Alternatives to Polyaxon

Fully managed ML tools allow you to build, deploy and scale machine-learning (ML) models quickly, for any use case. Vertex AI Workbench is natively integrated with BigQuery Dataproc and Spark. You can use BigQuery to create and execute machine-learning models in BigQuery by using standard SQL queries and spreadsheets or you can export datasets directly from BigQuery into Vertex AI Workbench to run your models there. Vertex Data Labeling can be used to create highly accurate labels for data collection.

An API powered by Google's AI technology allows you to accurately convert speech into text. You can accurately caption your content, provide a better user experience with products using voice commands, and gain insight from customer interactions to improve your service. Google's deep learning neural network algorithms are the most advanced in automatic speech recognition (ASR). Speech-to-Text allows for experimentation, creation, management, and customization of custom resources. You can deploy speech recognition wherever you need it, whether it's in the cloud using the API or on-premises using Speech-to-Text O-Prem. You can customize speech recognition to translate domain-specific terms or rare words. Automated conversion of spoken numbers into addresses, years and currencies. Our user interface makes it easy to experiment with your speech audio.

Open source platform for machine learning. TensorFlow is a machine learning platform that is open-source and available to all. It offers a flexible, comprehensive ecosystem of tools, libraries, and community resources that allows researchers to push the boundaries of machine learning. Developers can easily create and deploy ML-powered applications using its tools. Easy ML model training and development using high-level APIs such as Keras. This allows for quick model iteration and debugging. No matter what language you choose, you can easily train and deploy models in cloud, browser, on-prem, or on-device. It is a simple and flexible architecture that allows you to quickly take new ideas from concept to code to state-of the-art models and publication. TensorFlow makes it easy to build, deploy, and test.

Amazon SageMaker, a fully managed service, provides data scientists and developers with the ability to quickly build, train, deploy, and deploy machine-learning (ML) models. SageMaker takes the hard work out of each step in the machine learning process, making it easier to create high-quality models. Traditional ML development can be complex, costly, and iterative. This is made worse by the lack of integrated tools to support the entire machine learning workflow. It is tedious and error-prone to combine tools and workflows. SageMaker solves the problem by combining all components needed for machine learning into a single toolset. This allows models to be produced faster and with less effort. Amazon SageMaker Studio is a web-based visual interface that allows you to perform all ML development tasks. SageMaker Studio allows you to have complete control over each step and gives you visibility.

MLflow is an open-source platform that manages the ML lifecycle. It includes experimentation, reproducibility and deployment. There is also a central model registry. MLflow currently has four components. Record and query experiments: data, code, config, results. Data science code can be packaged in a format that can be reproduced on any platform. Machine learning models can be deployed in a variety of environments. A central repository can store, annotate and discover models, as well as manage them. The MLflow Tracking component provides an API and UI to log parameters, code versions and metrics. It can also be used to visualize the results later. MLflow Tracking allows you to log and query experiments using Python REST, R API, Java API APIs, and REST. An MLflow Project is a way to package data science code in a reusable, reproducible manner. It is based primarily upon conventions. The Projects component also includes an API and command line tools to run projects.

Manage and optimize models throughout the entire ML lifecycle. This includes experiment tracking, monitoring production models, and more. The platform was designed to meet the demands of large enterprise teams that deploy ML at scale. It supports any deployment strategy, whether it is private cloud, hybrid, or on-premise servers. Add two lines of code into your notebook or script to start tracking your experiments. It works with any machine-learning library and for any task. To understand differences in model performance, you can easily compare code, hyperparameters and metrics. Monitor your models from training to production. You can get alerts when something is wrong and debug your model to fix it. You can increase productivity, collaboration, visibility, and visibility among data scientists, data science groups, and even business stakeholders.

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.

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.

Distributed training is possible without changing the model code. Determined takes care of provisioning, networking, data load, and fault tolerance. Our open-source deep-learning platform allows you to train your models in minutes and hours, not days or weeks. You can avoid tedious tasks such as manual hyperparameter tweaking, re-running failed jobs, or worrying about hardware resources. Our distributed training implementation is more efficient than the industry standard. It requires no code changes and is fully integrated into our state-ofthe-art platform. With its built-in experiment tracker and visualization, Determined records metrics and makes your ML project reproducible. It also allows your team to work together more easily. Instead of worrying about infrastructure and errors, your researchers can focus on their domain and build upon the progress made by their team.

