Alternatives to Rasgo

Open-Source AI layer for databases. Machine Learning capabilities can be integrated directly into your data domain to increase efficiency and productivity. MindsDB makes it easy to create, train, and then test ML models. Then publish them as virtual AI tables into databases. Integrate seamlessly with all major databases. SQL queries can be used to manipulate ML models. You can increase model training speed using GPU without affecting the performance of your database. Learn how the ML model arrived at its conclusions and what factors affect prediction confidence. Visual tools that allow you to analyze model performance. SQL and Python queries that return explanation insights in a single code. You can use What-if analysis to determine confidence based upon different inputs. Automate the process for applying machine learning using the state-of the-art Lightwood AutoML library. Machine Learning can be used to create custom solutions in your preferred programming language.

The Xilinx AI development platform for AI Inference on Xilinx hardware platforms consists optimized IP, tools and libraries, models, examples, and models. It was designed to be efficient and easy-to-use, allowing AI acceleration on Xilinx FPGA or ACAP. Supports mainstream frameworks as well as the most recent models that can perform diverse deep learning tasks. A comprehensive collection of pre-optimized models is available for deployment on Xilinx devices. Find the closest model to your application and begin retraining! This powerful open-source quantizer supports model calibration, quantization, and fine tuning. The AI profiler allows you to analyze layers in order to identify bottlenecks. The AI library provides open-source high-level Python and C++ APIs that allow maximum portability from the edge to the cloud. You can customize the IP cores to meet your specific needs for many different applications.

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.

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.

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.

Keepsake, an open-source Python tool, is designed to provide versioning for machine learning models and experiments. It allows users to track code, hyperparameters and training data. It also tracks metrics and Python dependencies. Keepsake integrates seamlessly into existing workflows. It requires minimal code additions and allows users to continue training while Keepsake stores code and weights in Amazon S3 or Google Cloud Storage. This allows for the retrieval and deployment of code or weights at any checkpoint. Keepsake is compatible with a variety of machine learning frameworks including TensorFlow and PyTorch. It also supports scikit-learn and XGBoost. It also has features like experiment comparison that allow users to compare parameters, metrics and dependencies between experiments.

Amazon SageMaker Clarify is a machine learning (ML), development tool that provides purpose-built tools to help them gain more insight into their ML training data. SageMaker Clarify measures and detects potential bias using a variety metrics so that ML developers can address bias and explain model predictions. SageMaker Clarify detects potential bias in data preparation, model training, and in your model. You can, for example, check for bias due to age in your data or in your model. A detailed report will quantify the different types of possible bias. SageMaker Clarify also offers feature importance scores that allow you to explain how SageMaker Clarify makes predictions and generates explainability reports in bulk. These reports can be used to support internal or customer presentations and to identify potential problems with your model.

Robust Intelligence Platform seamlessly integrates into your ML lifecycle to eliminate any model failures. The platform detects weaknesses in your model, detects statistical data issues such as drift, and prevents data from being inserted into your AI system. A single test is the heart of our test-based approach. Each test measures the model's resistance to a particular type of production model failure. Stress Testing runs hundreds upon hundreds of these tests in order to assess model production readiness. These tests are used to automatically configure an AI Firewall to protect the model from the specific types of failures to which it is most vulnerable. Continuous Testing also runs these tests during production. Continuous Testing provides an automated root cause analysis that identifies the root cause of any test failure. ML Integrity can be ensured by using all three elements of Robust Intelligence.

Observability allows you to detect data issues and ML problems faster, to deliver continuous improvements and to avoid costly incidents. Start with reliable data. Monitor data in motion for quality issues. Pinpoint data and models drift. Identify the training-serving skew, and proactively retrain. Monitor key performance metrics continuously to detect model accuracy degradation. Identify and prevent data leakage in generative AI applications. Protect your generative AI apps from malicious actions. Improve AI applications by using user feedback, monitoring and cross-team collaboration. Integrate in just minutes with agents that analyze raw data, without moving or replicating it. This ensures privacy and security. Use the proprietary privacy-preserving technology to integrate the WhyLabs SaaS Platform with any use case. Security approved by healthcare and banks.

You can deploy Mystic in your own Azure/AWS/GCP accounts or in our shared GPU cluster. All Mystic features can be accessed directly from your cloud. In just a few steps, you can get the most cost-effective way to run ML inference. Our shared cluster of graphics cards is used by hundreds of users at once. Low cost, but performance may vary depending on GPU availability in real time. We solve the infrastructure problem. A Kubernetes platform fully managed that runs on your own cloud. Open-source Python API and library to simplify your AI workflow. You get a platform that is high-performance to serve your AI models. Mystic will automatically scale GPUs up or down based on the number API calls that your models receive. You can easily view and edit your infrastructure using the Mystic dashboard, APIs, and CLI.

