AWS Machine Learning Blog

SEPTEMBER 1, 2023

These teams are as follows: Advanced analytics team (data lake and data mesh) – Data engineers are responsible for preparing and ingesting data from multiple sources, building ETL (extract, transform, and load) pipelines to curate and catalog the data, and prepare the necessary historical data for the ML use cases.

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DagsHub

OCTOBER 23, 2024

To combine the collected data, you can integrate different data producers into a data lake as a repository. A central repository for unstructured data is beneficial for tasks like analytics and data virtualization. Data Cleaning The next step is to clean the data after ingesting it into the data lake.

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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. Check out the Kubeflow documentation. For example, neptune.ai

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IBM Journey to AI blog

OCTOBER 16, 2023

It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits. How to scale AL and ML with built-in governance A fit-for-purpose data store built on an open lakehouse architecture allows you to scale AI and ML while providing built-in governance tools.

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AWS Machine Learning Blog

DECEMBER 18, 2023

In this post, we will explore the potential of using MongoDB’s time series data and SageMaker Canvas as a comprehensive solution. MongoDB Atlas MongoDB Atlas is a fully managed developer data platform that simplifies the deployment and scaling of MongoDB databases in the cloud.

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IBM Journey to AI blog

JULY 27, 2023

Precisely conducted a study that found that within enterprises, data scientists spend 80% of their time cleaning, integrating and preparing data , dealing with many formats, including documents, images, and videos. Overall placing emphasis on establishing a trusted and integrated data platform for AI.

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Alation

JULY 19, 2022

For example, a new data scientist who is curious about which customers are most likely to be repeat buyers, might search for customer data only to discover an article documenting a previous project that answered their exact question. Modern data catalogs also facilitate data quality checks.

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IBM Journey to AI blog

JULY 17, 2023

These encoder-only architecture models are fast and effective for many enterprise NLP tasks, such as classifying customer feedback and extracting information from large documents. While they require task-specific labeled data for fine tuning, they also offer clients the best cost performance trade-off for non-generative use cases.

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phData

APRIL 2, 2024

By leveraging cloud-based data platforms such as Snowflake Data Cloud , these commercial banks can aggregate and curate their data to understand individual customer preferences and offer relevant and personalized products.

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Alation

MARCH 1, 2022

Through Impact Analysis, users can determine if a problem occurred with data upstream, and locate the impacted data downstream. With robust data lineage, data engineers can find and fix issues fast and prevent them from recurring. Similarly, analysts gain a clear view of how data is created.

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ODSC - Open Data Science

OCTOBER 3, 2024

Building an Effective OSS Management Layer for Your Data Lake Ahead of her ODSC West session on OSS management layers, the speaker discusses how data lakes can benefit from this system. When your data consists of various patient chart document types (e.g.

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Pickl AI

APRIL 9, 2025

This includes operations like data validation, data cleansing, data aggregation, and data normalization. The goal is to ensure that the data is consistent and ready for analysis. Loading : Storing the transformed data in a target system like a data warehouse, data lake, or even a database.

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phData

SEPTEMBER 20, 2023

A data mesh is a conceptual architectural approach for managing data in large organizations. Traditional data management approaches often involve centralizing data in a data warehouse or data lake, leading to challenges like data silos, data ownership issues, and data access and processing bottlenecks.

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AWS Machine Learning Blog

FEBRUARY 7, 2025

Data scientists Perform data analysis, model development, model evaluation, and registering the models in a model registry. ML engineers Develop model deployment pipelines and control the model deployment processes. The platform engineer shares the two Service Catalog portfolios with workload accounts in the organization.

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ODSC - Open Data Science

OCTOBER 15, 2023

Cloudera Cloudera is a cloud-based platform that provides businesses with the tools they need to manage and analyze data. They offer a variety of services, including data warehousing, data lakes, and machine learning. ArangoDB ArangoDB is a company that provides a database platform for graph and document data.

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Alation

APRIL 18, 2023

Data profiling helps organizations understand the data they possess with an eye to its quality level, which is vital for effective data governance. Modern data profiling will also gather all the potential problems in one quick scan. Do you need to define a data quality rule and add that to the profile?