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.

Accelerate the entire machine learning lifecycle. Developers and data scientists can have more productive experiences building, training, and deploying machine-learning models faster by empowering them. Accelerate time-to-market and foster collaboration with industry-leading MLOps -DevOps machine learning. Innovate on a trusted platform that is secure and trustworthy, which is designed for responsible ML. Productivity for all levels, code-first and drag and drop designer, and automated machine-learning. Robust MLOps capabilities integrate with existing DevOps processes to help manage the entire ML lifecycle. Responsible ML capabilities – understand models with interpretability, fairness, and protect data with differential privacy, confidential computing, as well as control the ML cycle with datasheets and audit trials. Open-source languages and frameworks supported by the best in class, including MLflow and Kubeflow, ONNX and PyTorch. TensorFlow and Python are also supported.

Guild AI is a free, open-source toolkit for experiment tracking. It allows users to build faster and better models by bringing systematic control to machine-learning workflows. It captures all details of training runs and treats them as unique experiments. This allows for comprehensive tracking and analysis. Users can compare and analyse runs to improve their understanding and incrementally enhance models. Guild AI simplifies hyperparameter optimization by applying state-of the-art algorithms via simple commands, eliminating complex trial setups. It also supports pipeline automation, accelerating model creation, reducing errors and providing measurable outcomes. The toolkit runs on all major operating system platforms and integrates seamlessly with existing software engineering applications. Guild AI supports a variety of remote storage types including Amazon S3, Google Cloud Storage and Azure Blob Storage.

Data Version Control (DVC), an open-source version control system, is tailored for data science and ML projects. It provides a Git-like interface for organizing data, models, experiments, and allowing users to manage and version audio, video, text, and image files in storage. Users can also structure their machine learning modelling process into a reproducible work flow. DVC integrates seamlessly into existing software engineering tools. Teams can define any aspect of machine learning projects in metafiles that are readable by humans. This approach reduces the gap between software engineering and data science by allowing the use of established engineering toolsets and best practices. DVC leverages Git to enable versioning and sharing for entire machine learning projects. This includes source code, configurations and parameters, metrics and data assets.

ClearML is an open-source MLOps platform that enables data scientists, ML engineers, and DevOps to easily create, orchestrate and automate ML processes at scale. Our frictionless and unified end-to-end MLOps Suite allows users and customers to concentrate on developing ML code and automating their workflows. ClearML is used to develop a highly reproducible process for end-to-end AI models lifecycles by more than 1,300 enterprises, from product feature discovery to model deployment and production monitoring. You can use all of our modules to create a complete ecosystem, or you can plug in your existing tools and start using them. ClearML is trusted worldwide by more than 150,000 Data Scientists, Data Engineers and ML Engineers at Fortune 500 companies, enterprises and innovative start-ups.

DagsHub, a collaborative platform for data scientists and machine-learning engineers, is designed to streamline and manage their projects. It integrates code and data, experiments and models in a unified environment to facilitate efficient project management and collaboration. The user-friendly interface includes features such as dataset management, experiment tracker, model registry, data and model lineage and model registry. DagsHub integrates seamlessly with popular MLOps software, allowing users the ability to leverage their existing workflows. DagsHub improves machine learning development efficiency, transparency, and reproducibility by providing a central hub for all project elements. DagsHub, a platform for AI/ML developers, allows you to manage and collaborate with your data, models and experiments alongside your code. DagsHub is designed to handle unstructured data, such as text, images, audio files, medical imaging and binary files.

