ODSC - Open Data Science

MAY 31, 2023

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. Editor’s note: Kai Waehner is a speaker for ODSC Europe this June.

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Data Lakes Machine Learning Machine Learning Apache Kafka 131

Pickl AI

DECEMBER 25, 2024

Business Analytics requires business acumen; Data Science demands technical expertise in coding and ML. Big data platforms such as Apache Hadoop and Spark help handle massive datasets efficiently. They must also stay updated on tools such as TensorFlow, Hadoop, and cloud-based platforms like AWS or Azure.

Data Science 52

Data Science Analytics Analytics Data Scientist 52

The MLOps Blog

JANUARY 23, 2023

Dolt LakeFS Delta Lake Pachyderm Git-like versioning Database tool Data lake Data pipelines Experiment tracking Integration with cloud platforms Integrations with ML tools Examples of data version control tools in ML DVC Data Version Control DVC is a version control system for data and machine learning teams. DVC Git LFS neptune.ai

ML 52

ML ML Data Lakes Machine Learning 52

ODSC - Open Data Science

JUNE 7, 2023

Microsoft’s Azure Data Lake The Azure Data Lake is considered to be a top-tier service in the data storage market. Amazon Web Services Similar to Azure, Amazon Simple Storage Service is an object storage service offering scalability, data availability, security, and performance. Delta & Databricks Make This A Reality!

Data Lakes 52

Data Lakes Azure Data Warehouse Hadoop 52

Pickl AI

NOVEMBER 28, 2024

Introduction Machine Learning ( ML ) is revolutionising industries, from healthcare and finance to retail and manufacturing. As businesses increasingly rely on ML to gain insights and improve decision-making, the demand for skilled professionals surges. This growth signifies Python’s increasing role in ML and related fields.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

O'Reilly Media

MAY 15, 2025

5:34 : You work with the folks at Azure, so presumably you know what actual enterprises are doing with generative AI. We have DeepSeek R1 available on Azure. 29:29 : Back then, we only had a few options: Hadoop, Spark. 30:03 : Back then people didnt need Hadoop or MapReduce or Spark if they didnt have lots of data.

AI 84

AI AI Hadoop Python 84

Pickl AI

JANUARY 13, 2025

Key Skills Experience with cloud platforms (AWS, Azure). Machine Learning (ML) Knowledge Understand various ML techniques, including supervised, unsupervised, and reinforcement learning. Hadoop , Apache Spark ) is beneficial for handling large datasets effectively. They ensure that AI systems are scalable and efficient.

AI 52

AI AI Machine Learning Machine Learning 52

Mlearning.ai

MAY 23, 2023

Check out this course to build your skillset in Seaborn —  [link] Big Data Technologies Familiarity with big data technologies like Apache Hadoop, Apache Spark, or distributed computing frameworks is becoming increasingly important as the volume and complexity of data continue to grow. in these fields.

Data Science 52

Data Science Data Scientist Machine Learning Machine Learning 52

DagsHub

DECEMBER 11, 2023

DVC tracks ML models and data sets (source: Iterative website ) Strengths Open source, and compatible with all major cloud platforms and storage types. LakeFS Most big data storage solutions such as Azure, Google cloud storage, and Amazon S3 have good performance, cost-effective, and have good connectivity with other tooling.

Machine Learning 98

Machine Learning Machine Learning Data Lakes Data Science 98

DagsHub

OCTOBER 23, 2024

Managing unstructured data is essential for the success of machine learning (ML) projects. This article will discuss managing unstructured data for AI and ML projects. You will learn the following: Why unstructured data management is necessary for AI and ML projects. How to properly manage unstructured data.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

IBM Journey to AI blog

JANUARY 18, 2023

They defined it as : “ A data lakehouse is a new, open data management architecture that combines the flexibility, cost-efficiency, and scale of data lakes with the data management and ACID transactions of data warehouses, enabling business intelligence (BI) and machine learning (ML) on all data. ”. Data fabric: A mostly new architecture.

Data Lakes 52

Data Lakes Data Warehouse Azure Apache Hadoop 52

Mlearning.ai

MARCH 15, 2023

Experience with cloud platforms like; AWS, AZURE, etc. Knowledge of big data platforms like; Hadoop and Apache Spark. Experience with machine learning frameworks for supervised and unsupervised learning. Experience with visualization tools like; Tableau and Power BI.

Data Science 52

Data Science Analytics Analytics Data Analyst 52

Pickl AI

DECEMBER 4, 2024

The rise of advanced technologies such as Artificial Intelligence (AI), Machine Learning (ML) , and Big Data analytics is reshaping industries and creating new opportunities for Data Scientists. Gain Experience with Big Data Technologies With the rise of Big Data, familiarity with technologies like Hadoop and Spark is essential.

Data Science 52

Data Science Data Scientist Machine Learning Machine Learning 52

ODSC - Open Data Science

JANUARY 7, 2025

As MLOps become more relevant to ML demand for strong software architecture skills will increase aswell. Machine Learning As machine learning is one of the most notable disciplines under data science, most employers are looking to build a team to work on ML fundamentals like algorithms, automation, and so on.

Data Scientist 40

Data Scientist Data Science Machine Learning Machine Learning 40

DagsHub

NOVEMBER 11, 2024

We use data-specific preprocessing and ML algorithms suited to each modality to filter out noise and inconsistencies in unstructured data. Embedding Generation: Bridging Data Types Embedding generation converts unstructured data into numerical vectors that ML models can understand. Tools like Unstructured.io

AI 52

AI AI Data Lakes Database 52

Heartbeat

JULY 31, 2023

Comet also integrates with popular data storage and processing tools like Amazon S3, Google Cloud Storage, and Hadoop. Comet also works with popular cloud platforms like AWS, GCP, and Azure, making it easy to deploy models to the cloud with just a few clicks. We pay our contributors, and we don’t sell ads.

Machine Learning 52

Machine Learning Machine Learning Deep Learning Deep Learning 52

How to Learn Machine Learning

MAY 3, 2025

All the clouds are different, and for us GCP offers some cool benefits that we will highlight in this article vs the AWS AI Services or Azure Machine Learning. Built on Google’s ML Infrastructure The same infrastructure that powers Google’s own AI applications is at your fingertips! What Exactly is GCP AI Platform?

Machine Learning 59

Machine Learning Machine Learning AI AI 59