Best AI Development Platforms for Amazon EC2

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.

Faros AI combines all your operational data from multiple sources and enhances them with machine learning signals. The Faros AI Engineering Operations Platform allows you to harness this data so you can accelerate productivity, and better manager your engineering operations. With Faros AI, engineering leaders can scale their operations in a more data-informed way — using data to identify bottlenecks, measure progress towards organizational goals, better support teams with the right resources, and accurately assess the impact of interventions over time. DORA Metrics come standard in Faros AI, and the platform is extensible to allow organizations to build their own custom dashboards and metrics so they can get deep insights into their engineering operations and take intelligent action in a data-driven manner. Leading organizations including Box, Coursera, GoFundMe, Astronomer, Salesforce, etc. trust Faros AI as their engops platform of choice.

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.

PostgresML is an entire platform that comes as a PostgreSQL Extension. Build simpler, faster and more scalable model right inside your database. Explore the SDK, and test open-source models in our hosted databases. Automate the entire workflow, from embedding creation to indexing and Querying for the easiest (and fastest) knowledge based chatbot implementation. Use multiple types of machine learning and natural language processing models, such as vector search or personalization with embeddings, to improve search results. Time series forecasting can help you gain key business insights. SQL and dozens regression algorithms allow you to build statistical and predictive models. ML at database layer can detect fraud and return results faster. PostgresML abstracts data management overheads from the ML/AI cycle by allowing users to run ML/LLM on a Postgres Database.