Best IT Management Software for BentoML

Compute Engine is Google's infrastructure as a service (IaaS) platform for organizations to create and run cloud-based virtual machines. Computing infrastructure in predefined or custom machine sizes to accelerate your cloud transformation. General purpose (E2, N1, N2, N2D) machines provide a good balance of price and performance. Compute optimized (C2) machines offer high-end vCPU performance for compute-intensive workloads. Memory optimized (M2) machines offer the highest memory and are great for in-memory databases. Accelerator optimized (A2) machines are based on the A100 GPU, for very demanding applications. Integrate Compute with other Google Cloud services such as AI/ML and data analytics. Make reservations to help ensure your applications have the capacity they need as they scale. Save money just for running Compute with sustained-use discounts, and achieve greater savings when you use committed-use discounts.

Cloud Run is a fully-managed compute platform that lets you run your code in a container directly on top of Google's scalable infrastructure. We’ve intentionally designed Cloud Run to make developers more productive - you get to focus on writing your code, using your favorite language, and Cloud Run takes care of operating your service. Fully managed compute platform for deploying and scaling containerized applications quickly and securely. Write code your way using your favorite languages (Go, Python, Java, Ruby, Node.js, and more). Abstract away all infrastructure management for a simple developer experience. Build applications in your favorite language, with your favorite dependencies and tools, and deploy them in seconds. Cloud Run abstracts away all infrastructure management by automatically scaling up and down from zero almost instantaneously—depending on traffic. Cloud Run only charges you for the exact resources you use. Cloud Run makes app development & deployment simpler.

An end-to-end open source machine learning platform. TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications. Build and train ML models easily using intuitive high-level APIs like Keras with eager execution, which makes for immediate model iteration and easy debugging. Easily train and deploy models in the cloud, on-prem, in the browser, or on-device no matter what language you use. A simple and flexible architecture to take new ideas from concept to code, to state-of-the-art models, and to publication faster. Build, deploy, and experiment easily with TensorFlow.

Docker takes away repetitive, mundane configuration tasks and is used throughout the development lifecycle for fast, easy and portable application development, desktop and cloud. Docker’s comprehensive end-to-end platform includes UIs, CLIs, APIs and security that are engineered to work together across the entire application delivery lifecycle. Get a head start on your coding by leveraging Docker images to efficiently develop your own unique applications on Windows and Mac. Create your multi-container application using Docker Compose. Integrate with your favorite tools throughout your development pipeline, Docker works with all development tools you use including VS Code, CircleCI and GitHub. Package applications as portable container images to run in any environment consistently from on-premises Kubernetes to AWS ECS, Azure ACI, Google GKE and more. Leverage Docker Trusted Content, including Docker Official Images and images from Docker Verified Publishers.

Kubernetes (K8s) is an open-source system for automating deployment, scaling, and management of containerized applications. It groups containers that make up an application into logical units for easy management and discovery. Kubernetes builds upon 15 years of experience of running production workloads at Google, combined with best-of-breed ideas and practices from the community. Designed on the same principles that allows Google to run billions of containers a week, Kubernetes can scale without increasing your ops team. Whether testing locally or running a global enterprise, Kubernetes flexibility grows with you to deliver your applications consistently and easily no matter how complex your need is. Kubernetes is open source giving you the freedom to take advantage of on-premises, hybrid, or public cloud infrastructure, letting you effortlessly move workloads to where it matters to you.

Whether you're looking for compute power, database storage, content delivery, or other functionality, AWS has the services to help you build sophisticated applications with increased flexibility, scalability and reliability. Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform, offering over 175 fully featured services from data centers globally. Millions of customers—including the fastest-growing startups, largest enterprises, and leading government agencies—are using AWS to lower costs, become more agile, and innovate faster. AWS has significantly more services, and more features within those services, than any other cloud provider–from infrastructure technologies like compute, storage, and databases–to emerging technologies, such as machine learning and artificial intelligence, data lakes and analytics, and Internet of Things. This makes it faster, easier, and more cost effective to move your existing applications to the cloud.

Heroku is a cloud platform that lets companies build, deliver, monitor and scale apps — we're the fastest way to go from idea to URL, bypassing all those infrastructure headaches. “There’s an app for that” – only a few years ago a catchy marketing campaign introduced the world to a new relationship with the mobile phone. Now, apps have become a way of life for most of us. Whether mobile or web, apps and their underlying APIs are how we manage our lives, make purchases, socialize, stay informed, and interact with customers. An app starts impacting the world when customers start interacting with it. Getting apps out in the wild, out onto the Internet quickly, and iterating, fast, is what can make or break companies. Heroku focuses relentlessly on apps and the developer experience around apps. Heroku lets companies of all sizes embrace the value of apps, not the distraction of hardware, nor the distraction of servers - virtual or otherwise.

