AWS Machine Learning Blog

FEBRUARY 19, 2024

RAG architecture involves two key workflows: data preprocessing through ingestion, and text generation using enhanced context. The data ingestion workflow uses LLMs to create embedding vectors that represent semantic meaning of texts. It offers fully managed data ingestion and text generation workflows.

Chatbots 112

Chatbots Data Ingestion Machine Learning Generative AI 112

Mlearning.ai

AUGUST 28, 2023

I highly recommend anyone coming from a Machine Learning or Deep Learning modeling background who wants to learn about deploying models (MLOps) on a cloud platform to take this exam or an equivalent; the exam also includes topics on SQL data ingestion with Azure and Databricks, which is also a very important skill to have in Data Science.

ML 52

ML Data Science Python Data Scientist 52

AWS Machine Learning Blog

FEBRUARY 7, 2025

This post, part of the Governing the ML lifecycle at scale series ( Part 1 , Part 2 , Part 3 ), explains how to set up and govern a multi-account ML platform that addresses these challenges. An enterprise might have the following roles involved in the ML lifecycles. This ML platform provides several key benefits.

ML 62

ML Data Scientist ML Engineer Data Science 62

AWS Machine Learning Blog

SEPTEMBER 18, 2023

Machine learning (ML) is becoming increasingly complex as customers try to solve more and more challenging problems. This complexity often leads to the need for distributed ML, where multiple machines are used to train a single model. SageMaker is a fully managed service for building, training, and deploying ML models.

Machine Learning 118

Machine Learning ML Python Auto-complete 118

AWS Machine Learning Blog

APRIL 4, 2023

AWS provides various services catered to time series data that are low code/no code, which both machine learning (ML) and non-ML practitioners can use for building ML solutions. We recommend running this notebook on Amazon SageMaker Studio , a web-based, integrated development environment (IDE) for ML.

Python 92

Python Machine Learning Explainability Data Ingestion 92

Towards AI

MARCH 28, 2024

This e-book focuses on adapting large language models (LLMs) to specific use cases by leveraging Prompt Engineering, Fine-Tuning, and Retrieval Augmented Generation (RAG), tailored for readers with an intermediate knowledge of Python. Elymsyr wants to develop new projects to improve their ML, RL, computer vision, and co-working skills.

Large Language Models 97

Large Language Models Prompt Engineering Prompt Engineer LLM 97

The MLOps Blog

JUNE 27, 2023

Alignment to other tools in the organization’s tech stack Consider how well the MLOps tool integrates with your existing tools and workflows, such as data sources, data engineering platforms, code repositories, CI/CD pipelines, monitoring systems, etc. and Pandas or Apache Spark DataFrames.

Machine Learning 134

Machine Learning Metadata Data Scientist Data Quality 134

AWS Machine Learning Blog

JANUARY 6, 2023

We downloaded the data from AWS Data Exchange and processed it in AWS Glue to generate KG files. In Part 2 , we demonstrated how to use Amazon Neptune ML (in Amazon SageMaker ) to train the KG and create KG embeddings. Initializes the OpenSearch Service client using the Boto3 Python library.

Metadata 94

Metadata Machine Learning Data Scientist ML 94

AWS Machine Learning Blog

JUNE 27, 2023

When machine learning (ML) models are deployed into production and employed to drive business decisions, the challenge often lies in the operation and management of multiple models. That is where Provectus , an AWS Premier Consulting Partner with competencies in Machine Learning, Data & Analytics, and DevOps, stepped in.

DevOps 109

DevOps ML Machine Learning ML Engineer 109

The MLOps Blog

JANUARY 26, 2024

Luckily, we have tried and trusted tools and architectural patterns that provide a blueprint for reliable ML systems. In this article, I’ll introduce you to a unified architecture for ML systems built around the idea of FTI pipelines and a feature store as the central component. But what is an ML pipeline?

Machine Learning 72

Machine Learning Metadata ML Python 72

ODSC - Open Data Science

MAY 31, 2023

Be sure to check out his talk, “ Apache Kafka for Real-Time Machine Learning Without a Data Lake ,” there! The combination of data streaming and machine learning (ML) enables you to build one scalable, reliable, but also simple infrastructure for all machine learning tasks using the Apache Kafka ecosystem.

Machine Learning 131

Machine Learning Data Science Data Ingestion Neural Network 131

AWS Machine Learning Blog

APRIL 5, 2023

Amazon SageMaker Serverless Inference is a purpose-built inference service that makes it easy to deploy and scale machine learning (ML) models. This includes preparing data, creating a SageMaker model, and performing batch transform using the model. You can use CLIP with Amazon SageMaker to perform encoding. path local_data_root = f'.

