AWS Machine Learning Blog

OCTOBER 2, 2024

In that post, you can learn more about the developmental lifecycle of a generative AI application and the additional skills, processes, and technologies needed to operationalize generative AI applications. AWS provides several services to support this; the following diagram illustrates these at a high level.

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AWS Machine Learning Blog

FEBRUARY 23, 2023

SageMaker has developed the distributed data parallel library , which splits data per node and optimizes the communication between the nodes. You can use the SageMaker Python SDK to trigger a job with data parallelism with minimal modifications to the training script.

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AWS Machine Learning Blog

DECEMBER 18, 2023

MongoDB Atlas offers automatic sharding, horizontal scalability, and flexible indexing for high-volume data ingestion. 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.

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ODSC - Open Data Science

FEBRUARY 25, 2025

Deep Learning & Multi-Modal Models TrackPush Neural NetworksFurther Dive into the latest advancements in neural networks, multimodal learning, and self-supervised models. This track provides practical guidance on building and optimizing deep learningsystems.

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The MLOps Blog

JUNE 27, 2023

Core features of end-to-end MLOps platforms End-to-end MLOps platforms combine a wide range of essential capabilities and tools, which should include: Data management and preprocessing : Provide capabilities for data ingestion, storage, and preprocessing, allowing you to efficiently manage and prepare data for training and evaluation.

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AWS Machine Learning Blog

JUNE 27, 2023

Earth.com’s leadership team recognized the vast potential of EarthSnap and set out to create an application that utilizes the latest deep learning (DL) architectures for computer vision (CV). EarthSnap was developed by Earth.com , a leading online platform for enthusiasts who are passionate about the environment, nature, and science.

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ODSC - Open Data Science

JANUARY 10, 2024

Over the course of this session, you will develop an understanding of no-code and low-code frameworks, how they are used in the ML workflow, how they can be used for data ingestion and analysis, and for building, training, and deploying ML models. Sign me up!

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AWS Machine Learning Blog

MAY 25, 2023

Amazon SageMaker Processing jobs for large scale data ingestion into OpenSearch. This notebook will ingest the SageMaker docs to an OpenSearch Service index called llm_apps_workshop_embeddings. This will download the dataset locally into the notebook and then ingest it into the OpenSearch Service index.

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John Snow Labs

MAY 31, 2023

The tool of choice for this project was NLP Lab for its powerful pre-annotation capabilities, allowing them to annotate the dataset efficiently and train a tailored deep learning model for automatic pre-annotation of new content in a couple of clicks.

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AWS Machine Learning Blog

JANUARY 6, 2023

Creates two indexes for text ( ooc_text ) and kNN embedding search ( ooc_knn ) and bulk uploads data from the combined dataframe through the ingest_data_into_ops function. This data ingestion process takes 5–10 minutes and can be monitored through the Amazon CloudWatch logs on the Monitoring tab of the Lambda function.

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AWS Machine Learning Blog

SEPTEMBER 11, 2024

Thirdly, the presence of GPUs enabled the labeled data to be processed. Together, these elements lead to the start of a period of dramatic progress in ML, with NN being redubbed deep learning. In order to train transformer models on internet-scale data, huge quantities of PBAs were needed.

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ODSC - Open Data Science

FEBRUARY 22, 2024

Stripling, PhD | Lead AI & ML Content Developer | Google Cloud In a no-code or low-code world you don’t have to have mastered coding to deploy machine learning models. Conclusion Can’t wait to start learning from these incredible speakers and experts?

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Mlearning.ai

AUGUST 28, 2023

I recently took the Azure Data Scientist Associate certification exam DP-100, thankfully I passed after about 3–4 months for studying the Microsoft Data Science Learning Path and the Coursera Microsoft Azure Data Scientist Associate Specialization.

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DataRobot Blog

SEPTEMBER 13, 2022

Once an organization has identified its AI use cases , data scientists informally explore methodologies and solutions relevant to the business’s needs in the hunt for proofs of concept. These might include—but are not limited to—deep learning, image recognition and natural language processing.

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The MLOps Blog

JANUARY 26, 2024

Recommended How to Solve the Data Ingestion and Feature Store Component of the MLOps Stack Read more A unified architecture for ML systems One of the challenges in building machine-learning systems is architecting the system. In Table 1 below, I’ve compiled a list of different ML systems that follow the unified architecture.

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AWS Machine Learning Blog

MARCH 29, 2023

Data ingestion HAYAT HOLDING has a state-of-the art infrastructure for acquiring, recording, analyzing, and processing measurement data. Two types of data sources exist for this use case. KEAS ranks first in the industry with its approximately 7,000 employees and exports to more than 100 countries.

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Pickl AI

DECEMBER 26, 2024

BERT and Sentence Transformers : These advanced models use Deep Learning and transformer architectures to generate context-aware embeddings, enabling nuanced understanding for tasks like semantic search and question answering.

