Business Software for Amazon SageMaker

Around 25 million engineers work across dozens of distinct functions. Engineers are using New Relic as every company is becoming a software company to gather real-time insight and trending data on the performance of their software. This allows them to be more resilient and provide exceptional customer experiences. New Relic is the only platform that offers an all-in one solution. New Relic offers customers a secure cloud for all metrics and events, powerful full-stack analytics tools, and simple, transparent pricing based on usage. New Relic also has curated the largest open source ecosystem in the industry, making it simple for engineers to get started using observability.

AWS is the leading provider of cloud computing, delivering over 200 fully featured services to organizations worldwide. Its offerings cover everything from infrastructure—such as compute, storage, and networking—to advanced technologies like artificial intelligence, machine learning, and agentic AI. Businesses use AWS to modernize legacy systems, run high-performance workloads, and build scalable, secure applications. Core services like Amazon EC2, Amazon S3, and Amazon DynamoDB provide foundational capabilities, while advanced solutions like SageMaker and AWS Transform enable AI-driven transformation. The platform is supported by a global infrastructure that includes 38 regions, 120 availability zones, and 400+ edge locations, ensuring low latency and high reliability. AWS integrates with leading enterprise tools, developer SDKs, and partner ecosystems, giving teams the flexibility to adopt cloud at their own pace. Its training and certification programs help individuals and companies grow cloud expertise with industry-recognized credentials. With its unmatched breadth, depth, and proven track record, AWS empowers organizations to innovate and compete in the digital-first economy.

Amazon Bedrock is a comprehensive service that streamlines the development and expansion of generative AI applications by offering access to a diverse range of high-performance foundation models (FMs) from top AI organizations, including AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon. Utilizing a unified API, developers have the opportunity to explore these models, personalize them through methods such as fine-tuning and Retrieval Augmented Generation (RAG), and build agents that can engage with various enterprise systems and data sources. As a serverless solution, Amazon Bedrock removes the complexities associated with infrastructure management, enabling the effortless incorporation of generative AI functionalities into applications while prioritizing security, privacy, and ethical AI practices. This service empowers developers to innovate rapidly, ultimately enhancing the capabilities of their applications and fostering a more dynamic tech ecosystem.

Access and access management today have become more complex and frustrating. strongDM redesigns access around the people who need it, making it incredibly simple and usable while ensuring total security and compliance. We call it People-First Access. End users enjoy fast, intuitive, and auditable access to the resources they need. Administrators gain precise controls, eliminating unauthorized and excessive access permissions. IT, Security, DevOps, and Compliance teams can easily answer who did what, where, and when with comprehensive audit logs. It seamlessly and securely integrates with every environment and protocol your team needs, with responsive 24/7 support.

Amazon Elastic Compute Cloud (Amazon EC2) is a cloud service that offers flexible and secure computing capabilities. Its primary aim is to simplify large-scale cloud computing for developers. With an easy-to-use web service interface, Amazon EC2 allows users to quickly obtain and configure computing resources with ease. Users gain full control over their computing power while utilizing Amazon’s established computing framework. The service offers an extensive range of compute options, networking capabilities (up to 400 Gbps), and tailored storage solutions that enhance price and performance specifically for machine learning initiatives. Developers can create, test, and deploy macOS workloads on demand. Furthermore, users can scale their capacity dynamically as requirements change, all while benefiting from AWS's pay-as-you-go pricing model. This infrastructure enables rapid access to the necessary resources for high-performance computing (HPC) applications, resulting in enhanced speed and cost efficiency. In essence, Amazon EC2 ensures a secure, dependable, and high-performance computing environment that caters to the diverse demands of modern businesses. Overall, it stands out as a versatile solution for various computing needs across different industries.

The Domino Enterprise MLOps Platform helps data science teams improve the speed, quality, and impact of data science at scale. Domino is open and flexible, empowering professional data scientists to use their preferred tools and infrastructure. Data science models get into production fast and are kept operating at peak performance with integrated workflows. Domino also delivers the security, governance and compliance that enterprises expect. The Self-Service Infrastructure Portal makes data science teams become more productive with easy access to their preferred tools, scalable compute, and diverse data sets. By automating time-consuming and tedious DevOps tasks, data scientists can focus on the tasks at hand. The Integrated Model Factory includes a workbench, model and app deployment, and integrated monitoring to rapidly experiment, deploy the best models in production, ensure optimal performance, and collaborate across the end-to-end data science lifecycle. The System of Record has a powerful reproducibility engine, search and knowledge management, and integrated project management. Teams can easily find, reuse, reproduce, and build on any data science work to amplify innovation.

