ODSC - Open Data Science

APRIL 24, 2023

Botnet Detection at Scale — Lessons Learned From Clustering Billions of Web Attacks Into Botnets Editor’s note: Ori Nakar is a speaker for ODSC Europe this June. Be sure to check out his talk, “ Botnet detection at scale — Lesson learned from clustering billions of web attacks into botnets ,” there!

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Smart Data Collective

AUGUST 5, 2021

Collecting, processing, and carrying out analysis on streaming data , in industries such as ad-tech involves intense data engineering. The data generated daily is huge (100s of GB data) and requires a significant processing time to process the data for subsequent steps. What is Delta Lake?

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AWS Machine Learning Blog

DECEMBER 18, 2023

Prerequisites For this solution we use MongoDB Atlas to store time series data, Amazon SageMaker Canvas to train a model and produce forecasts, and Amazon S3 to store data extracted from MongoDB Atlas. The following screenshots shows the setup of the data federation. Setup the Database access and Network access.

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AWS Machine Learning Blog

JUNE 21, 2024

eSentire has over 2 TB of signal data stored in their Amazon Simple Storage Service (Amazon S3) data lake. This further step updates the FM by training with data labeled by security experts (such as Q&A pairs and investigation conclusions). They needed no additional infrastructure for data integration.

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AWS Machine Learning Blog

MAY 15, 2023

Algorithm Selection Amazon Forecast has six built-in algorithms ( ARIMA , ETS , NPTS , Prophet , DeepAR+ , CNN-QR ), which are clustered into two groups: statististical and deep/neural network. His team is responsible for designing, implementing, and maintaining end-to-end machine learning algorithms and data-driven solutions for Getir.

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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. so that organizations can focus on delivering value rather than be burdened by operational complexities.

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phData

DECEMBER 4, 2023

Thirty seconds is a good default for human users; if you find that queries are regularly queueing, consider making your warehouse a multi-cluster that scales on-demand. Cluster Count If your warehouse has to serve many concurrent requests, you may need to increase the cluster count to meet demand.

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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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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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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. This provides end-to-end support for data engineering and MLOps workflows.

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AWS Machine Learning Blog

FEBRUARY 7, 2025

Data Governance Account This account hosts data governance services for data lake, central feature store, and fine-grained data access. The SageMaker Project Portfolio has SageMaker projects that data scientists and ML engineers can use to accelerate model training and deployment.

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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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Snorkel AI

MAY 26, 2023

And that’s really key for taking data science experiments into production. The data scientists will start with experimentation, and then once they find some insights and the experiment is successful, then they hand over the baton to data engineers and ML engineers that help them put these models into production.

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Snorkel AI

MAY 26, 2023

And that’s really key for taking data science experiments into production. The data scientists will start with experimentation, and then once they find some insights and the experiment is successful, then they hand over the baton to data engineers and ML engineers that help them put these models into production.

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Flipboard

DECEMBER 4, 2024

However, building data-driven applications can be challenging. It often requires multiple teams working together and integrating various data sources, tools, and services. For example, creating a targeted marketing app involves data engineers, data scientists, and business analysts using different systems and tools.

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phData

NOVEMBER 9, 2023

A data warehouse is a centralized and structured storage system that enables organizations to efficiently store, manage, and analyze large volumes of data for business intelligence and reporting purposes. What is a Data Lake? What is the Difference Between a Data Lake and a Data Warehouse?

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phData

SEPTEMBER 20, 2023

phData has been working in data engineering since the inception of the company back in 2015. We have seen customers transform their data analytics with Snowflake and transform their data engineering and machine learning applications with Spark, Java, Scala, and Python.

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AWS Machine Learning Blog

AUGUST 15, 2024

Organizations often struggle to extract meaningful insights and value from their ever-growing volume of data. You need data engineering expertise and time to develop the proper scripts and pipelines to wrangle, clean, and transform data. Explore the future of no-code ML with SageMaker Canvas today.

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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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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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