Metadata 101

Metadata Neural Network ML Python 101

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.

LLM 86

LLM Data Ingestion Python ML 86

AWS Machine Learning Blog

FEBRUARY 23, 2023

Identification of relevant representation data from a huge volume of data – This is essential to reduce biases in the datasets so that common scenarios (driving at normal speed with obstruction) don’t create class imbalance. To yield better accuracy, DNNs require large volumes of diverse, good quality data.

Automation 108

Automation Machine Learning Neural Network Data Scientist 108

ODSC - Open Data Science

FEBRUARY 22, 2024

Andre Franca | CTO | connectedFlow Join this session to demystify the world of Causal AI, with a focus on understanding cause-and-effect relationships within data to drive optimal decisions. By the end of this session, you’ll have a practical blueprint to efficiently harness feature stores within ML workflows.

Machine Learning 52

Machine Learning Data Science Python ML 52

AWS Machine Learning Blog

MAY 5, 2023

In this post, we discuss a machine learning (ML) solution for complex image searches using Amazon Kendra and Amazon Rekognition. Amazon Kendra is an intelligent search service powered by ML, and Amazon Rekognition is an ML service that can identify objects, people, text, scenes, and activities from images or videos.

Metadata 104

Metadata ETL ML Data Ingestion 104

AWS Machine Learning Blog

MAY 4, 2023

Simple methods for time series forecasting use historical values of the same variable whose future values need to be predicted, whereas more complex, machine learning (ML)-based methods use additional information, such as the time series data of related variables. All of this is possible without having to write or compile code.

Automation 113

Automation Metadata Data Ingestion Data Scientist 113

DataRobot Blog

JUNE 16, 2022

Airflow is a perfect tool to orchestrate stages of the DataRobot machine learning (ML) pipeline, because it provides an easy but powerful solution to integrate DataRobot capabilities into bigger pipelines, combine it with other services, as well as to clean your data, and store or publish the results. DataRobot Provider Modules.

Python 59

Python ML Machine Learning Data Ingestion 59

ODSC - Open Data Science

MARCH 24, 2023

Why the Best Time to Learn Python is Right Now In this blog, we will explore five reasons why Python has become the most popular programming language and why it is worth considering for your next project, regardless of your area of expertise.

Data Science 52

Data Science Machine Learning Data Ingestion NLP 52

AWS Machine Learning Blog

APRIL 8, 2024

Deploy with the SageMaker Python SDK You can use the SageMaker Python SDK to deploy the LLMs, as shown in the code available in the repository. Call the loader’s load_data method to parse your source files and data and convert them into LlamaIndex Document objects, ready for indexing and querying.

LLM 115

LLM Large Language Models Machine Learning Data Ingestion 115

Snorkel AI

JANUARY 24, 2023

Unfortunately accessing data across various locations and file types and then operationalizing that data for AI usage has traditionally been a painfully manual, time-consuming, and costly process. Ahmad Khan, Head of AI/ML Strategy at Snowflake, discusses the challenges of operationalizing ML in a recent talk.

Data Ingestion 52

Data Ingestion Machine Learning Data Science ML 52

Snorkel AI

JANUARY 24, 2023

Unfortunately accessing data across various locations and file types and then operationalizing that data for AI usage has traditionally been a painfully manual, time-consuming, and costly process. Ahmad Khan, Head of AI/ML Strategy at Snowflake, discusses the challenges of operationalizing ML in a recent talk.

Data Ingestion 52

Data Ingestion Machine Learning Data Science ML 52

AWS Machine Learning Blog

SEPTEMBER 11, 2024

These activities cover disparate fields such as basic data processing, analytics, and machine learning (ML). ML is often associated with PBAs, so we start this post with an illustrative figure. The ML paradigm is learning followed by inference. The union of advances in hardware and ML has led us to the current day.

ML 91

ML Deep Learning Algorithm Large Language Models 91

ODSC - Open Data Science

JANUARY 10, 2024

Andre Franca | CTO | connectedFlow Explore the world of Causal AI for data science practitioners, with a focus on understanding cause-and-effect relationships within data to drive optimal decisions. How do we figure out what is causal and what isn’t, with a brief introduction to methods of structure learning and causal discovery?

Large Language Models 52

Large Language Models Deep Learning Data Science LLM 52

ODSC - Open Data Science

JULY 19, 2023

Machine Learning Operations (MLOps) can significantly accelerate how data scientists and ML engineers meet organizational needs. A well-implemented MLOps process not only expedites the transition from testing to production but also offers ownership, lineage, and historical data about ML artifacts used within the team.