Dataiku serves as a sophisticated platform for data science and machine learning, aimed at facilitating teams in the construction, deployment, and management of AI and analytics projects on a large scale. It enables a diverse range of users, including data scientists and business analysts, to work together in developing data pipelines, crafting machine learning models, and preparing data through various visual and coding interfaces. Supporting the complete AI lifecycle, Dataiku provides essential tools for data preparation, model training, deployment, and ongoing monitoring of projects. Additionally, the platform incorporates integrations that enhance its capabilities, such as generative AI, thereby allowing organizations to innovate and implement AI solutions across various sectors. This adaptability positions Dataiku as a valuable asset for teams looking to harness the power of AI effectively.

There are countless devices operating in various environments such as residences, industrial sites, oil extraction facilities, medical centers, vehicles, and numerous other locations. As the number of these devices continues to rise, there is a growing demand for effective solutions that can connect them, as well as gather, store, and analyze the data they generate. AWS provides a comprehensive suite of IoT services that span from edge computing to cloud-based solutions. Unique among cloud providers, AWS IoT integrates data management with advanced analytics capabilities tailored to handle the complexities of IoT data seamlessly. The platform includes robust security features at every level, offering preventive measures like encryption and access control to safeguard device data, along with ongoing monitoring and auditing of configurations. By merging AI with IoT, AWS enhances the intelligence of devices, allowing users to build models in the cloud and deploy them to devices where they operate twice as efficiently as comparable solutions. Additionally, you can streamline operations by easily creating digital twins that mirror real-world systems and conduct analytics on large volumes of IoT data without the need to construct a dedicated analytics infrastructure. This means businesses can focus more on leveraging insights rather than getting bogged down in technical complexities.

PromptX offers a unified enterprise knowledge system that consolidates data from SharePoint, Google Drive, email, cloud storage, and legacy systems into one accessible platform. Leveraging AI-powered search capabilities, users can ask natural, conversational questions and instantly get context-rich, verifiable answers that drive faster decision-making. The system auto-ingests diverse unstructured data—including files, emails, and URLs—and converts them into Knowledge Cards through advanced semantic tagging and entity recognition. PromptX also includes adaptive prompts, split-chat pathways, collaborative workspaces, and intelligent agent automations that streamline complex workflows. Designed for deployment across any cloud or hybrid infrastructure, PromptX easily integrates with a variety of large language models (LLMs) and external search engines to extend its functionality. The platform scales effortlessly for enterprises of any size while maintaining stringent security measures such as granular permissions, single sign-on (SSO), audit trails, and AI governance. Its flexible architecture supports robust data management and compliance requirements. Overall, PromptX empowers organizations to harness their collective knowledge efficiently and securely.

Amazon Redshift is the preferred choice among customers for cloud data warehousing, outpacing all competitors in popularity. It supports analytical tasks for a diverse range of organizations, from Fortune 500 companies to emerging startups, facilitating their evolution into large-scale enterprises, as evidenced by Lyft's growth. No other data warehouse simplifies the process of extracting insights from extensive datasets as effectively as Redshift. Users can perform queries on vast amounts of structured and semi-structured data across their operational databases, data lakes, and the data warehouse using standard SQL queries. Moreover, Redshift allows for the seamless saving of query results back to S3 data lakes in open formats like Apache Parquet, enabling further analysis through various analytics services, including Amazon EMR, Amazon Athena, and Amazon SageMaker. Recognized as the fastest cloud data warehouse globally, Redshift continues to enhance its performance year after year. For workloads that demand high performance, the new RA3 instances provide up to three times the performance compared to any other cloud data warehouse available today, ensuring businesses can operate at peak efficiency. This combination of speed and user-friendly features makes Redshift a compelling choice for organizations of all sizes.

One tool can manage your entire data labeling workflow. We invite you to discover the best way to manage your labeling staff, improve data quality, work 70% faster, and get organized!

AWS Step Functions serves as a serverless orchestrator, simplifying the process of arranging AWS Lambda functions alongside various AWS services to develop essential business applications. It features a visual interface that allows users to design and execute a series of event-driven workflows with checkpoints, ensuring that the application state is preserved throughout. The subsequent step in the workflow utilizes the output from the previous one, creating a seamless flow dictated by the specified business logic. As each component of your application is executed in the designated order, the orchestration of distinct serverless applications can present challenges, especially with tasks like managing retries and troubleshooting issues. The increasing complexity of distributed applications demands effective management strategies, which can be daunting. However, Step Functions alleviates much of this operational strain through integrated controls that handle sequencing, error management, retry mechanisms, and state maintenance. This functionality allows teams to focus more on innovation rather than the intricacies of application management. Ultimately, AWS Step Functions empowers users to translate business needs into technical solutions rapidly by providing intuitive visual workflows for streamlined development.