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Alation

APRIL 6, 2023

Data governance is traditionally applied to structured data assets that are most often found in databases and information systems. This blog focuses on governing spreadsheets that contain data, information, and metadata, and must themselves be governed. There are others that consider spreadsheets to be trouble.

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Pickl AI

OCTOBER 10, 2024

Below, we explore five popular data transformation tools, providing an overview of their features, use cases, strengths, and limitations. Apache Nifi Apache Nifi is an open-source data integration tool that automates system data flow. Auditing helps track changes and maintain data integrity.

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Mlearning.ai

FEBRUARY 16, 2023

Thus, the solution allows for scaling data workloads independently from one another and seamlessly handling data warehousing, data lakes , data sharing, and engineering. Simplify and Win Experienced data engineers value simplicity. What will You Attain with Snowflake?

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Mlearning.ai

JULY 10, 2023

Without partitioning, daily data activities will cost your company a fortune and a moment will come where the cost advantage of GCP BigQuery becomes questionable. There are other options you can place, and as usual, I suggest you to reference the official documentation to learn more.

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Pickl AI

SEPTEMBER 5, 2024

Storage Solutions: Secure and scalable storage options like Azure Blob Storage and Azure Data Lake Storage. Key features and benefits of Azure for Data Science include: Scalability: Easily scale resources up or down based on demand, ideal for handling large datasets and complex computations.

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Alation

JULY 7, 2022

As Alation worked to create a new category of enterprise data management tool, the data catalog , Aaron wanted to also use this new technology to advance the cause of academic research. Aaron turned his attention from Alation Open to launch the Alation Data Catalog. programs in Information Science and Data Analytics.

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The MLOps Blog

MARCH 21, 2023

Other users Some other users you may encounter include: Data engineers , if the data platform is not particularly separate from the ML platform. Analytics engineers and data analysts , if you need to integrate third-party business intelligence tools and the data platform, is not separate. Allegro.io

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phData

FEBRUARY 14, 2023

Data Collector also offers replication and Change Data Capture (CDC) to be able to accurately and efficiently get your data into Snowflake. Data Collector can use Snowflake’s native Snowpipe in its pipelines. Why not just use one of the native ingestion methods for Snowflake? The biggest reason is the ease of use.

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phData

MAY 6, 2024

This means bringing together one or more of: Behavioral data like website visits, purchases, engagement with emails, and ads. Store this data in a customer data platform or data lake. Connect POS systems and CRM databases to your centralized data store.

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Alation

FEBRUARY 17, 2023

How do you get executives to understand the value of data governance? First, document your successes of good data, and how it happened. Share stories of data in good times and in bad (pictures help!). We’re planning data governance that’s primarily focused on compliance, data privacy, and protection.

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phData

DECEMBER 4, 2023

For greater detail, see the Snowflake documentation. If you answer “yes” to any of these questions, you will need cloud storage, such as Amazon AWS’s S3, Azure Data Lake Storage or GCP’s Google Storage. That’s why it’s so valuable to have experienced data engineers on your side, like the ones here at phData.

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The MLOps Blog

JUNE 5, 2023

When I was at Ford, we needed to hook things up to the car and telemetry it out and download all that data somewhere and make a data lake and hire a team of people to sort that data and make it usable; the blocker of doing any ML was changing cars and building data lakes and things like that.

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AWS Machine Learning Blog

JANUARY 26, 2024

Accelerate your security and AI/ML learning with best practices guidance, training, and certification AWS also curates recommendations from Best Practices for Security, Identity, & Compliance and AWS Security Documentation to help you identify ways to secure your training, development, testing, and operational environments.

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AWS Machine Learning Blog

OCTOBER 24, 2024

Large language models (LLMs) are very large deep-learning models that are pre-trained on vast amounts of data. One model can perform completely different tasks such as answering questions, summarizing documents, translating languages, and completing sentences. Data must be preprocessed to enable semantic search during inference.

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AWS Machine Learning Blog

NOVEMBER 1, 2024

Much of these greenhouse gas emissions can be attributed to travel (such as air travel, hotel, meetings), distribution associated for drugs and documents, and electricity used in coordination centers. Decentralized clinical trials, however, often employ a singular data lake for all of an organization’s clinical trials.

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