Amazon SageMaker offers all the tools and libraries needed to build ML models. It allows you to iteratively test different algorithms and evaluate their accuracy to determine the best one for you. Amazon SageMaker allows you to choose from over 15 algorithms that have been optimized for SageMaker. You can also access over 150 pre-built models available from popular model zoos with just a few clicks. SageMaker offers a variety model-building tools, including RStudio and Amazon SageMaker Studio Notebooks. These allow you to run ML models on a small scale and view reports on their performance. This allows you to create high-quality working prototypes. Amazon SageMaker Studio Notebooks make it easier to build ML models and collaborate with your team. Amazon SageMaker Studio notebooks allow you to start working in seconds with Jupyter notebooks. Amazon SageMaker allows for one-click sharing of notebooks.

HPE Ezmeral ML Ops offers pre-packaged tools that enable you to operate machine learning workflows at any stage of the ML lifecycle. This will give you DevOps-like speed, agility, and speed. You can quickly set up environments using your preferred data science tools. This allows you to explore multiple enterprise data sources, and simultaneously experiment with multiple deep learning frameworks or machine learning models to find the best model for the business problems. On-demand, self-service environments that can be used for testing and development as well as production workloads. Highly performant training environments with separation of compute/storage that securely access shared enterprise data sources in cloud-based or on-premises storage.

An end-to-end solution gives you all the tools your data science team needs to scale your machine learning development, from research to production. cnvrg.io, the world's leading data science platform for MLOps (model management) is a leader in creating cutting-edge machine-learning development solutions that allow you to build high-impact models in half the time. In a collaborative and clear machine learning management environment, bridge science and engineering teams. Use interactive workspaces, dashboards and model repositories to communicate and reproduce results. You should be less concerned about technical complexity and more focused on creating high-impact ML models. The Cnvrg.io container based infrastructure simplifies engineering heavy tasks such as tracking, monitoring and configuration, compute resource management, server infrastructure, feature extraction, model deployment, and serving infrastructure.

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.

Google Cloud allows you to quickly build your deep learning project. You can quickly prototype your AI applications using Deep Learning Containers. These Docker images are compatible with popular frameworks, optimized for performance, and ready to be deployed. Deep Learning Containers create a consistent environment across Google Cloud Services, making it easy for you to scale in the cloud and shift from on-premises. You can deploy on Google Kubernetes Engine, AI Platform, Cloud Run and Compute Engine as well as Docker Swarm and Kubernetes Engine.

TensorBoard, TensorFlow’s comprehensive visualization toolkit, is designed to facilitate machine-learning experimentation. It allows users to track and visual metrics such as accuracy and loss, visualize the model graph, view histograms for weights, biases or other tensors over time, display embeddings in a lower-dimensional area, and display images and text. TensorBoard also offers profiling capabilities for optimizing TensorFlow programmes. These features provide a suite to help understand, debug and optimize TensorFlow, improving the machine learning workflow. To improve something in machine learning, you need to be able measure it. TensorBoard provides the measurements and visualisations required during the machine-learning workflow. It allows tracking experiment metrics, visualizing model graphs, and projecting embedded embeddings into a lower-dimensional space.

Aim logs your AI metadata (experiments and prompts) enables a UI for comparison & observation, and SDK for programmatic querying. Aim is a self-hosted, open-source AI Metadata tracking tool that can handle 100,000s tracked metadata sequences. The two most famous AI metadata applications include experiment tracking and prompting engineering. Aim offers a beautiful and performant UI for exploring, comparing and exploring training runs and prompt sessions.

AI engineering does not have to be a mystery. You can get full visibility using tools for tracing and evaluation, prompt management and more. HoneyHive is a platform for AI observability, evaluation and team collaboration that helps teams build reliable generative AI applications. It provides tools for evaluating and testing AI models and monitoring them, allowing engineers, product managers and domain experts to work together effectively. Measure the quality of large test suites in order to identify improvements and regressions at each iteration. Track usage, feedback and quality at a large scale to identify issues and drive continuous improvements. HoneyHive offers flexibility and scalability for diverse organizational needs. It supports integration with different model providers and frameworks. It is ideal for teams who want to ensure the performance and quality of their AI agents. It provides a unified platform that allows for evaluation, monitoring and prompt management.

