advanced analytics, business intelligence and data management through its unique end-to-end data science platform called Analance. -
46
BryteFlow
BryteFlow
BryteFlow builds the most efficient automated environments for analytics ever. It converts Amazon S3 into an awesome analytics platform by leveraging the AWS ecosystem intelligently to deliver data at lightning speeds. It complements AWS Lake Formation and automates the Modern Data Architecture providing performance and productivity. You can completely automate data ingestion with BryteFlow Ingest’s simple point-and-click interface while BryteFlow XL Ingest is great for the initial full ingest for very large datasets. No coding is needed! With BryteFlow Blend you can merge data from varied sources like Oracle, SQL Server, Salesforce and SAP etc. and transform it to make it ready for Analytics and Machine Learning. BryteFlow TruData reconciles the data at the destination with the source continually or at a frequency you select. If data is missing or incomplete you get an alert so you can fix the issue easily. -
47
Weka
University of Waikato
Weka is a collection of machine learning algorithms for data mining tasks. It contains tools for data preparation, classification, regression, clustering, association rules mining, and visualization. Found only on the islands of New Zealand, the Weka is a flightless bird with an inquisitive nature. The name is pronounced like this, and the bird sounds like this. Weka is open source software issued under the GNU General Public License. We have put together several free online courses that teach machine learning and data mining using Weka. The videos for the courses are available on Youtube. An exciting and potentially far-reaching development in computer science is the invention and application of methods of machine learning (ML). These enable a computer program to automatically analyze a large body of data and decide what information is most relevant. This crystallized information can then be used to automatically make predictions or to help people make decisions faster. -
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Altair Knowledge Works
Altair
It is undeniable that data and analytics are key drivers behind transformative business initiatives. More people across the enterprise are accessing data to answer complex questions. The demand for easy-to-use, low-code yet flexible data transformation and machine learning tools has never been greater. The use of multiple tools leads to inefficient data analytic processes, higher costs, and slows down decision making. Aging solutions with overlapping features threaten current data science projects as proprietary functions in closed vendor solutions become obsolete. Combining decades of experience in data preparation, machine learning and visualization with one unified interface Knowledge Works scales as data sizes grow, new open source features and functionalities are developed, and user profiles become more sophisticated. Data scientists and business analysts can efficiently operationalize data analytics applications using its low-code, cloud ready interface. -
49
Modelbit
Modelbit
Don't change your day-to-day, works with Jupyter Notebooks and any other Python environment. Simply call modelbi.deploy to deploy your model, and let Modelbit carry it — and all its dependencies — to production. ML models deployed with Modelbit can be called directly from your warehouse as easily as calling a SQL function. They can also be called as a REST endpoint directly from your product. Modelbit is backed by your git repo. GitHub, GitLab, or home grown. Code review. CI/CD pipelines. PRs and merge requests. Bring your whole git workflow to your Python ML models. Modelbit integrates seamlessly with Hex, DeepNote, Noteable and more. Take your model straight from your favorite cloud notebook into production. Sick of VPC configurations and IAM roles? Seamlessly redeploy your SageMaker models to Modelbit. Immediately reap the benefits of Modelbit's platform with the models you've already built. -
50
StreamFlux
Fractal
Data is crucial when it comes to building, streamlining and growing your business. However, getting the full value out of data can be a challenge, many organizations are faced with poor access to data, incompatible tools, spiraling costs and slow results. Simply put, leaders who can turn raw data into real results will thrive in today’s landscape. The key to this is empowering everyone across your business to be able to analyze, build and collaborate on end-to-end AI and machine learning solutions in one place, fast. Streamflux is a one-stop shop to meet your data analytics and AI challenges. Our self-serve platform allows you the freedom to build end-to-end data solutions, uses models to answer complex questions and assesses user behaviors. Whether you’re predicting customer churn and future revenue, or generating recommendations, you can go from raw data to genuine business impact in days, not months.