Create statistically optimized representations for your data to train accurate ML models with limited, sparse and high-dimensional data. Dark Matter accelerates training and improves model performance by learning how to extract complex relationships from your existing data. This is done without extensive feature engineering and resource-intensive deep-learning. Data scientists can spend less time on data to solve hard problems. Dark Matter significantly improved the model precision and f1 score in predicting customer convertion in the online retail sector. When trained on an embedded optimization learned from sparse and high-dimensional data, model performance metrics improved across board. The banking industry improved its predictions of customer churn by training XGBoost with a better representation. No matter what model or domain you are in, you can improve your pipeline.

MyDataModels' best-in-class predictive analytics model TADA allows professionals to use their Small Data to improve their business. It is a simple-to-use tool that is easy to set up. TADA is a predictive modeling tool that delivers fast and useful results. With our 40% faster automated data preparation, you can transform your time from days to just a few hours to create ad-hoc effective models. You can get results from your data without any programming or machine learning skills. Make your time more efficient with easy-to-understand models that are clear and understandable. You can quickly turn your data into insights on any platform and create automated models that are effective. TADA automates the process of creating predictive models. Our web-based pre-processing capabilities allow you to create and run machine learning models from any device or platform.

TAZI is focused on the business outcome and ROI of AI predictions. TAZI is available to all business users, regardless of whether they are business intelligence analysts or C-level executives. TAZI Profiler allows you to instantly understand and gain insight into your ML-Ready data source. TAZI Business Dashboards and Explanation Model to validate and understand the AI models for production. For ROI optimization, identify and predict subsets of operations. Automated data discovery and preparation automates data quality checks and important statistics. Allows you to make feature engineering simpler with recommendations, even for composite features or data transformations.

Data analysts, engineers, scientists, and other scientists can be brought together. Automate self-service analytics and machine learning operations. Get results today, build for tomorrow. Dataiku DSS is a collaborative data science platform that allows data scientists, engineers, and data analysts to create, prototype, build, then deliver their data products more efficiently. Use notebooks (Python, R, Spark, Scala, Hive, etc.) You can also use a drag-and-drop visual interface or Python, R, Spark, Scala, Hive notebooks at every step of the predictive dataflow prototyping procedure - from wrangling to analysis and modeling. Visually profile the data at each stage of the analysis. Interactively explore your data and chart it using 25+ built in charts. Use 80+ built-in functions to prepare, enrich, blend, clean, and clean your data. Make use of Machine Learning technologies such as Scikit-Learn (MLlib), TensorFlow and Keras. In a visual UI. You can build and optimize models in Python or R, and integrate any external library of ML through code APIs.

It supports high-performance learning on AWS Trainium based Amazon Elastic Compute Cloud Trn1 instances. It supports low-latency and high-performance inference for model deployment on AWS Inferentia based Amazon EC2 Inf1 and AWS Inferentia2-based Amazon EC2 Inf2 instance. Neuron allows you to use popular frameworks such as TensorFlow or PyTorch and train and deploy machine-learning (ML) models using Amazon EC2 Trn1, inf1, and inf2 instances without requiring vendor-specific solutions. AWS Neuron SDK is natively integrated into PyTorch and TensorFlow, and supports Inferentia, Trainium, and other accelerators. This integration allows you to continue using your existing workflows within these popular frameworks, and get started by changing only a few lines. The Neuron SDK provides libraries for distributed model training such as Megatron LM and PyTorch Fully Sharded Data Parallel (FSDP).

Invert provides a complete solution for collecting, cleaning and contextualizing data. This ensures that every analysis and insight are based on reliable and organized data. Invert collects, standardizes, and models all your bioprocessing data. It has powerful built-in tools for analysis, machine-learning, and modeling. Data that is clean, standardized and pristine is only the beginning. Explore our suite of tools for data management, analysis and modeling. Replace manual workflows with spreadsheets or statistical software. Calculate anything with powerful statistical features. Automatically generate reports using recent runs. Add interactive plots and calculations and share them with collaborators. Streamline the planning, coordination and execution of experiments. Find the data you want and dive deep into any analysis. Find all the tools to manage your data, from integration to analysis and modeling.

