Best Machine Learning Software for Keras

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.

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.

The most complex AI, ML, Deep Learning models can be trained. With just a few clicks, you can scale from a single machine up to a whole fleet of VMs. Lambda Cloud makes it easy to scale up or start your Deep Learning project. You can get started quickly, save compute costs, and scale up to hundreds of GPUs. Every VM is pre-installed with the most recent version of Lambda Stack. This includes major deep learning frameworks as well as CUDA®. drivers. You can access the cloud dashboard to instantly access a Jupyter Notebook development environment on each machine. You can connect directly via the Web Terminal or use SSH directly using one of your SSH keys. Lambda can make significant savings by building scaled compute infrastructure to meet the needs of deep learning researchers. Cloud computing allows you to be flexible and save money, even when your workloads increase rapidly.

Explore a new library and dataset in a notebook. A 2orkflow automates preprocessing, training, and testing. A deployment brings your application to life. You can use notebooks, workflows, or deployments separately. Compatible with all. Gradient is compatible with all major frameworks. Gradient is powered with Paperspace's top-of-the-line GPU instances. Source control integration makes it easier to move faster. Connect to GitHub to manage your work and compute resources using git. In seconds, you can launch a GPU-enabled Jupyter Notebook directly from your browser. Any library or framework is possible. Invite collaborators and share a link. This cloud workspace runs on free GPUs. A notebook environment that is easy to use and share can be set up in seconds. Perfect for ML developers. This environment is simple and powerful with lots of features that just work. You can either use a pre-built template, or create your own. Get a free GPU

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.

RZT aiOS provides all the benefits of a unified AI platform, and more. It's not just a platform, it's an Operating System that connects, manages, and unifies all your AI initiatives. AI developers can now do what used to take months in days thanks to aiOS process management which dramatically increases their productivity. This Operating System provides an intuitive environment for AI development. It allows you to visually build models, explore data and create processing pipelines. You can also run experiments and view analytics. It's easy to do all of this without any advanced software engineering skills.

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

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.

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

The Databricks Data Intelligence Platform enables your entire organization to utilize data and AI. It is built on a lakehouse that provides an open, unified platform for all data and governance. It's powered by a Data Intelligence Engine, which understands the uniqueness in your data. Data and AI companies will win in every industry. Databricks can help you achieve your data and AI goals faster and easier. Databricks combines the benefits of a lakehouse with generative AI to power a Data Intelligence Engine which understands the unique semantics in your data. The Databricks Platform can then optimize performance and manage infrastructure according to the unique needs of your business. The Data Intelligence Engine speaks your organization's native language, making it easy to search for and discover new data. It is just like asking a colleague a question.

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.

A platform for machine learning and deep learning applications that is reproducible and scaleable. Learn more about the products and features that make up today's most innovative platform to manage data science workflows. Polyaxon offers an interactive workspace that includes notebooks, tensorboards and visualizations. You can collaborate with your team and share and compare results. Reproducible results are possible with the built-in version control system for code and experiments. Polyaxon can be deployed on-premises, in the cloud, or in hybrid environments. This includes single laptops, container management platforms, and Kubernetes. You can spin up or down, add nodes, increase storage, and add more GPUs.

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.

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.

OctoAI is a world-class computing infrastructure that allows you to run and tune models that will impress your users. Model endpoints that are fast and efficient, with the freedom to run any type of model. OctoAI models can be used or you can bring your own. Create ergonomic model endpoints within minutes with just a few lines code. Customize your model for any use case that benefits your users. You can scale from zero users to millions without worrying about hardware, speed or cost overruns. Use our curated list to find the best open-source foundations models. We've optimized them for faster and cheaper performance using our expertise in machine learning compilation and acceleration techniques. OctoAI selects the best hardware target and applies the latest optimization techniques to keep your running models optimized.

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.

This machine learning monitoring tool allows you to easily monitor and troubleshoot large model volumes. Data scientists can avoid false alarms and dead ends by using an unrivaled explainability accuracy and unique analyses that aren't available anywhere else. This allows them to quickly and effectively address critical problems. So that your business runs at its best, machine learning models are optimized. TruEra's explainability engine is the result of years of dedicated research and development. It is significantly more accurate that current tools. TruEra's enterprise-class AI explainability tech is unrivalled. The core diagnostic engine is built on six years of research by Carnegie Mellon University. It outperforms all competitors. The platform performs sophisticated sensitivity analyses quickly, allowing data scientists, business users, risk and compliance teams to understand how and why a model makes predictions.

Data is essential when it comes to constructing, streamlining and growing your company. Unfortunately, it can be difficult to get the most out of data. Many organizations face incompatibilities, slow results, poor access to data and spiraling costs. Leaders who can transform raw data into real results are the ones who will succeed in today's competitive landscape. This is possible by empowering everyone in your company to be able analyze, build, and collaborate on machine learning and AI solutions. Streamflux is a one stop shop for all your data analytics and AI needs. Our self-service platform gives you the freedom to create end-to-end data solutions. It uses models to answer complex questions, and evaluates user behavior. You can transform raw data into real business impact in days instead of months, whether you are generating recommendations or predicting customer turnover and future revenue.

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.

Optimize ML models with real-time training metrics capture and alerting when anomalies are detected. To reduce the time and costs of training ML models, stop training when the desired accuracy has been achieved. To continuously improve resource utilization, automatically profile and monitor the system's resource utilization. Amazon SageMaker Debugger reduces troubleshooting time from days to minutes. It automatically detects and alerts you when there are common errors in training, such as too large or too small gradient values. You can view alerts in Amazon SageMaker Studio, or configure them through Amazon CloudWatch. The SageMaker Debugger SDK allows you to automatically detect new types of model-specific errors like data sampling, hyperparameter value, and out-of bound values.