AWS Machine Learning Blog

APRIL 19, 2023

Since 2018, our team has been developing a variety of ML models to enable betting products for NFL and NCAA football. These models are then pushed to an Amazon Simple Storage Service (Amazon S3) bucket using DVC, a version control tool for ML models. Thirdly, there are improvements to demos and the extension for Spark.

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Iguazio

MAY 29, 2024

The 4 Gen AI Architecture Pipelines The four pipelines are: 1. The Data Pipeline The data pipeline is the foundation of any AI system. It's responsible for collecting and ingesting the data from various external sources, processing it and managing the data.

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Iguazio

JULY 22, 2024

MongoDB for end-to-end AI data management MongoDB Atlas , an integrated suite of data services centered around a multi-cloud NoSQL database, enables developers to unify operational, analytical, and AI data services to streamline building AI-enriched applications. Atlas Vector Search lets you search unstructured data.

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Iguazio

JANUARY 22, 2024

Demo: How to Build a Smart GenAI Call Center App How we used LLMs to turn call center conversation audio files of customers and agents into valuable data in a single workflow orchestrated by MLRun. For example, when indexing a new version of a document, it’s important to take care of versioning in the ML pipeline.

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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.

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The MLOps Blog

SEPTEMBER 7, 2023

This situation is not different in the ML world. Data Scientists and ML Engineers typically write lots and lots of code. Directives and architectural tricks for robust data pipelines Gain insights into an extensive array of directives and architectural strategies tailored for the development of highly dependable data pipelines.

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AWS Machine Learning Blog

DECEMBER 15, 2023

We are excited to announce the launch of Amazon DocumentDB (with MongoDB compatibility) integration with Amazon SageMaker Canvas , allowing Amazon DocumentDB customers to build and use generative AI and machine learning (ML) solutions without writing code. On the Import data page, for Data Source , choose DocumentDB and Add Connection.

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DataRobot Blog

SEPTEMBER 13, 2022

Data scientists drive business outcomes. Many implement machine learning and artificial intelligence to tackle challenges in the age of Big Data. They develop and continuously optimize AI/ML models , collaborating with stakeholders across the enterprise to inform decisions that drive strategic business value. Download Now.

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phData

JANUARY 31, 2024

In this blog, we’ll show you how to build a robust energy price forecasting solution within the Snowflake Data Cloud ecosystem. We’ll cover how to get the data via the Snowflake Marketplace, how to apply machine learning with Snowpark , and then bring it all together to create an automated ML model to forecast energy prices.

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phData

JULY 2, 2024

Familiar Client Side Libraries – Snowpark brings in-house and deeply integrated, DataFrame-style programming abstract and OSS-compatible APIs to the languages data practitioners like to use (Python, Scala, etc). It also includes the Snowpark ML API for more efficient machine language (ML) modeling and ML operations.

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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.

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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.

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phData

APRIL 5, 2024

By harnessing the advancements of LLMs, users can now extract key information buried within large documents without any code or ML knowledge required. Before we dive into the demo, the next section covers a couple of the key technologies that enable Document AI. Document AI is a new Snowflake tool that ingests documents (e.g.,

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Iguazio

FEBRUARY 8, 2024

They are characterized by their enormous size, complexity, and the vast amount of data they process. These elements need to be taken into consideration when managing, streamlining and deploying LLMs in ML pipelines, hence the specialized discipline of LLMOps. Continuous monitoring of resources, data, and metrics.

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Iguazio

FEBRUARY 20, 2024

Building MLOpsPedia This demo on Github shows how to fine tune an LLM domain expert and build an ML application Read More Building Gen AI for Production The ability to successfully scale and drive adoption of a generative AI application requires a comprehensive enterprise approach. Let’s dive into the data management pipeline.

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Iguazio

OCTOBER 20, 2024

In this blog post, we provide a staged approach for rolling out gen AI, together with use cases, a demo and examples that you can implement and follow. The webinar hosts Eli Stein, Partner and Modern Marketing Capabilities Leader from McKinsey, Ze’ev Rispler, ML Engineer, from Iguazio (acquired by McKinsey), and myself.

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DagsHub

MAY 7, 2024

Machine learning, particularly its subsets, deep learning, and generative ML, is currently in the spotlight. While many businesses believe that ML-driven automation is key to their success, a survey by McKinsey shows that only 15% of businesses’ ML projects ever succeed. What is Machine Learning Model Testing?

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DagsHub

MAY 7, 2024

Machine learning, particularly its subsets, deep learning, and generative ML, is currently in the spotlight. While many businesses believe that ML-driven automation is key to their success, a survey by McKinsey shows that only 15% of businesses’ ML projects ever succeed. What is Machine Learning Model Testing?