Deep Talk is the fastest way for text to be transformed from chats, emails and surveys into real business intelligence. Our AI platform makes it easy to understand what's going on inside customer communications. Unsupervised deep learning models for unstructured text data analysis Deepers are pre-trained deep learning models that can detect custom patterns in your data. The "Deepers API" allows you to analyze text in real-time and tag text or conversations. Reach out to the people who are in need of a product, ask for a new feature, or complain. Deep Talk offers cloud-based deeplearning models as a service. To extract all the insights and data from WhatsApp, chat conversation, emails, surveys, or social networks, you just need to upload the data or integrate one the support services

You can make changes to your models quickly and easily by turning on the black box, pip installing 3LC. Iterate quickly and remove the guesswork in your model training. Visualize per-sample metrics in your browser. Analyze and fix issues in your dataset by analyzing your training. Interactive data debugging, guided by models. Find out which samples are important or inefficient. Understanding what samples work well and where your model struggles. Improve your model in different ways by weighting your data. Make sparse and non-destructive changes to individual samples or a batch. Keep track of all changes, and restore previous revisions. Data tracking and metrics per-sample, per-epoch will allow you to go deeper than standard experiment trackers. To uncover hidden trends, aggregate metrics by sample features rather than epoch. Each training run should be tied to a specific revision of the dataset for reproducibility.

Amazon SageMaker Feature Store can be used to store, share and manage features for machine-learning (ML) models. Features are inputs to machine learning models that are used for training and inference. In an example, features might include song ratings, listening time, and listener demographics. Multiple teams may use the same features repeatedly, so it is important to ensure that the feature quality is high-quality. It can be difficult to keep the feature stores synchronized when features are used to train models offline in batches. SageMaker Feature Store is a secure and unified place for feature use throughout the ML lifecycle. To encourage feature reuse across ML applications, you can store, share, and manage ML-model features for training and inference. Any data source, streaming or batch, can be used to import features, such as application logs and service logs, clickstreams and sensors, etc.

Amazon SageMaker Data Wrangler cuts down the time it takes for data preparation and aggregation for machine learning (ML). This reduces the time taken from weeks to minutes. SageMaker Data Wrangler makes it easy to simplify the process of data preparation. It also allows you to complete every step of the data preparation workflow (including data exploration, cleansing, visualization, and scaling) using a single visual interface. SQL can be used to quickly select the data you need from a variety of data sources. The Data Quality and Insights Report can be used to automatically check data quality and detect anomalies such as duplicate rows or target leakage. SageMaker Data Wrangler has over 300 built-in data transforms that allow you to quickly transform data without having to write any code. After you've completed your data preparation workflow you can scale it up to your full datasets with SageMaker data processing jobs. You can also train, tune and deploy models using SageMaker data processing jobs.

You can develop on your laptop, then scale the same Python code elastically across hundreds or GPUs on any cloud. Ray converts existing Python concepts into the distributed setting, so any serial application can be easily parallelized with little code changes. With a strong ecosystem distributed libraries, scale compute-heavy machine learning workloads such as model serving, deep learning, and hyperparameter tuning. Scale existing workloads (e.g. Pytorch on Ray is easy to scale by using integrations. Ray Tune and Ray Serve native Ray libraries make it easier to scale the most complex machine learning workloads like hyperparameter tuning, deep learning models training, reinforcement learning, and training deep learning models. In just 10 lines of code, you can get started with distributed hyperparameter tune. Creating distributed apps is hard. Ray is an expert in distributed execution.

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.

Your software can see objects in video and images. A few dozen images can be used to train a computer vision model. This takes less than 24 hours. We support innovators just like you in applying computer vision. Upload files via API or manually, including images, annotations, videos, and audio. There are many annotation formats that we support and it is easy to add training data as you gather it. Roboflow Annotate was designed to make labeling quick and easy. Your team can quickly annotate hundreds upon images in a matter of minutes. You can assess the quality of your data and prepare them for training. Use transformation tools to create new training data. See what configurations result in better model performance. All your experiments can be managed from one central location. You can quickly annotate images right from your browser. Your model can be deployed to the cloud, the edge or the browser. Predict where you need them, in half the time.

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.

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.

The list is not exhaustive. Our solution engineers will work with your team to customize Kolena to your workflows and business metrics. The aggregate metrics do not tell the whole story. Unexpected model behavior is the norm. The current testing processes are manual and error-prone. They also cannot be repeated. Models are evaluated based on arbitrary statistics that do not align with product objectives. It is difficult to track model improvement as data evolves. Techniques that are adequate for research environments do not meet the needs of production.

