IBM Journey to AI blog

AUGUST 12, 2024

Whereas AIOps is a comprehensive discipline that includes a variety of analytics and AI initiatives that are aimed at optimizing IT operations, MLOps is specifically concerned with the operational aspects of ML models, promoting efficient deployment, monitoring and maintenance.

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Flipboard

NOVEMBER 24, 2023

This offering enables BMW ML engineers to perform code-centric data analytics and ML, increases developer productivity by providing self-service capability and infrastructure automation, and tightly integrates with BMW’s centralized IT tooling landscape.

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AWS Machine Learning Blog

AUGUST 21, 2024

In the following sections, we provide a detailed, step-by-step guide on implementing these new capabilities, covering everything from data preparation to job submission and output analysis. This use case serves to illustrate the broader potential of the feature for handling diverse data processing tasks.

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Ocean Protocol

NOVEMBER 28, 2024

This competition emphasized leveraging analytics in one of the world’s fastest and most data-intensive sports. Yunus focused on building a robust data pipeline, merging historical and current-season data to create a comprehensive dataset.

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AWS Machine Learning Blog

OCTOBER 18, 2023

The solution focuses on the fundamental principles of developing an AI/ML application workflow of data preparation, model training, model evaluation, and model monitoring. Rushikesh Jagtap is a Solutions Architect with 5+ years of experience in AWS Analytics services.

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Flipboard

AUGUST 17, 2023

AWS Glue is a serverless data integration service that makes it easy to discover, prepare, and combine data for analytics, ML, and application development. Above all, this solution offers you a native Spark way to implement an end-to-end data pipeline from Amazon Redshift to SageMaker.

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Pickl AI

NOVEMBER 4, 2024

Data Engineering is designing, constructing, and managing systems that enable data collection, storage, and analysis. It involves developing data pipelines that efficiently transport data from various sources to storage solutions and analytical tools. ETL is vital for ensuring data quality and integrity.

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AWS Machine Learning Blog

JUNE 18, 2024

If useful, it can be further extended to a data lake platform that uses AWS Glue (a serverless data integration service for data preparation) and Amazon Athena (a serverless and interactive analytics service) to analyze and visualize data.

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AWS Machine Learning Blog

MARCH 1, 2023

This post is co-written with Suhyoung Kim, General Manager at KakaoGames Data Analytics Lab. Continuous ML model retraining is one method to overcome this challenge by relearning from the most recent data. To solve this problem, we had to design a strong data pipeline to create the ML features from the raw data and MLOps.

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phData

AUGUST 2, 2024

Efficient data transformation and processing are crucial for data analytics and generating insights. Snowflake AI Data Cloud is one of the most powerful platforms, including storage services supporting complex data. Integrating Snowflake with dbt adds another layer of automation and control to the data pipeline.

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IBM Journey to AI blog

JULY 17, 2023

Visual modeling: Delivers easy-to-use workflows for data scientists to build data preparation and predictive machine learning pipelines that include text analytics, visualizations and a variety of modeling methods. ” Vitaly Tsivin, EVP Business Intelligence at AMC Networks.

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The MLOps Blog

JUNE 27, 2023

See also Thoughtworks’s guide to Evaluating MLOps Platforms End-to-end MLOps platforms End-to-end MLOps platforms provide a unified ecosystem that streamlines the entire ML workflow, from data preparation and model development to deployment and monitoring. Flyte Flyte is a platform for orchestrating ML pipelines at scale.

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AWS Machine Learning Blog

MARCH 8, 2023

In order to train a model using data stored outside of the three supported storage services, the data first needs to be ingested into one of these services (typically Amazon S3). This requires building a data pipeline (using tools such as Amazon SageMaker Data Wrangler ) to move data into Amazon S3.

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Pickl AI

OCTOBER 10, 2024

Summary: Data transformation tools streamline data processing by automating the conversion of raw data into usable formats. These tools enhance efficiency, improve data quality, and support Advanced Analytics like Machine Learning. BI tools rely on high-quality, consistent data to generate accurate insights.

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phData

DECEMBER 4, 2023

However, for analytics warehouses, you may need to scale for usage. Knowing this, you want to have data prepared in a way to optimize your load. Data Pipelines “Data pipeline” means moving data in a consistent, secure, and reliable way at some frequency that meets your requirements.

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Kaggle

JULY 29, 2020

With sports (and everything else) cancelled, this data scientist decided to take on COVID-19 | A Winner’s Interview with David Mezzetti When his hobbies went on hiatus, Kaggler David Mezzetti made fighting COVID-19 his mission. He previously co-founded and built Data Works into a 50+ person well-respected software services company.