Visdom is an interactive visualization tool that helps researchers and developers keep track of their remote servers-based scientific experiments. Visdom visualizations can be viewed and shared in browsers. Visdom is an interactive visualization tool to support scientific experimentation. Visualizations can be broadcast to collaborators and yourself. Visdom's UI allows researchers and developers alike to organize the visualization space, allowing them to debug code and inspect results from multiple projects. Windows, environments, filters, and views are also available to organize and view important experimental data. Create and customize visualizations to suit your project.

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.

ROBIN is the first hyper-converged Kubernetes platform in the industry for big data, databases and AI/ML. The platform offers a self-service App store experience to deploy any application anywhere. It runs on-premises in your private cloud or in public-cloud environments (AWS, Azure and GCP). Hyper-converged Kubernetes combines containerized storage and networking with compute (Kubernetes) and the application management layer to create a single system. Our approach extends Kubernetes to data-intensive applications like Hortonworks, Cloudera and Elastic stack, RDBMSs, NoSQL database, and AI/ML. Facilitates faster and easier roll-out of important Enterprise IT and LoB initiatives such as containerization and cloud-migration, cost consolidation, productivity improvement, and cost-consolidation. This solution addresses the fundamental problems of managing big data and databases in Kubernetes.

The Domino Enterprise MLOps Platform helps data science teams improve the speed, quality, and impact of data science at scale. Domino is open and flexible, empowering professional data scientists to use their preferred tools and infrastructure. Data science models get into production fast and are kept operating at peak performance with integrated workflows. Domino also delivers the security, governance and compliance that enterprises expect. The Self-Service Infrastructure Portal makes data science teams become more productive with easy access to their preferred tools, scalable compute, and diverse data sets. By automating time-consuming and tedious DevOps tasks, data scientists can focus on the tasks at hand. The Integrated Model Factory includes a workbench, model and app deployment, and integrated monitoring to rapidly experiment, deploy the best models in production, ensure optimal performance, and collaborate across the end-to-end data science lifecycle. The System of Record has a powerful reproducibility engine, search and knowledge management, and integrated project management. Teams can easily find, reuse, reproduce, and build on any data science work to amplify innovation.

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.

Data science platform that increases productivity and has unparalleled capabilities. Create and evaluate machine learning (ML), models of higher quality. Easy deployment of ML models can help increase business flexibility and enable enterprise-trusted data work faster. Cloud-based platforms can be used to uncover new business insights. Iterative processes are necessary to build a machine-learning model. This ebook will explain how machine learning models are constructed and break down the process. Use notebooks to build and test machine learning algorithms. AutoML will show you the results of data science. It is easier and faster to create high-quality models. Automated machine-learning capabilities quickly analyze the data and recommend the best data features and algorithms. Automated machine learning also tunes the model and explains its results.

Kubeflow is a project that makes machine learning (ML), workflows on Kubernetes portable, scalable, and easy to deploy. Our goal is not create new services, but to make it easy to deploy the best-of-breed open source systems for ML to different infrastructures. Kubeflow can be run anywhere Kubernetes is running. Kubeflow offers a custom TensorFlow job operator that can be used to train your ML model. Kubeflow's job manager can handle distributed TensorFlow training jobs. You can configure the training controller to use GPUs or CPUs, and to adapt to different cluster sizes. Kubeflow provides services to create and manage interactive Jupyter Notebooks. You can adjust your notebook deployment and compute resources to meet your data science requirements. You can experiment with your workflows locally and then move them to the cloud when you are ready.

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.

Paradise employs robust, unsupervised machine-learning and supervised deep learning technologies in order to increase interpretation and gain greater insight from the data. Generate attributes to extract valuable geological information and for input into machine learning analysis. Identify the attributes that have the greatest variance and contribution to a given set of attributes in a particular geologic setting. Display the neural classes (topology), and the associated colors resulting Stratigraphic analysis. These indicate the distribution of facies. Deep learning and machine learning can automatically detect faults. Compare machine learning classification results with other seismic attributes to traditional logs. In fraction of the time it takes to generate spectral and geometric decomposition attributes on a cluster compute nodes, you can do this in fraction of the time with a single machine.