Pipelines are permanent, models are temporary. Train, Evaluate, Deploy, Repeat. Valohai is the only MLOps platform to automate everything, from data extraction to model deployment. Automate everything, from data extraction to model installation. Automatically store every model, experiment, and artifact. Monitor and deploy models in a Kubernetes cluster. Just point to your code and hit "run". Valohai launches workers and runs your experiments. Then, Valohai shuts down the instances. You can create notebooks, scripts, or shared git projects using any language or framework. Our API allows you to expand endlessly. Track each experiment and trace back to the original training data. All data can be audited and shared.

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.

Neural Designer is a data-science and machine learning platform that allows you to build, train, deploy, and maintain neural network models. This tool was created to allow innovative companies and research centres to focus on their applications, not on programming algorithms or programming techniques. Neural Designer does not require you to code or create block diagrams. Instead, the interface guides users through a series of clearly defined steps. Machine Learning can be applied in different industries. These are some examples of machine learning solutions: - In engineering: Performance optimization, quality improvement and fault detection - In banking, insurance: churn prevention and customer targeting. - In healthcare: medical diagnosis, prognosis and activity recognition, microarray analysis and drug design. Neural Designer's strength is its ability to intuitively build predictive models and perform complex operations.

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 deep learning workload can be accelerated. AI model training and inference can speed up your time to value. Deep learning is becoming more popular as enterprises adopt it to gain and scale insight through speech recognition and natural language processing. Deep learning can read text, images and video at scale and generate patterns for recommendation engines. It can also model financial risk and detect anomalies. Due to the sheer number of layers and volumes of data required to train neural networks, it has been necessary to use high computational power. Businesses are finding it difficult to demonstrate results from deep learning experiments that were implemented in silos.

Amazon SageMaker Model Monitor allows you to select the data you want to monitor and analyze, without having to write any code. SageMaker Model monitor lets you choose data from a variety of options, such as prediction output. It also captures metadata such a timestamp, model name and endpoint so that you can analyze model predictions based upon the metadata. In the case of high volume real time predictions, you can specify the sampling rate as a percentage. The data is stored in an Amazon S3 bucket. This data can be encrypted, configured fine-grained security and defined data retention policies. Access control mechanisms can be implemented for secure access. Amazon SageMaker Model Monitor provides built-in analysis, in the form statistical rules, to detect data drifts and improve model quality. You can also create custom rules and set thresholds for each one.

Easy deployment, management, monitoring, and security of AI models in production. Modzy is an Enterprise AI platform that makes it easy to scale trusted AI in your enterprise. Modzy can help you accelerate the deployment, management and governance of trusted AI. It offers enterprise-grade platform features such as security, APIs and SDKs that allow unlimited model deployment, management and governance. You can deploy on your hardware, private cloud, or public cloud. Includes AirGap deployments, and tactical edge. Auditing and governance for central AI management. This will give you access to all AI models in production in real time. The world's fastest explanation (beta), deep neural network solution, creating audit logs for model predictions. High-tech security features to prevent data poisoning, as well as a full-suite patented Adversarial Defence to protect models in production.

With a single command, you can train and serve large AI models in scale. You can simply point to your S3 bucket. We take care of the rest: orchestration, efficiency and node failures. Simple and scalable. MosaicML allows you to train and deploy large AI model on your data in a secure environment. Keep up with the latest techniques, recipes, and foundation models. Our research team has developed and rigorously tested these recipes. In just a few easy steps, you can deploy your private cloud. Your data and models will never leave the firewalls. You can start in one cloud and continue in another without missing a beat. Own the model trained on your data. Model decisions can be better explained by examining them. Filter content and data according to your business needs. Integrate seamlessly with your existing data pipelines and experiment trackers. We are cloud-agnostic and enterprise-proven.

Core ML creates a model by applying a machine-learning algorithm to a collection of training data. A model is used to make predictions using new input data. Models can perform a variety of tasks which would be difficult to code or impractical. You can train a model, for example, to categorize images or detect specific objects in a photo based on its pixels. After creating the model, you can integrate it into your app and deploy on the device of the user. Your app uses Core ML and user data to make forecasts and train or fine-tune a model. Create ML, which is bundled with Xcode, allows you to build and train a ML model. Create ML models are Core ML formatted and ready to be used in your app. Core ML Tools can be used to convert models from other machine learning libraries into Core ML format. Core ML can be used to retrain a model on the device of a user.