You can develop on your laptop, then scale the same Python code elastically across hundreds or GPUs on any cloud. Ray converts existing Python concepts into the distributed setting, so any serial application can be easily parallelized with little code changes. With a strong ecosystem distributed libraries, scale compute-heavy machine learning workloads such as model serving, deep learning, and hyperparameter tuning. Scale existing workloads (e.g. Pytorch on Ray is easy to scale by using integrations. Ray Tune and Ray Serve native Ray libraries make it easier to scale the most complex machine learning workloads like hyperparameter tuning, deep learning models training, reinforcement learning, and training deep learning models. In just 10 lines of code, you can get started with distributed hyperparameter tune. Creating distributed apps is hard. Ray is an expert in distributed execution.

Union.ai Benefits: - Accelerated Data Processing & ML: Union.ai significantly speeds up data processing and machine learning. - Built on Trusted Open-Source: Leverages the robust open-source project Flyte™, ensuring a reliable and tested foundation for your ML projects. - Kubernetes Efficiency: Harnesses the power and efficiency of Kubernetes along with enhanced observability and enterprise features. - Optimized Infrastructure: Facilitates easier collaboration among Data and ML teams on optimized infrastructures, boosting project velocity. - Breaks Down Silos: Tackles the challenges of distributed tooling and infrastructure by simplifying work-sharing across teams and environments with reusable tasks, versioned workflows, and an extensible plugin system. - Seamless Multi-Cloud Operations: Navigate the complexities of on-prem, hybrid, or multi-cloud setups with ease, ensuring consistent data handling, secure networking, and smooth service integrations. - Cost Optimization: Keeps a tight rein on your compute costs, tracks usage, and optimizes resource allocation even across distributed providers and instances, ensuring cost-effectiveness.

Camunda helps organizations coordinate and automate processes involving people, systems, and devices—removing complexity, improving efficiency, and making AI workflows operational. Designed for both business and IT teams, Camunda’s platform runs any process with the speed and scale needed to stay competitive while meeting security and governance standards. More than 700 companies, including Atlassian, ING, and Vodafone, use Camunda to design, automate, and optimize core business processes. Learn more at camunda.com.

Amazon Transcribe simplifies the integration of speech-to-text features for developers looking to enhance their applications. Analyzing and searching audio data presents significant challenges for computers, making it essential to convert spoken words into written format for effective usage in various applications. Traditionally, businesses had to collaborate with transcription services that imposed costly contracts and were complicated to integrate with existing technology, making the transcription process cumbersome. Moreover, many of these services relied on outdated technologies that struggled to handle specific situations, such as the low-quality audio typical in contact center environments, leading to decreased accuracy. In contrast, Amazon Transcribe utilizes an advanced deep learning technique known as automatic speech recognition (ASR) to convert speech into text efficiently and with high precision. This service is versatile, allowing for the transcription of customer service interactions, the automation of subtitling, and the creation of metadata for media files, ultimately resulting in a comprehensive and searchable archive of content. With its user-friendly design and robust capabilities, Amazon Transcribe stands out as an essential tool for developers aiming to enhance the functionality of their applications.

Datalore is a platform for collaborative data science and analytics that aims to improve the entire analytics workflow and make working with data more enjoyable for both data scientists as well as data-savvy business teams. Datalore is a collaborative platform that focuses on data teams workflow. It offers technical-savvy business users the opportunity to work with data teams using no-code and low-code, as well as the power of Jupyter Notebooks. Datalore allows business users to perform analytic self-service. They can work with data using SQL or no-code cells, create reports, and dive deep into data. It allows core data teams to focus on simpler tasks. Datalore allows data scientists and analysts to share their results with ML Engineers. You can share your code with ML Engineers on powerful CPUs and GPUs, and you can collaborate with your colleagues in real time.

Create models at ten times the speed, link them directly to your data sources, and share insights through interactive dashboards with stunning visuals. Causal's formulas are designed to be straightforward—eliminating the need for complex cell references or cryptic syntax, and a single formula in Causal can replace dozens or even hundreds of traditional spreadsheet formulas. With the built-in scenario feature, you can effortlessly establish and analyze various what-if scenarios, utilizing ranges like "5 to 10" to grasp the complete spectrum of potential outcomes for your model. Startups leverage Causal for critical tasks such as calculating runway, monitoring key performance indicators, planning staff compensation, and crafting financial models that are ready for investors. Create eye-catching charts and tables without the hassle of lengthy customization processes. Additionally, you can seamlessly toggle between different time scales and summary formats to suit your analysis needs. Unleash the power of your data and transform the way you visualize your business metrics.