A fully-featured machine learning platform empowers enterprises to conduct real data science at scale and speed. You can spend less time managing infrastructure and tools so that you can concentrate on building machine learning applications to propel your business forward. Anaconda Enterprise removes the hassle from ML operations and puts open-source innovation at the fingertips. It provides the foundation for serious machine learning and data science production without locking you into any specific models, templates, workflows, or models. AE allows data scientists and software developers to work together to create, test, debug and deploy models using their preferred languages. AE gives developers and data scientists access to both notebooks as well as IDEs, allowing them to work more efficiently together. They can also choose between preconfigured projects and example projects. AE projects can be easily moved from one environment to the next by being automatically packaged.

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.

Elastic Fabric Adapter is a network-interface for Amazon EC2 instances. It allows customers to run applications that require high levels of internode communication at scale. Its custom-built OS bypass hardware interface improves the performance of interinstance communications which is crucial for scaling these applications. EFA allows High-Performance Computing applications (HPC) using the Message Passing Interface, (MPI), and Machine Learning applications (ML) using NVIDIA's Collective Communications Library, (NCCL), to scale up to thousands of CPUs and GPUs. You get the performance of HPC clusters on-premises, with the elasticity and flexibility on-demand of AWS. EFA is a free networking feature available on all supported EC2 instances. Plus, EFA works with the most common interfaces, libraries, and APIs for inter-node communication.

AIxBlock is an end-to-end blockchain-based platform for AI that harnesses unused computing resources of BTC miners, as well as all global consumer GPUs. Our platform's training method is a hybrid machine learning approach that allows simultaneous training on multiple nodes. We use the DeepSpeed-TED method, a three-dimensional hybrid parallel algorithm which integrates data, tensor and expert parallelism. This allows for the training of Mixture of Experts models (MoE) on base models that are 4 to 8x larger than the current state of the art. The platform will identify and add compatible computing resources from the computing marketplace to the existing cluster of training nodes, and distribute the ML model for unlimited computations. This process unfolds dynamically and automatically, culminating in decentralized supercomputers which facilitate AI success.

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.

Platform with NVIDIA H100 Tensor core GPUs. Competitive pricing. Support from a dedicated team. Built for large-scale ML workloads. Get the most from multihost training with thousands of H100 GPUs in full mesh connections using the latest InfiniBand networks up to 3.2Tb/s. Best value: Save up to 50% on GPU compute when compared with major public cloud providers*. You can save even more by purchasing GPUs in large quantities and reserving GPUs. Onboarding assistance: We provide a dedicated engineer to ensure smooth platform adoption. Get your infrastructure optimized, and k8s installed. Fully managed Kubernetes - Simplify the deployment and scaling of ML frameworks using Kubernetes. Use Managed Kubernetes to train GPUs on multiple nodes. Marketplace with ML Frameworks: Browse our Marketplace to find ML-focused libraries and applications, frameworks, and tools that will streamline your model training. Easy to use. All new users are entitled to a one-month free trial.

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.

It works with Jupyter Notebooks or any other Python environment. Modelbit will deploy your model and all its dependencies to production by calling modelbi.deploy. Modelbit's ML models can be called from your warehouse just as easily as a SQL function. They can be called directly as a REST-endpoint from your product. Modelbit is backed up by your git repository. GitHub, GitLab or your own. Code review. CI/CD pipelines. PRs and merge request. Bring your entire git workflow into your Python ML models. Modelbit integrates seamlessly into Hex, DeepNote and Noteable. Modelbit lets you take your model directly from your cloud notebook to production. Tired of VPC configurations or IAM roles? Redeploy SageMaker models seamlessly to Modelbit. Modelbit's platform is available to you immediately with the models that you have already created.

Almato's high-security, out-of-the box AI services make you smarter. We adapt to your business model, on-premises, public cloud, or private cloud. These are the foundation for innovative extensions of business apps, upskilling bots or analytics. Digitize with artificial Intelligence from Almato: Machine Learning for Cognitive Automation and intelligent apps. The Almato intelligent scanner can be integrated quickly and easily into any app. It is specifically designed for international retail companies. The intelligent scanner is used to link the digital and analog worlds. Customers will find the analog shopping experience more efficient with digital components and intuitive use. Diverse but custom AI and ML solutions can lead to significant cost savings and improved customer experience.