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Pickl AI

SEPTEMBER 5, 2024

This service enables Data Scientists to query data on their terms using serverless or provisioned resources at scale. It also integrates deeply with Power BI and Azure Machine Learning, providing a seamless workflow from data ingestion to advanced analytics.

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Bugra Akyildiz

SEPTEMBER 22, 2024

Discretization methods: To apply SSMs to discrete-time data, various discretization methods are used, such as Zero-Order Hold (ZOH) or bilinear transformations. Normalization layers: Like many deep learning models, SSMs often incorporate normalization layers (e.g., LayerNorm) to stabilize training.

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AWS Machine Learning Blog

APRIL 5, 2023

In this phase, you submit a text search query or image search query through the deep learning model (CLIP) to encode as embeddings. The data ingestion for this practice should finish within 60 seconds. It also runs a simple query to verify if the data has been ingested into the index successfully.

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ODSC - Open Data Science

JULY 19, 2023

Personas associated with this phase may be primarily Infrastructure Team but may also include all of Data Engineers, Machine Learning Engineers, and Data Scientists. Model Development (Inner Loop): The inner loop element consists of your iterative data science workflow.

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Heartbeat

NOVEMBER 6, 2023

You have learned how to trigger a DAG in Airflow, create a DAG from scratch, and initiate its execution. In the upcoming part of this series, we will delve into advanced concepts of Airflow, including backfilling techniques and building an ETL pipeline in Airflow for data ingestion into Postgres and Google Cloud BigQuery.

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The MLOps Blog

MARCH 12, 2024

Related Deep Learning Model Optimization Methods Read more Example Scenario: Deploying customer service chatbot Imagine that you are in charge of implementing a LLM-powered chatbot for customer support. Develop the text preprocessing pipeline Data ingestion: Use Unstructured.io

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The MLOps Blog

AUGUST 11, 2023

1 Data Ingestion (e.g., Apache Kafka, Amazon Kinesis) 2 Data Preprocessing (e.g., Scikit-learn, Feature Tools) 4 Model Training (e.g., Scikit-learn, MLflow) 6 Model Deployment (e.g., Each of these architectural patterns plays a crucial role in enhancing the efficiency of machine learning pipelines.

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AWS Machine Learning Blog

OCTOBER 23, 2024

Data flow Here is an example of this data flow for an Agent Creator pipeline that involves data ingestion, preprocessing, and vectorization using Chunker and Embedding Snaps. He focuses on Deep learning including NLP and Computer Vision domains.

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AWS Machine Learning Blog

SEPTEMBER 18, 2024

Zeta’s AI innovations over the past few years span 30 pending and issued patents, primarily related to the application of deep learning and generative AI to marketing technology. Hosted on Amazon ECS with tasks run on Fargate, this platform streamlines the end-to-end ML workflow, from data ingestion to model deployment.

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AWS Machine Learning Blog

SEPTEMBER 19, 2024

This helps address the requirements of the generative AI fine-tuning lifecycle, from data ingestion and multi-node fine-tuning to inference and evaluation. Refer to the installation instructions and PyTorch documentation to learn more about torchtune and its concepts.

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TransOrg Analytics

SEPTEMBER 24, 2024

Google Data Studio: Turn data into informative dashboards and reports. Data & ML/LLM Ops on GCP Vertex AI: End-to-end platform to build, deploy, and scale ML models. TensorFlow Enterprise: High-performance deep learning on Google Cloud. Natural Language AI: Analyze and understand text data for LLM applications.

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TransOrg Analytics

SEPTEMBER 24, 2024

Google Data Studio: Turn data into informative dashboards and reports. Data & ML/LLM Ops on GCP Vertex AI: End-to-end platform to build, deploy, and scale ML models. TensorFlow Enterprise: High-performance deep learning on Google Cloud. Natural Language AI: Analyze and understand text data for LLM applications.

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The MLOps Blog

MARCH 21, 2023

Name Short Description Algorithmia Securely govern your machine learning operations with a healthy ML lifecycle. An end-to-end enterprise-grade platform for data scientists, data engineers, DevOps, and managers to manage the entire machine learning & deep learning product life-cycle. Allegro.io

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AWS Machine Learning Blog

NOVEMBER 7, 2024

Depending on the complexity of the problem and the structure of underlying data, the predictive models at Zalando range from simple statistical averages, over tree-based models to a Transformer-based deep learning architecture (Kunz et al. Deep Learning based Forecasting: a case study from the online fashion industry.”

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AWS Machine Learning Blog

OCTOBER 11, 2024

Data lineage and auditing – Metadata can provide information about the provenance and lineage of documents, such as the source system, data ingestion pipeline, or other transformations applied to the data. This information can be valuable for data governance, auditing, and compliance purposes.

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