The NVIDIA Triton™ inference server provides efficient and scalable AI solutions for production environments. This open-source software simplifies the process of AI inference, allowing teams to deploy trained models from various frameworks, such as TensorFlow, NVIDIA TensorRT®, PyTorch, ONNX, XGBoost, Python, and more, across any infrastructure that relies on GPUs or CPUs, whether in the cloud, data center, or at the edge. By enabling concurrent model execution on GPUs, Triton enhances throughput and resource utilization, while also supporting inferencing on both x86 and ARM architectures. It comes equipped with advanced features such as dynamic batching, model analysis, ensemble modeling, and audio streaming capabilities. Additionally, Triton is designed to integrate seamlessly with Kubernetes, facilitating orchestration and scaling, while providing Prometheus metrics for effective monitoring and supporting live updates to models. This software is compatible with all major public cloud machine learning platforms and managed Kubernetes services, making it an essential tool for standardizing model deployment in production settings. Ultimately, Triton empowers developers to achieve high-performance inference while simplifying the overall deployment process.

Deploy your machine learning model in the cloud within minutes using a consolidated packaging format that supports both online and offline operations across various platforms. Experience a performance boost with throughput that is 100 times greater than traditional flask-based model servers, achieved through our innovative micro-batching technique. Provide exceptional prediction services that align seamlessly with DevOps practices and integrate effortlessly with widely-used infrastructure tools. The unified deployment format ensures high-performance model serving while incorporating best practices for DevOps. This service utilizes the BERT model, which has been trained with the TensorFlow framework to effectively gauge the sentiment of movie reviews. Our BentoML workflow eliminates the need for DevOps expertise, automating everything from prediction service registration to deployment and endpoint monitoring, all set up effortlessly for your team. This creates a robust environment for managing substantial ML workloads in production. Ensure that all models, deployments, and updates are easily accessible and maintain control over access through SSO, RBAC, client authentication, and detailed auditing logs, thereby enhancing both security and transparency within your operations. With these features, your machine learning deployment process becomes more efficient and manageable than ever before.

Flyte is a robust platform designed for automating intricate, mission-critical data and machine learning workflows at scale. It simplifies the creation of concurrent, scalable, and maintainable workflows, making it an essential tool for data processing and machine learning applications. Companies like Lyft, Spotify, and Freenome have adopted Flyte for their production needs. At Lyft, Flyte has been a cornerstone for model training and data processes for more than four years, establishing itself as the go-to platform for various teams including pricing, locations, ETA, mapping, and autonomous vehicles. Notably, Flyte oversees more than 10,000 unique workflows at Lyft alone, culminating in over 1,000,000 executions each month, along with 20 million tasks and 40 million container instances. Its reliability has been proven in high-demand environments such as those at Lyft and Spotify, among others. As an entirely open-source initiative licensed under Apache 2.0 and backed by the Linux Foundation, it is governed by a committee representing multiple industries. Although YAML configurations can introduce complexity and potential errors in machine learning and data workflows, Flyte aims to alleviate these challenges effectively. This makes Flyte not only a powerful tool but also a user-friendly option for teams looking to streamline their data operations.

Neptune.ai serves as a robust platform for machine learning operations (MLOps), aimed at simplifying the management of experiment tracking, organization, and sharing within the model-building process. It offers a thorough environment for data scientists and machine learning engineers to log data, visualize outcomes, and compare various model training sessions, datasets, hyperparameters, and performance metrics in real-time. Seamlessly integrating with widely-used machine learning libraries, Neptune.ai allows teams to effectively oversee both their research and production processes. Its features promote collaboration, version control, and reproducibility of experiments, ultimately boosting productivity and ensuring that machine learning initiatives are transparent and thoroughly documented throughout their entire lifecycle. This platform not only enhances team efficiency but also provides a structured approach to managing complex machine learning workflows.

JFrog ML (formerly Qwak) is a comprehensive MLOps platform that provides end-to-end management for building, training, and deploying AI models. The platform supports large-scale AI applications, including LLMs, and offers capabilities like automatic model retraining, real-time performance monitoring, and scalable deployment options. It also provides a centralized feature store for managing the entire feature lifecycle, as well as tools for ingesting, processing, and transforming data from multiple sources. JFrog ML is built to enable fast experimentation, collaboration, and deployment across various AI and ML use cases, making it an ideal platform for organizations looking to streamline their AI workflows.

AWS App Mesh is a service mesh designed to enhance application-level networking, enabling seamless communication among your services across diverse computing environments. It provides excellent visibility and ensures high availability for your applications. Typically, modern applications comprise several services, each capable of being developed on various compute platforms, including Amazon EC2, Amazon ECS, Amazon EKS, and AWS Fargate. As the complexity increases with more services being added, identifying error sources and managing traffic rerouting after issues become challenging, along with safely implementing code modifications. In the past, developers had to embed monitoring and control mechanisms within their code, necessitating a redeployment of services with each update. This reliance on manual intervention can lead to longer downtimes and increased potential for human error, but App Mesh alleviates these concerns by streamlining the process.

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.