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Pickl AI

OCTOBER 17, 2024

By following this structured approach, businesses can consolidate data from multiple origins, ensuring a unified view for analysis and reporting. The Role of ETL in Data Warehousing and Analytics ETL plays a pivotal role in data warehousing and analytics by facilitating the smooth movement of data across different systems.

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Pickl AI

SEPTEMBER 5, 2024

Introduction Data Science is revolutionising industries by extracting valuable insights from complex data sets, driving innovation, and enhancing decision-making. This roadmap aims to guide aspiring Azure Data Scientists through the essential steps to build a successful career.

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phData

SEPTEMBER 25, 2024

Under this category, tools with pre-built connectors for popular data sources and visual tools for data transformation are better choices. Integration: How well does the tool integrate with your existing infrastructure, databases, cloud platforms, and analytics tools? Another way is to add the Snowflake details through Fivetran.

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phData

FEBRUARY 7, 2024

Snowpark Use Cases Data Science Streamlining data preparation and pre-processing: Snowpark’s Python, Java, and Scala libraries allow data scientists to use familiar tools for wrangling and cleaning data directly within Snowflake, eliminating the need for separate ETL pipelines and reducing context switching.

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IBM Journey to AI blog

AUGUST 11, 2023

Because the machine learning lifecycle has many complex components that reach across multiple teams, it requires close-knit collaboration to ensure that hand-offs occur efficiently, from data preparation and model training to model deployment and monitoring. How to use ML to automate the refining process into a cyclical ML process.

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AWS Machine Learning Blog

SEPTEMBER 11, 2024

The financial services industry (FSI) is no exception to this, and is a well-established producer and consumer of data and analytics. These activities cover disparate fields such as basic data processing, analytics, and machine learning (ML). The union of advances in hardware and ML has led us to the current day.

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Snorkel AI

MARCH 2, 2023

Data preparation, train and tune, deploy and monitor. We have data pipelines and data preparation. In the data pipeline phase—I’m just going to call out things that I think are more important than the obvious. So the basic ones: you collect and validate and prepare data.

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Snorkel AI

MARCH 2, 2023

Data preparation, train and tune, deploy and monitor. We have data pipelines and data preparation. In the data pipeline phase—I’m just going to call out things that I think are more important than the obvious. So the basic ones: you collect and validate and prepare data.

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DagsHub

APRIL 21, 2024

A traditional machine learning (ML) pipeline is a collection of various stages that include data collection, data preparation, model training and evaluation, hyperparameter tuning (if needed), model deployment and scaling, monitoring, security and compliance, and CI/CD.

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phData

NOVEMBER 4, 2024

The modern data stack is defined by its ability to handle large datasets, support complex analytical workflows, and scale effortlessly as data and business needs grow. Two key technologies that have become foundational for this type of architecture are the Snowflake AI Data Cloud and Dataiku.

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AWS Machine Learning Blog

SEPTEMBER 18, 2024

ZOE is a multi-agent LLM application that integrates with multiple data sources to provide a unified view of the customer, simplify analytics queries, and facilitate marketing campaign creation. Additionally, Feast promotes feature reuse, so the time spent on data preparation is reduced greatly.

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DataRobot Blog

OCTOBER 3, 2017

Standard Chartered Bank’s Global Head of Technology, Santhosh Mahendiran , discussed the democratization of data across 3,500+ business users in 68 countries. We look at data as an asset, regardless of whether the use case is AML/fraud or new revenue. 3) Data professionals come in all shapes and forms.

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DataRobot

SEPTEMBER 14, 2021

We’re building a platform for all users: data scientists, analytics experts, business users, and IT. DataRobot now delivers both visual and code-centric data preparation and data pipelines, along with automated machine learning that is composable, and can be driven by hosted notebooks or a graphical user experience.

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Flipboard

MARCH 21, 2025

The ability for organizations to quickly analyze data across multiple sources is crucial for maintaining a competitive advantage. SageMaker Unified Studio provides a unified experience for using data, analytics, and AI capabilities. For the simplicity, we chose the SQL analytics project profile.

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AWS Machine Learning Blog

DECEMBER 18, 2024

This strategic decision was driven by several factors: Efficient data preparation Building a high-quality pre-training dataset is a complex task, involving assembling and preprocessing text data from various sources, including web sources and partner companies. The team opted for fine-tuning on AWS.

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Dataconomy

MARCH 19, 2025

Data science combines various disciplines to help businesses understand their operations, customers, and markets more effectively. What is data science? Data science is an interdisciplinary field that utilizes advanced analytics techniques to extract meaningful insights from vast amounts of data.

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