Power your metrics and alerting with a leading open-source monitoring solution. Prometheus fundamentally stores all data as time series: streams of timestamped values belonging to the same metric and the same set of labeled dimensions. Besides stored time series, Prometheus may generate temporary derived time series as the result of queries. Prometheus provides a functional query language called PromQL (Prometheus Query Language) that lets the user select and aggregate time series data in real time. The result of an expression can either be shown as a graph, viewed as tabular data in Prometheus's expression browser, or consumed by external systems via the HTTP API. Prometheus is configured via command-line flags and a configuration file. While the command-line flags configure immutable system parameters (such as storage locations, amount of data to keep on disk and in memory, etc.). Download: https://sourceforge.net/projects/prometheus.mirror/

Build, store, secure, scan, replicate, and manage container images and artifacts with a fully managed, geo-replicated instance of OCI distribution. Connect across environments, including Azure Kubernetes Service and Azure Red Hat OpenShift, and across Azure services like App Service, Machine Learning, and Batch. Geo-replication to efficiently manage a single registry across multiple regions. OCI artifact repository for adding helm charts, singularity support, and new OCI artifact-supported formats. Automated container building and patching including base image updates and task scheduling. Integrated security with Azure Active Directory (Azure AD) authentication, role-based access control, Docker content trust, and virtual network integration. Streamline building, testing, pushing, and deploying images to Azure with Azure Container Registry Tasks.

Knative, created originally by Google with contributions from over 50 different companies, delivers an essential set of components to build and run serverless applications on Kubernetes. Knative offers features like scale-to-zero, autoscaling, in-cluster builds, and eventing framework for cloud-native applications on Kubernetes. Whether on-premises, in the cloud, or in a third-party data center, Knative codifies the best practices shared by successful real-world Kubernetes-based frameworks. Most importantly, Knative enables developers to focus on writing code without the need to worry about the “boring but difficult” parts of building, deploying, and managing their application.

Run code without thinking about servers. Pay only for the compute time you consume. AWS Lambda lets you run code without provisioning or managing servers. You pay only for the compute time you consume. With Lambda, you can run code for virtually any type of application or backend service - all with zero administration. Just upload your code and Lambda takes care of everything required to run and scale your code with high availability. You can set up your code to automatically trigger from other AWS services or call it directly from any web or mobile app. AWS Lambda automatically runs your code without requiring you to provision or manage servers. Just write the code and upload it to Lambda. AWS Lambda automatically scales your application by running code in response to each trigger. Your code runs in parallel and processes each trigger individually, scaling precisely with the size of the workload.

Observe all of your data in one place with Enterprise plugins like Splunk, ServiceNow, Datadog, and more. Built-in collaboration features allow teams to work together from a single dashboard. Advanced security and compliance features to ensure your data is always secure. Access to Prometheus, Graphite, Grafana experts and hands-on support teams. Other vendors will try to sell you an “everything in my database” mentality. At Grafana Labs, we have a different approach: We want to help you with your observability, not own it. Grafana Enterprise includes access to enterprise plugins that take your existing data sources and allow you to drop them right into Grafana. This means you can get the best out of your complex, expensive monitoring solutions and databases by visualizing all the data in an easier and more effective way.

Develop more efficiently with Functions, an event-driven serverless compute platform that can also solve complex orchestration problems. Build and debug locally without additional setup, deploy and operate at scale in the cloud, and integrate services using triggers and bindings. End-to-end development experience with integrated tools and built-in DevOps capabilities. Integrated programming model to respond to events and seamlessly connect to other services. Implement a variety of functions and scenarios, such as web apps and APIs with .NET, Node.js, or Java; machine learning workflows with Python; and cloud automation with PowerShell. Get a complete serverless application development experience—from building and debugging locally to deploying and monitoring in the cloud.

Airflow is a platform created by the community to programmatically author, schedule and monitor workflows. Airflow has a modular architecture and uses a message queue to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity. Airflow pipelines are defined in Python, allowing for dynamic pipeline generation. This allows for writing code that instantiates pipelines dynamically. Easily define your own operators and extend libraries to fit the level of abstraction that suits your environment. Airflow pipelines are lean and explicit. Parametrization is built into its core using the powerful Jinja templating engine. No more command-line or XML black-magic! Use standard Python features to create your workflows, including date time formats for scheduling and loops to dynamically generate tasks. This allows you to maintain full flexibility when building your workflows.