Machine Learning 52

Machine Learning Data Drift Data Science Data Scientist 52

The MLOps Blog

FEBRUARY 5, 2023

It provides a web-based interface for building data pipelines and can be used to process both batch and streaming data. Azure Stream Analytics : A cloud-based service that can be used to process streaming data in real-time. It provides a variety of features, such as data ingestion, data transformation, and real-time processing.

Machine Learning 72

Machine Learning Big Data Auto-complete Data Ingestion 72

Pickl AI

APRIL 6, 2023

Strong programming language skills in at least one of the languages like Python, Java, R, or Scala. Having a solid understanding of ML principles and practical knowledge of statistics, algorithms, and mathematics. Answer : Polybase helps optimize data ingestion into PDW and supports T-SQL. What is Polybase?

Big Data 52

Big Data ETL Data Ingestion Software Engineer 52

Mlearning.ai

MAY 15, 2023

Photo by Andrew Neel on Unsplash Introduction If you are working or have worked on any data science task then you definitely used pandas. So, pandas is a library which helps with performing data ingestion and transformations.

Data Ingestion 52

Data Ingestion Data Science LLM OpenAI 52

Mlearning.ai

FEBRUARY 22, 2023

pip install snowflake-connector-python import sys import boto3 import snowflake.connector region = 'us-west-2' # you need to know which region your Snowflake account is created from your admin We are going to use Python boto3 library to extract the user/password for the Snowflake connection.

Machine Learning 52

Machine Learning Python Data Ingestion ML 52

Heartbeat

NOVEMBER 6, 2023

In the image, you can see that the extract the weather data and extract metadata information about the location need to run in parallel. This is necessary because additional Python modules need to be installed. Similarly, if you need to complete a Python process, you will need the Python operator.

ETL 52

ETL Python Metadata Deep Learning 52

Pickl AI

JULY 8, 2024

Handling Missing Data: Imputing missing values or applying suitable techniques like mean substitution or predictive modelling. Tools such as Python’s Pandas library, Apache Spark, or specialised data cleaning software streamline these processes, ensuring data integrity before further transformation.

Data Quality 52

Data Quality ETL Data Integration Automation 52

The MLOps Blog

MARCH 15, 2023

Data pipeline stages But before delving deeper into the technical aspects of these tools, let’s quickly understand the core components of a data pipeline succinctly captured in the image below: Data pipeline stages | Source: Author What does a good data pipeline look like? Strong community and tech support.

ETL 59

ETL Categorization Data Integration Automation 59

Mlearning.ai

AUGUST 10, 2023

BigQuery is very useful in terms of having a centralized location of structured data; ingestion on GCP is wonderful using the ‘bq load’ command line tool for uploading local .csv PubSub and Dataflow are solutions for storing newly created data from website/application activity, in either BigQuery or Google Cloud Storage.

Auto-complete 52

Auto-complete Data Ingestion Explainability Python 52

The MLOps Blog

MAY 9, 2023

One of the most prevalent complaints we hear from ML engineers in the community is how costly and error-prone it is to manually go through the ML workflow of building and deploying models. Building end-to-end machine learning pipelines lets ML engineers build once, rerun, and reuse many times. If all goes well, of course ?

ML 98

ML Machine Learning Metadata Data Science 98

The MLOps Blog

JUNE 6, 2023

Complete ML model training pipeline workflow | Source But before we delve into the step-by-step model training pipeline, it’s essential to understand the basics, architecture, motivations, challenges associated with ML pipelines, and a few tools that you will need to work with.

ML 52

ML Machine Learning Auto-classification Auto-complete 52

AWS Machine Learning Blog

SEPTEMBER 18, 2024

The ZMP analyzes billions of structured and unstructured data points to predict consumer intent by using sophisticated artificial intelligence (AI) to personalize experiences at scale. Hosted on Amazon ECS with tasks run on Fargate, this platform streamlines the end-to-end ML workflow, from data ingestion to model deployment.

Machine Learning 109

Machine Learning Data Scientist ML Data Ingestion 109

The MLOps Blog

MARCH 21, 2023

From gathering and processing data to building models through experiments, deploying the best ones, and managing them at scale for continuous value in production—it’s a lot. As the number of ML-powered apps and services grows, it gets overwhelming for data scientists and ML engineers to build and deploy models at scale.

Machine Learning 69

Machine Learning Data Scientist ML Metadata 69