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DataRobot

SEPTEMBER 14, 2021

With Composable ML , expert data scientists can extend DataRobot’s AutoML blueprints with their domain knowledge and custom code. Composable ML turns DataRobot blueprints into reusable building blocks. Request a Demo. Jump-Start Your AI Journey Today. The post Advancing AI Cloud with Release 7.2

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Alation

APRIL 14, 2023

And there’s a growing consensus that dumping all data into the cloud data warehouse is too expensive to be sustainable – though presenters expressed doubt that this may be the case for just small use cases. So, how can a data catalog support the critical project of building data pipelines?

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Alation

FEBRUARY 20, 2020

While this year the BI Bake Off is designed for BI vendors, we wanted to show how the Alation Data Catalog can help make the analysis of this important dataset more effective and efficient. . Alation BI Bake Off Demo. With Alation, you can search for assets across the entire data pipeline.

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The MLOps Blog

MARCH 28, 2024

Strategies for improving GPU usage include mixed-precision training, optimizing data transfer and processing, and appropriately dividing workloads between CPU and GPU. GPU and CPU metrics can be monitored using an ML experiment tracker like Neptune, enabling teams to identify bottlenecks and systematically improve training performance.

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AWS Machine Learning Blog

DECEMBER 6, 2023

For this architecture, we propose an implementation on GitHub , with loosely coupled components where the backend (5), data pipelines (1, 2, 3) and front end (4) can evolve separately. Choose the link with the following format to open the demo: [link]. Inside the frontend/streamlit-ui folder, run bash run-streamlit-ui.sh.

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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.

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phData

JULY 5, 2024

This approach incorporates relevant data from a data store into prompts, providing large language models with additional context to help answer queries. Conclusion This blog has only covered the minimum technologies required to build the bare bones of a generative AI application.

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phData

JUNE 7, 2023

Data scientists use data-driven approaches to enable AI systems to make better predictions, optimize decision-making, and uncover hidden patterns that ultimately drive innovation and improve performance across various domains. It includes techniques like supervised, unsupervised, and reinforcement learning.

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PyImageSearch

DECEMBER 18, 2023

But again, stick around for a surprise demo at the end. ? This format made for a fast-paced and diverse showcase of ideas and applications in AI and ML. In just 3 minutes, each participant managed to highlight the core of their work, offering insights into the innovative ways in which AI and ML are being applied across various fields.

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BAIR

FEBRUARY 17, 2024

We frequently see this with LLM users, where a good LLM creates a compelling but frustratingly unreliable first demo, and engineering teams then go on to systematically raise quality. Optimization Often in ML, maximizing the quality of a compound system requires co-optimizing the components to work well together.

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BAIR

FEBRUARY 18, 2024

We frequently see this with LLM users, where a good LLM creates a compelling but frustratingly unreliable first demo, and engineering teams then go on to systematically raise quality. Optimization Often in ML, maximizing the quality of a compound system requires co-optimizing the components to work well together.

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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.

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ODSC - Open Data Science

SEPTEMBER 20, 2023

Tuesday is the first day of the AI Expo and Demo Hall , where you can connect with our conference partners and check out the latest developments and research from leading tech companies. Finally, get ready for some All Hallows Eve fun with Halloween Data After Dark , featuring a costume contest, candy, and more. What’s next?

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AWS Machine Learning Blog

OCTOBER 23, 2024

Boost productivity – Empowers knowledge workers with the ability to automatically and reliably summarize reports and articles, quickly find answers, and extract valuable insights from unstructured data. The following demo shows Agent Creator in action. He currently is working on Generative AI for data integration.

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Flipboard

NOVEMBER 24, 2023

Iris was designed to use machine learning (ML) algorithms to predict the next steps in building a data pipeline. Conclusion To get started today with SnapGPT, request a free trial of SnapLogic or request a demo of the product. He currently is working on Generative AI for data integration.

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Mlearning.ai

JUNE 16, 2023

GPT-4 Data Pipelines: Transform JSON to SQL Schema Instantly Blockstream’s public Bitcoin API. The data would be interesting to analyze. From Data Engineering to Prompt Engineering Prompt to do data analysis BI report generation/data analysis In BI/data analysis world, people usually need to query data (small/large).

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IBM Journey to AI blog

JULY 27, 2023

Request a demo to see how watsonx can put AI to work There’s no AI, without IA AI is only as good as the data that informs it, and the need for the right data foundation has never been greater. A data lakehouse with multiple query engines and storage can allow engineers to share data in open formats.

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Alation

APRIL 19, 2022

Access the resources your data applications need — no more, no less. Data Pipeline Automation. Consolidate all data sources to automate pipelines for processing in a single repository. For example, as you build out your data modernization initiative you need to consider: Before Migration: What data is popular?

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