Best AI Development Platforms for AWS Lambda

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.

Oumi, a platform open-source, streamlines the lifecycle of foundational models from data preparation to training and evaluation. It supports training and fine tuning models with parameters ranging from 10 millions to 405 billion using state-of the-art techniques like SFT, LoRA QLoRA and DPO. The platform supports text and multimodal models including architectures such as Llama DeepSeek Qwen and Phi. Oumi provides tools for data curation and synthesis, allowing users to efficiently generate and manage training datasets. It integrates with popular engines such as vLLM and SGLang for deployment, ensuring efficient serving of models. The platform provides comprehensive evaluation capabilities to assess model performance using standard benchmarks. Oumi is designed to be flexible and can run in a variety of environments, including local laptops, cloud infrastructures like AWS, Azure GCP, Lambda, etc.

Saagie's cloud data factory allows you to create and manage your data & AI project in a single interface. It can be deployed in just a few seconds. Saagie data factories allows you to develop your AI models and test them in a safe environment. With a single interface, you can get your data and AI project off the ground and centralize your team to make rapid progress. Saagie is the platform for you, no matter what your maturity level. From your first data project, to a data and AI-driven strategy. Unifying your work onto a single platform will simplify your workflows, increase your productivity and help you make better decisions. Orchestrate your data pipelines to transform raw data into powerful insights. Quickly access the information you require to make better decisions. Simplify management and scaleability of your AI and data infrastructure. Accelerate your AI, deep learning, and machine learning models.

No one wants to have their AI respond to a client with outdated information. Neum AI provides accurate and current context for AI applications. Set up your data pipelines quickly by using built-in connectors. These include data sources such as Amazon S3 and Azure Blob Storage and vector stores such as Pinecone and Weaviate. Transform and embed your data using built-in connectors to embed models like OpenAI, Replicate and serverless functions such as Azure Functions and AWS Lambda. Use role-based controls to ensure that only the right people have access to specific vectors. Bring your own embedding model, vector stores, and sources. Ask us how you can run Neum AI on your own cloud.

Simulations based on historical data can be used to improve models and update them incrementally. Data governance, access control, and versioning allow for easy collaboration. Ikigai has a wide range of integrations that make it easy to integrate with tools already in your workflow. Ikigai has 200+ connectors that allow you to connect to almost any data source. Want to push your ML to a dashboard or website? Integrate directly using Ikigai’s web integrations. Triggers can be used to run data synchronizations, and retrieve updates every time you run an automation flow. You can integrate Ikigai seamlessly by using your own APIs or creating APIs for your data stack.