AWS Machine Learning

NOVEMBER 17, 2022

AWS IoT SiteWise is a managed service that can collect, organize, search, and consume equipment data from industrial equipment at scale. It should use serverless services where possible to reduce the infrastructure footprint. SageMaker is a fully-managed service that covers the entire ML workflow. Solution overview.

APIs 91

APIs Accountability Metrics Engineering 91

AWS Machine Learning

FEBRUARY 27, 2024

Our challenge To stay competitive in terms of cloud services and AI/ML, Axfood chose to partner with AWS and has been collaborating with them for many years. Automation of building new projects based on the template is streamlined through AWS Service Catalog , where a portfolio is created, serving as an abstraction for multiple products.

Best practices 110

Best practices Engineering Scripts Analytics 110

AWS Machine Learning

SEPTEMBER 1, 2023

After the completion of the research phase, the data scientists need to collaborate with ML engineers to create automations for building (ML pipelines) and deploying models into production using CI/CD pipelines. They orchestrate the deployment of the model as a service for use by consumers.

Engineering 100

Engineering Accountability Construction APIs 100

AWS Machine Learning

OCTOBER 4, 2022

SageMaker Pipelines is a fully managed service for automating the different steps of the ML workflow, including data loading, data transformation, model training and tuning, and model deployment. These templates are automatically available to your organization, and are provisioned using AWS Service Catalog products. Prerequisites.

Scripts 71

Scripts Engineering Management APIs 71

AWS Machine Learning

APRIL 19, 2023

Our original plan was to also host the models in Python and utilize gRPC to communicate with another service, which will use the Java gRPC client to send the request. Mainly, we saw the network overhead between two different services running in separate run environments or pods, which resulted in higher latency.

Engineering 74

Engineering APIs Scripts Education 74

AWS Machine Learning

OCTOBER 20, 2023

Customers of every size and industry are innovating on AWS by infusing machine learning (ML) into their products and services. Companies in regulated industries such as financial services, healthcare, chemistry, and the private sector. This framework considers multiple personas and services to govern the ML lifecycle at scale.

Government 99

Government Engineering Accountability Enterprise 99

AWS Machine Learning

OCTOBER 2, 2023

However, using purpose-built services like Amazon SageMaker and AWS IoT Greengrass allows you to significantly reduce this effort. At this stage, we aren’t looking at technologies or specific services yet, but rather the high-level components of our pipeline. Now it’s time to draft an architecture for our MLOps pipeline.

Engineering 89

Engineering APIs Management Accountability 89

AWS Machine Learning

JUNE 27, 2023

That is where Provectus , an AWS Premier Consulting Partner with competencies in Machine Learning, Data & Analytics, and DevOps, stepped in. The application’s workflows were automated by implementing end-to-end ML pipelines, which were delivered as part of Provectus’s managed MLOps platform and supported through managed AI services.

Engineering 93

Engineering APIs Scripts Entertainment 93

AWS Machine Learning

FEBRUARY 29, 2024

They offer a wide range of services, including public and regional transport, freight services, and rail infrastructure. The AI platform team’s key objective is to ensure seamless access to Workbench services and SageMaker Studio for all Deutsche Bahn teams and projects, with a primary focus on data scientists and ML engineers.

APIs 96

APIs Construction Transportation Analytics 96

AWS Machine Learning

APRIL 21, 2023

AmazonSageMaker-ExecutionPolicy- is the execution role associated with the SageMaker user and has the necessary Amazon Simple Storage Service (Amazon S3) bucket policies. To expand this solution, you could incorporate this into a custom SageMaker project that also incorporates CI/CD and automated triggers using standardized MLOps templates.

Metrics 73

Metrics Telecommunications Analytics Healthcare 73

AWS Machine Learning

AUGUST 29, 2023

In this post, we describe how to create an MLOps workflow for batch inference that automates job scheduling, model monitoring, retraining, and registration, as well as error handling and notification by using Amazon SageMaker , Amazon EventBridge , AWS Lambda , Amazon Simple Notification Service (Amazon SNS), HashiCorp Terraform, and GitLab CI/CD.

Scripts 72

Scripts APIs Enterprise Accountability 72

AWS Machine Learning

JUNE 24, 2022

To overcome this, enterprises needs to shape a clear operating model defining how multiple personas, such as data scientists, data engineers, ML engineers, IT, and business stakeholders, should collaborate and interact; how to separate the concerns, responsibilities, and skills; and how to use AWS services optimally. MLOps maturity model.

Enterprise 88

Enterprise Engineering Accountability APIs 88

AWS Machine Learning

OCTOBER 16, 2023

Veriff is an identity verification platform partner for innovative growth-driven organizations, including pioneers in financial services, FinTech, crypto, gaming, mobility, and online marketplaces. This approach was initially used for all company services, including microservices that run expensive computer vision ML models.

APIs 82

APIs Best practices Engineering Metrics 82

AWS Machine Learning

NOVEMBER 24, 2023

Today, the BMW Group is the world’s leading manufacturer of premium automobiles and motorcycles, and provider of premium financial and mobility services. In this post, we will talk about how BMW Group, in collaboration with AWS Professional Services, built its Jupyter Managed (JuMa) service to address these challenges.

