Dataconomy

MARCH 19, 2025

Key disciplines involved in data science Understanding the core disciplines within data science provides a comprehensive perspective on the field’s multifaceted nature. Overview of core disciplines Data science encompasses several key disciplines including data engineering, data preparation, and predictive analytics.

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AWS Machine Learning Blog

AUGUST 15, 2024

With over 50 connectors, an intuitive Chat for data prep interface, and petabyte support, SageMaker Canvas provides a scalable, low-code/no-code (LCNC) ML solution for handling real-world, enterprise use cases. Organizations often struggle to extract meaningful insights and value from their ever-growing volume of data.

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Towards AI

NOVEMBER 8, 2024

Last Updated on November 9, 2024 by Editorial Team Author(s): Houssem Ben Braiek Originally published on Towards AI. Data preparation isn’t just a part of the ML engineering process — it’s the heart of it. Writing Output: Centralizing data into a structure, like a delta table. Published via Towards AI

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AWS Machine Learning Blog

OCTOBER 24, 2024

Conventional ML development cycles take weeks to many months and requires sparse data science understanding and ML development skills. Business analysts’ ideas to use ML models often sit in prolonged backlogs because of data engineering and data science team’s bandwidth and data preparation activities.

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Flipboard

DECEMBER 11, 2024

Organizations are building data-driven applications to guide business decisions, improve agility, and drive innovation. Many of these applications are complex to build because they require collaboration across teams and the integration of data, tools, and services. The following screenshot illustrates the SageMaker Unified Studio.

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Pickl AI

NOVEMBER 4, 2024

Summary: The fundamentals of Data Engineering encompass essential practices like data modelling, warehousing, pipelines, and integration. Understanding these concepts enables professionals to build robust systems that facilitate effective data management and insightful analysis. What is Data Engineering?

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Pickl AI

AUGUST 1, 2023

Aspiring and experienced Data Engineers alike can benefit from a curated list of books covering essential concepts and practical techniques. These 10 Best Data Engineering Books for beginners encompass a range of topics, from foundational principles to advanced data processing methods. What is Data Engineering?

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How to Learn Machine Learning

APRIL 26, 2025

The field of data science is now one of the most preferred and lucrative career options available in the area of data because of the increasing dependence on data for decision-making in businesses, which makes the demand for data science hires peak. Their insights must be in line with real-world goals.

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AWS Machine Learning Blog

DECEMBER 1, 2023

Additionally, these tools provide a comprehensive solution for faster workflows, enabling the following: Faster data preparation – SageMaker Canvas has over 300 built-in transformations and the ability to use natural language that can accelerate data preparation and making data ready for model building.

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Flipboard

JULY 7, 2025

Fine Tuning LLM Models – Generative AI Course When working with LLMs, you will often need to fine-tune LLMs, so consider learning efficient fine-tuning techniques such as LoRA and QLoRA, as well as model quantization techniques.

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Towards AI

AUGUST 16, 2023

Last Updated on August 17, 2023 by Editorial Team Author(s): Jeff Holmes MS MSCS Originally published on Towards AI. This article is intended as an outline of the key differences rather than a comprehensive discussion on the topic of the AI software process. MLOps is the intersection of Machine Learning, DevOps, and Data Engineering.

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Pickl AI

JUNE 8, 2025

Summary: In 2025, data science evolves with trends like augmented analytics, IoT data explosion, advanced machine learning, automation, and explainable AI. Key Takeaways Augmented analytics automates insights, making data accessible to non-experts.

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AWS Machine Learning Blog

JUNE 18, 2024

This post presents a solution that uses a generative artificial intelligence (AI) to standardize air quality data from low-cost sensors in Africa, specifically addressing the air quality data integration problem of low-cost sensors. Having a human-in-the-loop to validate each data transformation step is optional.

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AWS Machine Learning Blog

JANUARY 6, 2023

The recently published IDC MarketScape: Asia/Pacific (Excluding Japan) AI Life-Cycle Software Tools and Platforms 2022 Vendor Assessment positions AWS in the Leaders category. This was the first and only APEJ-specific analyst evaluation focused on AI life-cycle software from IDC. AWS position.

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Flipboard

NOVEMBER 24, 2023

Data scientists and ML engineers require capable tooling and sufficient compute for their work. To pave the way for the growth of AI, BMW Group needed to make a leap regarding scalability and elasticity while reducing operational overhead, software licensing, and hardware management.

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IBM Journey to AI blog

DECEMBER 7, 2023

Online analytical processing (OLAP) database systems and artificial intelligence (AI) complement each other and can help enhance data analysis and decision-making when used in tandem. As AI techniques continue to evolve, innovative applications in the OLAP domain are anticipated.

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AWS Machine Learning Blog

SEPTEMBER 1, 2023

Nowadays, the majority of our customers is excited about large language models (LLMs) and thinking how generative AI could transform their business. In this post, we discuss how to operationalize generative AI applications using MLOps principles leading to foundation model operations (FMOps).

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ODSC - Open Data Science

SEPTEMBER 25, 2023

Be sure to check out his talk, “ Using Graphs for Large Feature Engineering Pipelines ,” there to learn more about GraphReduce and more! For readers who work in ML/AI, it’s well understood that machine learning models prefer feature vectors of numerical information.

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Towards AI

JANUARY 28, 2024

Last Updated on January 29, 2024 by Editorial Team Author(s): Cassidy Hilton Originally published on Towards AI. Recapping the Cloud Amplifier and Snowflake Demo The combined power of Snowflake and Domo’s Cloud Amplifier is the best-kept secret in data management right now — and we’re reaching new heights every day.