Wallaroo is the last mile of your machine-learning journey. It helps you integrate ML into your production environment and improve your bottom line. Wallaroo was designed from the ground up to make it easy to deploy and manage ML production-wide, unlike Apache Spark or heavy-weight containers. ML that costs up to 80% less and can scale to more data, more complex models, and more models at a fraction of the cost. Wallaroo was designed to allow data scientists to quickly deploy their ML models against live data. This can be used for testing, staging, and prod environments. Wallaroo supports the most extensive range of machine learning training frameworks. The platform will take care of deployment and inference speed and scale, so you can focus on building and iterating your models.

Hopsworks is an open source Enterprise platform that allows you to develop and operate Machine Learning (ML), pipelines at scale. It is built around the first Feature Store for ML in the industry. You can quickly move from data exploration and model building in Python with Jupyter notebooks. Conda is all you need to run production-quality end-to-end ML pipes. Hopsworks can access data from any datasources you choose. They can be in the cloud, on premise, IoT networks or from your Industry 4.0-solution. You can deploy on-premises using your hardware or your preferred cloud provider. Hopsworks will offer the same user experience in cloud deployments or the most secure air-gapped deployments.

Creating ML applications should be easy and frictionless. UnionML is a Python framework that is built on Flyte™ and unifies the ecosystem of ML software into a single interface. Combine the tools you love with a simple, standard API. This allows you to stop writing boilerplate code and focus on the important things: the data and models that learn from it. Fit the rich ecosystems of tools and frameworks to a common protocol for Machine Learning. Implement endpoints using industry-standard machine-learning methods for fetching data and training models. Serve predictions (and more) in order to create a complete ML stack. UnionML apps can be used by data scientists, ML engineers, and MLOps professionals to define a single source for truth about the behavior of your ML system.

FinetuneFast allows you to fine-tune AI models, deploy them quickly and start making money online. Here are some of the features that make FinetuneFast unique: - Fine tune your ML models within days, not weeks - The ultimate ML boilerplate, including text-to-images, LLMs and more - Build your AI app to start earning online quickly - Pre-configured scripts for efficient training of models - Efficient data load pipelines for streamlined processing Hyperparameter optimization tools to improve model performance - Multi-GPU Support out of the Box for enhanced processing power - No-Code AI Model fine-tuning for simple customization - Model deployment with one-click for quick and hassle free deployment - Auto-scaling Infrastructure for seamless scaling of your models as they grow - API endpoint creation for easy integration with other system - Monitoring and logging for real-time performance monitoring

Weights & Biases allows for experiment tracking, hyperparameter optimization and model and dataset versioning. With just 5 lines of code, you can track, compare, and visualise ML experiments. Add a few lines of code to your script and you'll be able to see live updates to your dashboard each time you train a different version of your model. Our hyperparameter search tool is scalable to a massive scale, allowing you to optimize models. Sweeps plug into your existing infrastructure and are lightweight. Save all the details of your machine learning pipeline, including data preparation, data versions, training and evaluation. It's easier than ever to share project updates. Add experiment logging to your script in a matter of minutes. Our lightweight integration is compatible with any Python script. W&B Weave helps developers build and iterate their AI applications with confidence.

A simple-to-use interactive tool that allows data exploration, analysis, visualization and machine learning. Cloud Datalab is an interactive tool that allows you to analyze, transform, visualize, and create machine learning models on Google Cloud Platform. It runs on Compute Engine. It connects to multiple cloud services quickly so you can concentrate on data science tasks. Cloud Datalab is built using Jupyter (formerly IPython), a platform that boasts a rich ecosystem of modules and a solid knowledge base. Cloud Datalab allows you to analyze your data on BigQuery and AI Platform, Compute Engine and Cloud Storage using Python and SQL. JavaScript is also available (for BigQuery user defined functions). Cloud Datalab can handle megabytes and terabytes of data. Cloud Datalab allows you to query terabytes and run local analysis on samples of data, as well as run training jobs on terabytes in AI Platform.

Neptune.ai, a platform for machine learning operations, is designed to streamline tracking, organizing and sharing of experiments, and model-building. It provides a comprehensive platform for data scientists and machine-learning engineers to log, visualise, and compare model training run, datasets and hyperparameters in real-time. Neptune.ai integrates seamlessly with popular machine-learning libraries, allowing teams to efficiently manage research and production workflows. Neptune.ai's features, which include collaboration, versioning and reproducibility of experiments, enhance productivity and help ensure that machine-learning projects are transparent and well documented throughout their lifecycle.