Easy management, deployment and monitoring of machine learning models for supercharging MLOps. Available for all organizations on the best AI platform. You can use navio for various machine learning operations across your entire artificial intelligence landscape. Machine learning can be integrated into your business workflow to make a tangible, measurable impact on your business. navio offers various Machine Learning Operations (MLOps), which can be used to support you from the initial model development phase to the production run of your model. Automatically create REST endspoints and keep track the clients or machines that interact with your model. To get the best results, you should focus on exploring and training your models. You can also stop wasting time and resources setting up infrastructure. Let navio manage all aspects of product ionization so you can go live quickly with your machine-learning models.

Segmind simplifies access to large compute. It can be used to run high-performance workloads like Deep learning training and other complex processing jobs. Segmind allows you to create zero-setup environments in minutes and lets you share the access with other members of your team. Segmind's MLOps platform is also able to manage deep learning projects from start to finish with integrated data storage, experiment tracking, and data storage.

Kraken is suitable for all data scientists and analysts. It is designed to be easy-to-use and no-code automated machine-learning platform. The Kraken no code automated machine learning platform (AutoML), simplifies and automates data science tasks such as data prep, data cleaning and algorithm selection. It also allows for model training and deployment. Kraken was designed with engineers and analysts in mind. If you've done data analysis before, you're ready! Kraken's intuitive interface and integrated SONAR(c), training make it easy for citizens to become data scientists. Data scientists can work more efficiently and faster with advanced features. You can use Excel or flat files for daily reporting, or just ad-hoc analysis. With Kraken's drag-and-drop CSV upload feature and the Amazon S3 connector, you can quickly start building models. Kraken's Data Connectors allow you to connect with your favorite data warehouse, business intelligence tool, or cloud storage.

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

KitOps, a packaging, versioning and sharing system, is designed for AI/ML project. It uses open standards, so it can be used with your existing AI/ML, DevOps, and development tools. It can also be stored in the enterprise container registry. It is the preferred solution of AI/ML platform engineers for packaging and versioning assets. KitOps creates an AI/ML ModelKit that includes everything you need to replicate it locally or deploy it in production. You can unpack a ModelKit selectively so that different team members can save storage space and time by only taking what they need to complete a task. ModelKits are easy to track, control and audit because they're immutable, signed and reside in your existing container registry.

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.

Darwin is an automated machine-learning product that allows your data science and business analysis teams to quickly move from data to meaningful results. Darwin assists organizations in scaling the adoption of data science across their teams and the implementation machine learning applications across operations to become data-driven enterprises.

With a fully automated cloud infrastructure, experts can explore data and write stable codes at the same time. Zerve’s data science environment gives data scientists and ML teams a unified workspace to explore, collaborate and build data science & AI project like never before. Zerve provides true language interoperability. Users can use Python, R SQL or Markdown in the same canvas and connect these code blocks. Zerve offers unlimited parallelization, allowing for code blocks and containers to run in parallel at any stage of development. Analysis artifacts can be automatically serialized, stored and preserved. This allows you to change a step without having to rerun previous steps. Selecting compute resources and memory in a fine-grained manner for complex data transformation.

All work can be synced, searched and managed across your data science team. Zepl's powerful search allows you to discover and reuse models, code, and other data. Zepl's enterprise collaboration platform allows you to query data from Snowflake or Athena and then build your models in Python. For enhanced interactions with your data, use dynamic forms and pivoting. Zepl creates new containers every time you open your notebook. This ensures that you have the same image each time your models are run. You can invite your team members to join you in a shared space, and they will be able to work together in real-time. Or they can simply leave comments on a notebook. You can share your work with fine-grained access controls. You can allow others to read, edit, run, and share your work. This will facilitate collaboration and distribution. All notebooks can be saved and versioned automatically. An easy-to-use interface allows you to name, manage, roll back, and roll back all versions. You can also export seamlessly into Github.