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IBM Journey to AI blog

MARCH 14, 2024

Businesses face significant hurdles when preparing data for artificial intelligence (AI) applications. The existence of data silos and duplication, alongside apprehensions regarding data quality, presents a multifaceted environment for organizations to manage.

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ODSC - Open Data Science

FEBRUARY 5, 2025

This problem often stems from inadequate user value, underwhelming performance, and an absence of robust best practices for building and deploying LLM tools as part of the AI development lifecycle. Real-world applications often expose gaps that proper data preparation could have preempted. Evaluation: Tools likeNotion.

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Towards AI

MAY 8, 2024

Last Updated on May 9, 2024 by Editorial Team Author(s): Stephen Chege-Tierra Insights Originally published on Towards AI. How will this impact Google Earth Engine? Let us look at what is in this tool and how you can integrate Vertex with the Google Earth engine to analyse and deploy geospatial Earth data. What is Vertex?

Machine Learning 104

Machine Learning Machine Learning ML ML 104

Towards AI

AUGUST 16, 2023

Last Updated on August 17, 2023 by Editorial Team Author(s): Jeff Holmes MS MSCS Originally published on Towards AI. Thus, MLOps is the intersection of Machine Learning, DevOps, and Data Engineering (Figure 1). Hopefully, SEI or IEEE will soon publish an AI Engineering guide to standardize the terminology similar to SWEBOK.

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Machine Learning Machine Learning ML ML 98

Pickl AI

AUGUST 28, 2024

Summary: Vertex AI is a comprehensive platform that simplifies the entire Machine Learning lifecycle. From data preparation and model training to deployment and management, Vertex AI provides the tools and infrastructure needed to build intelligent applications.

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IBM Journey to AI blog

OCTOBER 20, 2023

AI platform tools enable knowledge workers to analyze data, formulate predictions and execute tasks with greater speed and precision than they can manually. AI plays a pivotal role as a catalyst in the new era of technological advancement. PwC calculates that “AI could contribute up to USD 15.7 trillion in value.

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AWS Machine Learning Blog

DECEMBER 1, 2023

Launched in 2019, Amazon SageMaker Studio provides one place for all end-to-end machine learning (ML) workflows, from data preparation, building and experimentation, training, hosting, and monitoring. About the Authors Mair Hasco is an AI/ML Specialist for Amazon SageMaker Studio. Get started on SageMaker Studio here.

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Dataconomy

JULY 28, 2023

These tools offer a wide range of functionalities to handle complex data preparation tasks efficiently. The tool also employs AI capabilities for automatically providing attribute names and short descriptions for reports, making it easy to use and efficient for data preparation.

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ODSC - Open Data Science

MARCH 13, 2023

First, there’s a need for preparing the data, aka data engineering basics. Machine learning practitioners are often working with data at the beginning and during the full stack of things, so they see a lot of workflow/pipeline development, data wrangling, and data preparation.

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IBM Journey to AI blog

JULY 17, 2023

is our enterprise-ready next-generation studio for AI builders, bringing together traditional machine learning (ML) and new generative AI capabilities powered by foundation models. With watsonx.ai, businesses can effectively train, validate, tune and deploy AI models with confidence and at scale across their enterprise.

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AWS Machine Learning Blog

APRIL 26, 2023

The AI Use Case Explorer is a good starting point to explore the right use cases by industry, business function, or desired business outcome and discover relevant customer success stories. Data engineering – Identifies the data sources, sets up data ingestion and pipelines, and prepares data using Data Wrangler.

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IBM Journey to AI blog

AUGUST 12, 2024

AIOPs refers to the application of artificial intelligence (AI) and machine learning (ML) techniques to enhance and automate various aspects of IT operations (ITOps). Consequently, AIOps is designed to harness data and insight generation capabilities to help organizations manage increasingly complex IT stacks.

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AWS Machine Learning Blog

JUNE 17, 2024

Being one of the largest AWS customers, Twilio engages with data and artificial intelligence and machine learning (AI/ML) services to run their daily workloads. The rise of generative AI increases the demand for training, deploying, and running ML models, and consequently, the use of data.

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AWS Machine Learning Blog

APRIL 3, 2024

SageMaker Studio provides all the tools you need to take your models from data preparation to experimentation to production while boosting your productivity. Amazon SageMaker Canvas is a powerful no-code ML tool designed for business and data teams to generate accurate predictions without writing code or having extensive ML experience.

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AWS Machine Learning Blog

MARCH 10, 2023

Starting today, you can connect to Amazon EMR Hive as a big data query engine to bring in large datasets for ML. Aggregating and preparing large amounts of data is a critical part of ML workflow. Solution overview With SageMaker Studio setups, data professionals can quickly identify and connect to existing EMR clusters.

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Clustering AWS ML ML 101

ODSC - Open Data Science

MAY 10, 2023

Unfortunately, even the data science industry — which should recognize tabular data’s true value — often underestimates its relevance in AI. Many mistakenly equate tabular data with business intelligence rather than AI, leading to a dismissive attitude toward its sophistication.

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Women in Big Data

APRIL 30, 2024

Members were encouraged to take advantage of the wide array of courses, and specialized training as well as the Associate and Professional Certifications, in Data Analysis, Data Science, and Data Engineering. Hoping to see more Women in Big Data projects as we continue learning from the DataCamp courses.

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AWS Machine Learning Blog

MAY 23, 2023

A recent PwC CEO survey unveiled that 84% of Canadian CEOs agree that artificial intelligence (AI) will significantly change their business within the next 5 years, making this technology more critical than ever. As such, an ML model is the product of an MLOps pipeline, and a pipeline is a workflow for creating one or more ML models.

Machine Learning 101

Machine Learning Machine Learning AWS ML 101