ODSC - Open Data Science

FEBRUARY 2, 2023

Just as a writer needs to know core skills like sentence structure, grammar, and so on, data scientists at all levels should know core data science skills like programming, computer science, algorithms, and so on. This will lead to algorithm development for any machine or deep learning processes.

Data Science 98

Data Science Data Scientist Computer Science Computer Science 98

AWS Machine Learning Blog

JUNE 5, 2023

Data is the foundation for machine learning (ML) algorithms. One of the most common formats for storing large amounts of data is Apache Parquet due to its compact and highly efficient format. Athena allows applications to use standard SQL to query massive amounts of data on an S3 data lake.

Machine Learning 98

Machine Learning Machine Learning AWS Data Lakes 98

ODSC - Open Data Science

DECEMBER 19, 2023

Data Engineer Data engineers are responsible for the end-to-end process of collecting, storing, and processing data. They use their knowledge of data warehousing, data lakes, and big data technologies to build and maintain data pipelines.

Data Scientist 96

Data Scientist Machine Learning Machine Learning AI 96

AWS Machine Learning Blog

MAY 15, 2023

Getir used Amazon Forecast , a fully managed service that uses machine learning (ML) algorithms to deliver highly accurate time series forecasts, to increase revenue by four percent and reduce waste cost by 50 percent. Deep/neural network algorithms also perform very well on sparse data set and in cold-start (new item introduction) scenarios.

Algorithm 100

Algorithm Data Scientist Machine Learning Machine Learning 100

IBM Journey to AI blog

SEPTEMBER 19, 2023

Business users will also perform data analytics within business intelligence (BI) platforms for insight into current market conditions or probable decision-making outcomes. Many functions of data analytics—such as making predictions—are built on machine learning algorithms and models that are developed by data scientists.

Data Science 94

Data Science Analytics Analytics Data Scientist 94

Tableau

FEBRUARY 14, 2022

“I think one of the most important things I see people do right, is to make sure that you build the data foundation from the ground up correctly,” said Ali Ghodsi, CEO of Databricks. The data lakehouse is one such architecture—with “lake” from data lake and “house” from data warehouse.

AI 98

AI AI Tableau Data Scientist 98

Tableau

FEBRUARY 14, 2022

“I think one of the most important things I see people do right, is to make sure that you build the data foundation from the ground up correctly,” said Ali Ghodsi, CEO of Databricks. The data lakehouse is one such architecture—with “lake” from data lake and “house” from data warehouse.

AI 95

AI AI Tableau Data Scientist 95

AWS Machine Learning Blog

DECEMBER 18, 2023

By harnessing the transformative potential of MongoDB’s native time series data capabilities and integrating it with the power of Amazon SageMaker Canvas , organizations can overcome these challenges and unlock new levels of agility. As a Data Engineer he was involved in applying AI/ML to fraud detection and office automation.

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Clustering AWS Database ML 126

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. For example, neptune.ai

Machine Learning 134

Machine Learning Machine Learning ML ML 134

Alation

MAY 16, 2023

This makes it easier to compare and contrast information and provides organizations with a unified view of their data. Machine Learning Data pipelines feed all the necessary data into machine learning algorithms, thereby making this branch of Artificial Intelligence (AI) possible.

Data Pipeline 69

Data Pipeline Data Governance Data Lakes Data Warehouse 69

ODSC - Open Data Science

OCTOBER 20, 2023

HPCC Systems — The Kit and Kaboodle for Big Data and Data Science Bob Foreman | Software Engineering Lead | LexisNexis/HPCC Join this session to learn how ECL can help you create powerful data queries through a comprehensive and dedicated data lake platform. Check them out for free!

AI 52

AI AI Data Science Machine Learning 52

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.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

ODSC - Open Data Science

MARCH 11, 2024

Data Morph: A Cautionary Tale of Summary Statistics Visualization in Bayesian Workflow Using Python or R Harnessing Bayesian Statistics for Business-Centric Data Science Data Engineering and Big Data Join this track to learn the latest techniques and processes to analyze raw data and automate data into mechanical processes and algorithms.

