Best Machine Learning Software for Grafana

InsightFinder Unified Intelligence Engine platform (UIE) provides human-centered AI solutions to identify root causes of incidents and prevent them from happening. InsightFinder uses patented self-tuning, unsupervised machine learning to continuously learn from logs, traces and triage threads of DevOps Engineers and SREs to identify root causes and predict future incidents. Companies of all sizes have adopted the platform and found that they can predict business-impacting incidents hours ahead of time with clearly identified root causes. You can get a complete overview of your IT Ops environment, including trends and patterns as well as team activities. You can also view calculations that show overall downtime savings, cost-of-labor savings, and the number of incidents solved.

TrueFoundry provides data scientists and ML engineers with the fastest framework to support the post-model pipeline. With the best DevOps practices, we enable instant monitored endpoints to models in just 15 minutes! You can save, version, and monitor ML models and artifacts. With one command, you can create an endpoint for your ML Model. WebApps can be created without any frontend knowledge or exposure to other users as per your choice. Social swag! Our mission is to make machine learning fast and scalable, which will bring positive value! TrueFoundry is enabling this transformation by automating parts of the ML pipeline that are automated and empowering ML Developers with the ability to test and launch models quickly and with as much autonomy possible. Our inspiration comes from the products that Platform teams have created in top tech companies such as Facebook, Google, Netflix, and others. These products allow all teams to move faster and deploy and iterate independently.

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.

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.

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.

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.