Analytics, APIs, Big data and Scripts - Customer Contact Central

Analytics

APIs

Big data

Scripts

How Amp on Amazon used data to increase customer engagement, Part 1: Building a data analytics platform

AWS Machine Learning

SEPTEMBER 9, 2022

However, as a new product in a new space for Amazon, Amp needed more relevant data to inform their decision-making process. Part 1 shows how data was collected and processed using the data and analytics platform, and Part 2 shows how the data was used to create show recommendations using Amazon SageMaker , a fully managed ML service.

Analytics

Analytics Engineering APIs Metrics

Use RAG for drug discovery with Knowledge Bases for Amazon Bedrock

AWS Machine Learning

FEBRUARY 29, 2024

The Retrieve and RetrieveAndGenerate APIs allow your applications to directly query the index using a unified and standard syntax without having to learn separate APIs for each different vector database, reducing the need to write custom index queries against your vector store.

APIs

APIs Healthcare Scripts Enterprise

Join 52,000+

Insiders

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Trending Sources

Speed ML development using SageMaker Feature Store and Apache Iceberg offline store compaction

AWS Machine Learning

DECEMBER 21, 2022

Customers can also access offline store data using a Spark runtime and perform big data processing for ML feature analysis and feature engineering use cases. Table formats provide a way to abstract data files as a table. Apache Iceberg is an open table format for very large analytic datasets. AWS Glue Job setup.

Scripts

Scripts Engineering APIs Big data

Enable fully homomorphic encryption with Amazon SageMaker endpoints for secure, real-time inferencing

AWS Machine Learning

MARCH 23, 2023

Homomorphic encryption is a new approach to encryption that allows computations and analytical functions to be run on encrypted data, without first having to decrypt it, in order to preserve privacy in cases where you have a policy that states data should never be decrypted. resource("s3").Bucket Bucket (bucket).Object

Scripts

Scripts APIs Government Analytics

6 Online Data Analyst Courses

JivoChat

JANUARY 31, 2023

The Data Analyst Course With the Data Analyst Course, you will be able to become a professional in this area, developing all the necessary skills to succeed in your career. The course also teaches beginner and advanced Python, basics and advanced NumPy and Pandas, and data visualization. Workload: 20.5

Analytics

Analytics APIs Scripts Big data

Amazon SageMaker Feature Store now supports cross-account sharing, discovery, and access

AWS Machine Learning

FEBRUARY 13, 2024

For example, the analytics team may curate features like customer profile, transaction history, and product catalogs in a central management account. The second script accepts the AWS RAM invitations to discover and access cross-account feature groups from the owner level.

Accountability

Accountability Government Engineering Scripts

Reduce cost and development time with Amazon SageMaker Pipelines local mode

AWS Machine Learning

OCTOBER 4, 2022

Developers usually test their processing and training scripts locally, but the pipelines themselves are typically tested in the cloud. One of the main drivers for new innovations and applications in ML is the availability and amount of data along with cheaper compute options. Build your pipeline.

Scripts

Scripts Engineering Management APIs

Architect defense-in-depth security for generative AI applications using the OWASP Top 10 for LLMs

AWS Machine Learning

JANUARY 26, 2024

Define strict data ingress and egress rules to help protect against manipulation and exfiltration using VPCs with AWS Network Firewall policies. He is passionate about building secure and scalable AI/ML and big data solutions to help enterprise customers with their cloud adoption and optimization journey to improve their business outcomes.

Best practices

Best practices Government Engineering Accountability

Access Snowflake data using OAuth-based authentication in Amazon SageMaker Data Wrangler

AWS Machine Learning

MARCH 22, 2023

Snowflake is a cloud data platform that provides data solutions for data warehousing to data science. Snowflake is an AWS Partner with multiple AWS accreditations, including AWS competencies in machine learning (ML), retail, and data and analytics. Configure Snowflake. Configure SageMaker Studio.

APIs

APIs Engineering Analytics Scripts

Accelerating time-to-insight with MongoDB time series collections and Amazon SageMaker Canvas

AWS Machine Learning

DECEMBER 18, 2023

Among all, the native time series capabilities is a standout feature, making it ideal for a managing high volume of time-series data, such as business critical application data, telemetry, server logs and more. With efficient querying, aggregation, and analytics, businesses can extract valuable insights from time-stamped data.

Finance

Finance APIs Analytics Scripts

Developing advanced machine learning systems at Trumid with the Deep Graph Library for Knowledge Embedding

AWS Machine Learning

JULY 25, 2022

For production, we wanted to invoke the model as a simple API call. We found that we didn’t need to separate data preparation, model training, and prediction, and it was convenient to package the whole pipeline as a single script and use SageMaker processing.

Scripts

Scripts Engineering Analytics APIs

Four approaches to manage Python packages in Amazon SageMaker Studio notebooks

AWS Machine Learning

MARCH 7, 2023

When you open a notebook in Studio, you are prompted to set up your environment by choosing a SageMaker image, a kernel, an instance type, and, optionally, a lifecycle configuration script that runs on image startup. The main benefit is that a data scientist can choose which script to run to customize the container with new packages.

Management

Management Scripts Government Engineering

Organize your machine learning journey with Amazon SageMaker Experiments and Amazon SageMaker Pipelines

AWS Machine Learning

JULY 21, 2022

As a result, this experimentation phase can produce multiple models, each created from their own inputs (datasets, training scripts, and hyperparameters) and producing their own outputs (model artifacts and evaluation metrics). Analytics with SageMaker Experiments. A specific data science and ML project.

Metrics

Metrics Scripts Enterprise Telecommunications

Build repeatable, secure, and extensible end-to-end machine learning workflows using Kubeflow on AWS

AWS Machine Learning

SEPTEMBER 9, 2022

Prior to our adoption of Kubeflow on AWS, our data scientists used a standardized set of tools and a process that allowed flexibility in the technology and workflow used to train a given model. Each project maintained detailed documentation that outlined how each script was used to build the final model.

Engineering

Engineering Healthcare APIs Scripts

How Amp on Amazon used data to increase customer engagement, Part 1: Building a data analytics platform

Use RAG for drug discovery with Knowledge Bases for Amazon Bedrock

Trending Sources

­­Speed ML development using SageMaker Feature Store and Apache Iceberg offline store compaction

Enable fully homomorphic encryption with Amazon SageMaker endpoints for secure, real-time inferencing

6 Online Data Analyst Courses

Amazon SageMaker Feature Store now supports cross-account sharing, discovery, and access

Reduce cost and development time with Amazon SageMaker Pipelines local mode

Architect defense-in-depth security for generative AI applications using the OWASP Top 10 for LLMs

Access Snowflake data using OAuth-based authentication in Amazon SageMaker Data Wrangler

Accelerating time-to-insight with MongoDB time series collections and Amazon SageMaker Canvas

Developing advanced machine learning systems at Trumid with the Deep Graph Library for Knowledge Embedding

Four approaches to manage Python packages in Amazon SageMaker Studio notebooks

Organize your machine learning journey with Amazon SageMaker Experiments and Amazon SageMaker Pipelines

Build repeatable, secure, and extensible end-to-end machine learning workflows using Kubeflow on AWS

Stay Connected

Speed ML development using SageMaker Feature Store and Apache Iceberg offline store compaction