Data Science 52

Data Science Deep Learning Deep Learning Machine Learning 52

ODSC - Open Data Science

APRIL 24, 2023

To cluster the data we have to calculate distances between IPs — The number of all possible IP pairs is very large, and we had to solve the scale problem. Data Processing and Clustering Our data is stored in a Data Lake and we used PrestoDB as a query engine.

Clustering 52

Clustering SQL Algorithm Data Science 52

Alation

JULY 19, 2022

For these reasons, finding and evaluating data is often time-consuming. Instead of spending most of their time leveraging their unique skillsets and algorithmic knowledge, data scientists are stuck sorting through data sets, trying to determine what’s trustworthy and how best to use that data for their own goals.

Data Scientist 59

Data Scientist Data Quality Data Science Data Analyst 59

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.

Azure 52

Azure Data Scientist Data Science Machine Learning 52

ODSC - Open Data Science

APRIL 3, 2023

Data analysts often must go out and find their data, process it, clean it, and get it ready for analysis. This pushes into Big Data as well, as many companies now have significant amounts of data and large data lakes that need analyzing.

Analytics 52

Analytics Analytics Data Analyst Data Science 52

ODSC - Open Data Science

OCTOBER 9, 2024

Tools like Harness and JenkinsX use machine learning algorithms to predict potential deployment failures, manage resource usage, and automate rollback procedures when something goes wrong. In the world of DevOps, AI can help monitor infrastructure, analyze logs, and detect performance bottlenecks in real-time. What should you be looking for?

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Apache Kafka AI AI Machine Learning 40

The MLOps Blog

MAY 17, 2023

This article explores the importance of ETL pipelines in machine learning, a hands-on example of building ETL pipelines with a popular tool, and suggests the best ways for data engineers to enhance and sustain their pipelines. We also need data profiling i.e. data discovery, to understand if the data is appropriate for ETL.

ETL 59

ETL Data Pipeline ML ML 59

Alation

MAY 16, 2023

This makes it easier to compare and contrast information and provides organizations with a unified view of their data. Machine Learning Data pipelines feed all the necessary data into machine learning algorithms, thereby making this branch of Artificial Intelligence (AI) possible.

Data Pipeline 40

Data Pipeline Data Governance Data Lakes Data Warehouse 40

Pickl AI

OCTOBER 10, 2024

Role of Data Transformation in Analytics, Machine Learning, and BI In Data Analytics, transformation helps prepare data for various operations, including filtering, sorting, and summarisation, making the data more accessible and useful for Analysts. Why Are Data Transformation Tools Important?

Data Quality 52

Data Quality AWS Machine Learning Machine Learning 52

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. Machine Learning Integration Opportunities Organizations harness machine learning (ML) algorithms to make forecasts on the data.

Data Warehouse 52

Data Warehouse Business Intelligence Business Intelligence Database 52

phData

MAY 6, 2024

Another benefit of deterministic matching is that the process to build these identities is relatively simple, and tools your teams might already use, like SQL and dbt , can efficiently manage this process within your cloud data warehouse. It thrives on patterns, combinations of data points, and statistical probabilities.

Data Lakes 52

Data Lakes Data Warehouse Cloud Data SQL 52

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.

SQL 52

SQL ML ML Python 52

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.

SQL 52

SQL ML ML Python 52

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

Machine Learning 69

Machine Learning Machine Learning Data Scientist ML 69

phData

SEPTEMBER 27, 2024

Let’s break down why this is so powerful for us marketers: Data Preservation : By keeping a copy of your raw customer data, you preserve the original context and granularity. Both persistent staging and data lakes involve storing large amounts of raw data. New user sign-up? Workout completed?

Data Modeling 52

Data Modeling Data Models Apache Kafka Data Lakes 52

AWS Machine Learning Blog

OCTOBER 24, 2024

An AI technique called embedding language models converts this external data into numerical representations and stores it in a vector database. RAG introduces additional data engineering requirements: Scalable retrieval indexes must ingest massive text corpora covering requisite knowledge domains.

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AWS Data Pipeline Database Big Data 120