Engineering 71

Engineering Analytics Government Self service 71

AWS Machine Learning

AUGUST 31, 2023

ML Shared Services Account – The MLOps set up from the Data Science account will trigger continuous integration and continuous delivery (CI/CD) pipelines using AWS CodeCommit and AWS CodePipeline. Solutions Architect at Amazon Web Services (AWS). His core area of focus includes Machine Learning, DevOps, and Containers.

Government 89

Government Accountability Enterprise Engineering 89

AWS Machine Learning

JUNE 14, 2023

Amazon SageMaker is a fully managed service to prepare data and build, train, and deploy machine learning (ML) models for any use case with fully managed infrastructure, tools, and workflows. We demonstrate how to use CI/CD the low-code/no-code tools code to integrate it into your MLOps environment, while adhering with MLOps best practices.

Best practices 76

Best practices Scripts Engineering Education 76

AWS Machine Learning

SEPTEMBER 9, 2022

athenahealth a leading provider of network-enabled software and services for medical groups and health systems nationwide. In the artificial intelligence (AI) space, athenahealth uses data science and machine learning (ML) to accelerate business processes and provide recommendations, predictions, and insights across multiple services.

Engineering 75

Engineering Healthcare APIs Scripts 75

AWS Machine Learning

MARCH 1, 2023

Solution overview Amazon Comprehend is a fully managed service that uses natural language processing (NLP) to extract insights about the content of documents. MLOps focuses on the intersection of data science and data engineering in combination with existing DevOps practices to streamline model delivery across the ML development lifecycle.

Engineering 83

Engineering Consulting Consulting APIs 83

AWS Machine Learning

AUGUST 16, 2023

The original concept came out of an AI/ML Hackathon supported by Simone Zucchet (AWS Solutions Architect) and Tim Precious (AWS Account Manager) and was developed into production using AWS services in under 6 weeks with support from AWS. Building the Open Arena Building the Open Arena was a multi-faceted process.

Enterprise 74

Enterprise APIs Engineering Entertainment 74

AWS Machine Learning

APRIL 26, 2023

You may have gaps in skills and technologies, including operationalizing ML solutions, implementing ML services, and managing ML projects for rapid iterations. Additionally, we provide recommendations on the technical path: starting with low-code or no-code services, or building a custom model using SageMaker.

Engineering 87

Engineering Government Accountability Technology 87

AWS Machine Learning

AUGUST 31, 2023

ML Shared Services Account – The MLOps set up from the Data Science account will trigger continuous integration and continuous delivery (CI/CD) pipelines using AWS CodeCommit and AWS CodePipeline. Solutions Architect at Amazon Web Services (AWS). His core area of focus includes Machine Learning, DevOps, and Containers.

Government 52

Government Accountability Enterprise Engineering 52

AWS Machine Learning

FEBRUARY 21, 2023

MLOps includes practices that integrate ML workloads into release management, CI/CD, and operations, accounting for the unique aspects of ML projects, including considerations for deploying and monitoring models. Similar to traditional CI/CD systems, we want to automate software tests, integration testing, and production deployments.

Best practices 70

Best practices Accountability Metrics Engineering 70

AWS Machine Learning

MARCH 7, 2023

You can implement comprehensive tests, governance, security guardrails, and CI/CD automation to produce custom app images. It abstracts the setup of your Docker build environments by automatically setting up the underlying services and workflow necessary for building Docker images.

Management 74

Management Scripts Government Engineering 74

AWS Machine Learning

JUNE 24, 2022

This transition from experiments in laboratories to solving real-world problems in production environments goes hand in hand with MLOps , or the adaptation of DevOps to the ML world. AWS Step Functions , a serverless low-code visual workflow service used to orchestrate and automate processes. Data consumption model.

APIs 63

APIs Engineering Accountability Metrics 63

SmartKarrot

APRIL 14, 2022

Some of the tools in a platform act like services, while others act like interfaces. An AI-driven interface connects with AI-based services to give a solution. This website monitoring service uses a series of specialized modules to fulfill its job. These tools are compatible and interact with each other. AppDynamics. DynaTrance.

SaaS 10

SaaS Enterprise Analytics Engineering 10

AWS Machine Learning

NOVEMBER 29, 2023

An evaluation is a task used to measure the quality and responsibility of output of an LLM or generative AI service. Model consumers or model deployers serve and monitor general purpose or fine-tuned models in production, aiming to enhance their applications or services through the adoption of LLMs.

Benchmark 81

Benchmark Metrics Engineering APIs 81

AWS Machine Learning

JANUARY 13, 2023

In 1992, Thomson Reuters (TR) released its first AI legal research service, WIN (Westlaw Is Natural), an innovation at the time, as most search engines only supported Boolean terms and connectors. With this tremendous increase of AI services, the next milestone for TR was to streamline innovation, and facilitate collaboration.

Government 74

Government Engineering Enterprise APIs 74