High-value use cases across all major verticals, with proven commercial value in just weeks ElectrifAi's largest library of pre-built machine intelligence models seamlessly integrates into existing workflows to deliver reliable and fast results. Our domain expertise is available through pre-trained, prestructured, or new models. Building machine learning is risky. ElectrifAi delivers superior results that are fast, reliable and accurate. We have over 1,000 machine learning models ready to deploy. They seamlessly integrate into existing workflows. We have the ability to quickly deploy proven ML models and provide solutions. We create the machine learning models, clean up the data, and insinuate the data. Our domain experts use your data to train the model that is most appropriate for your case.

Experience a completely new way to train machine learning models on Mac. Create ML simplifies model training and produces powerful Core ML Core models. Train multiple models with different datasets in one project. Preview the performance of your model using Continuity on your Mac with your iPhone's camera and microphone, or by dropping in sample data. Pause, save, resume and extend your training. Learn interactively how your model performs using test data from your evaluation dataset. Explore key metrics in relation to specific examples, to identify difficult use cases, additional investments in data collection and opportunities to improve model quality. You can improve the performance of model training by using an external graphics processor with your Mac. You can train models on your Mac at lightning speed by utilizing the CPU and GPU. Create ML offers a wide range of model types.

It can take weeks, months or even years to build a model. Reproducing model results, maintaining version control and auditing past work can be complex. Model building is an iterative task. It is important to record each step and how you got there. A model should not be a file that is hidden somewhere. It should be a tangible object that can be tracked and analyzed by all parties. Prevision.io allows users to track each experiment as they train it. You can also view its characteristics, automated analyses, versions, and version history as your project progresses, regardless of whether you used our AutoML or other tools. To build highly performant models, you can automatically experiment with dozens upon dozens of feature engineering strategies. The engine automatically tests different feature engineering strategies for each type of data in a single command. Tabular, text, and images are all options to maximize the information in your data.

The Descartes Labs Platform was created to address some of the most pressing geospatial analysis questions in the world. The platform allows customers to quickly and efficiently build models and algorithms that transform their businesses. We help AI become a core competency by providing data scientists and their line of business colleagues with the best geospatial and modeling tools in one package. Our massive data archive and their own data can be used by data science teams to create models faster than ever before. Our cloud-based platform allows customers to rapidly and securely scale machine learning, statistical, or computer vision models to inform business decisions using powerful raster-based analytics. Our extensive API documentation, tutorials and guides, as well as demos, provide users with a rich knowledge base that allows them to quickly deploy high-value apps across a variety of industries.

Amazon SageMaker Model training reduces the time and costs of training and tuning machine learning (ML), models at scale, without the need for infrastructure management. SageMaker automatically scales infrastructure up or down from one to thousands of GPUs. This allows you to take advantage of the most performant ML compute infrastructure available. You can control your training costs better because you only pay for what you use. SageMaker distributed libraries can automatically split large models across AWS GPU instances. You can also use third-party libraries like DeepSpeed, Horovod or Megatron to speed up deep learning models. You can efficiently manage your system resources using a variety of GPUs and CPUs, including P4d.24xl instances. These are the fastest training instances available in the cloud. Simply specify the location of the data and indicate the type of SageMaker instances to get started.

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.

Amazon SageMaker Studio (IDE) is an integrated development environment that allows you to access purpose-built tools to execute all steps of machine learning (ML). This includes preparing data, building, training and deploying your models. It can improve data science team productivity up to 10x. Quickly upload data, create notebooks, tune models, adjust experiments, collaborate within your organization, and then deploy models to production without leaving SageMaker Studio. All ML development tasks can be performed in one web-based interface, including preparing raw data and monitoring ML models. You can quickly move between the various stages of the ML development lifecycle to fine-tune models. SageMaker Studio allows you to replay training experiments, tune model features, and other inputs, and then compare the results.

The Grace Enterprise AI Platform is an AI platform that supports Governance, Risk, and Compliance (GRC), for AI. Grace allows for a secure, efficient, and robust AI implementation in any organization. It standardizes processes and workflows across all your AI projects. Grace provides the rich functionality that your organization requires to become fully AI-aware. It also helps to ensure regulatory excellence for AI to avoid compliance requirements slowing down or stopping implementation. Grace lowers entry barriers for AI users in all operational and technical roles within your organization. It also offers efficient workflows for data scientists and engineers who are experienced. Ensure that all activities are tracked, explained, and enforced. This covers all areas of the data science model development, including data used for model training, development, bias, and other activities.