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. This member-only story is on us. Upgrade to access all of Medium.

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Tableau

JUNE 11, 2024

Kristin Adderson June 11, 2024 - 4:53pm Noel Carter Senior Product Marketing Manager, Tableau Evan Slotnick Product Management Director, Tableau At the Tableau Conference 2024 keynote , Tableau CEO Ryan Aytay spoke about the new wave of analytics: the consumerization of data. June 18, 2024

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Snorkel AI

DECEMBER 2, 2024

At its core, Snorkel Flow empowers data scientists and domain experts to encode their knowledge into labeling functions, which are then used to generate high-quality training datasets. This approach not only enhances the efficiency of data preparation but also improves the accuracy and relevance of AI models.

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IBM Data Science in Practice

DECEMBER 7, 2022

Next Generation DataStage on Cloud Pak for Data Ensuring high-quality data A crucial aspect of downstream consumption is data quality. Studies have shown that 80% of time is spent on data preparation and cleansing, leaving only 20% of time for data analytics. This leaves more time for data analysis.

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Snorkel AI

NOVEMBER 9, 2023

LLM distillation will become a much more common and important practice for data science teams in 2024, according to a poll of attendees at Snorkel AI’s 2023 Enterprise LLM Virtual Summit. As data science teams reorient around the enduring value of small, deployable models, they’re also learning how LLMs can accelerate data labeling.

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Women in Big Data

JULY 19, 2024

The Women in Big Data (WiBD) Spring Hackathon 2024, organized by WiDS and led by WiBD’s Global Hackathon Director Rupa Gangatirkar , sponsored by Gilead Sciences, offered an exciting opportunity to sharpen data science skills while addressing critical social impact challenges.

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ODSC - Open Data Science

NOVEMBER 19, 2024

ODSC West 2024 showcased a wide range of talks and workshops from leading data science, AI, and machine learning experts. This blog highlights some of the most impactful AI slides from the world’s best data science instructors, focusing on cutting-edge advancements in AI, data modeling, and deployment strategies.

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AWS Machine Learning Blog

APRIL 30, 2025

Preparing your data Effective data preparation is crucial for successful distillation of agent function calling capabilities. Amazon Bedrock provides two primary methods for preparing your training data: uploading JSONL files to Amazon S3 or using historical invocation logs.

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Snorkel AI

NOVEMBER 9, 2023

LLM distillation will become a much more common and important practice for data science teams in 2024, according to a poll of attendees at Snorkel AI’s 2023 Enterprise LLM Virtual Summit. As data science teams reorient around the enduring value of small, deployable models, they’re also learning how LLMs can accelerate data labeling.

Data Science 52

Data Science Data Scientist Data Preparation AI 52

IBM Data Science in Practice

NOVEMBER 28, 2022

Ensuring high-quality data A crucial aspect of downstream consumption is data quality. Studies have shown that 80% of time is spent on data preparation and cleansing, leaving only 20% of time for data analytics. This leaves more time for data analysis. Let’s use address data as an example.

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Pickl AI

MARCH 4, 2024

Optimising Power BI reports for performance ensures efficient data analysis. Power BI proficiency opens doors to lucrative data analytics and business intelligence opportunities, driving organisational success in today’s data-driven landscape. How does Power Query help in data preparation?

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ODSC - Open Data Science

FEBRUARY 19, 2024

It has versatile data connectivity, real-time data exploration, and plenty of community support that helps users, new to veterans, unleash the program’s full potential. Most of these features also come with AI assistance to help users find the best way to visualize their data.

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DagsHub

JULY 25, 2024

Top 10 Deep Learning Platforms The top ten deep-learning platforms that will be driving the market in 2024 are examined in this section. Launched by Microsoft, Azure ML provides a comprehensive suite of tools and services to support the entire machine learning lifecycle, from data preparation to model deployment and management.

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ODSC - Open Data Science

FEBRUARY 12, 2024

You can even use generative AI to supplement your data sets with synthetic data for privacy or accuracy. Most businesses already recognize the need to automate the actual analysis of data, but you can go further. Automating the data preparation and interpretation phases will take much time and effort out of the equation, too.

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Iguazio

JUNE 9, 2025

In 2024, organizations are setting aside dedicated budgets for gen AI while ramping up their efforts to build accelerated infrastructure to support gen AI in production. It automates data preparation, model tuning, customization, validation and optimization of ML models, LLMs and live AI applications over elastic resources.

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ODSC - Open Data Science

JANUARY 29, 2024

Using skills such as statistical analysis and data visualization techniques, prompt engineers can assess the effectiveness of different prompts and understand patterns in the responses. This skill focuses on minimizing the resources and time required for an LLM to generate output based on your prompts.

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AWS Machine Learning Blog

DECEMBER 4, 2024

Start a distillation job with S3 JSONL data using an API To use an API to start a distillation job using training data stored in an S3 bucket, follow these steps: First, create and configure an Amazon Bedrock client: import boto3 from datetime import datetime bedrock_client = boto3.client(service_name="bedrock")

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Pickl AI

JUNE 18, 2024

It now allows users to clean, transform, and integrate data from various sources, streamlining the Data Analysis process. This eliminates the need to rely on separate tools for data preparation, saving time and resources.

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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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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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ODSC - Open Data Science

FEBRUARY 5, 2025

At ODSC Europe 2024 , Noe Achache, Engineering Manager & Generative AI Lead at Sicara, spoke about the performance challenges and outlined key lessons and best practices for creating successful, high-performing LLM-based solutions. Real-world applications often expose gaps that proper data preparation could have preempted.

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Towards AI

JULY 9, 2024

more work on custom LLM pipelines, niche models and frameworks (agents, data preparation, RAG, fine-tuning) and better foundational LLMs. We think the necessity for internal data and retrieval mechanisms in some form will always remain, and advanced custom LLM pipelines will continue to be essential.

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Towards AI

JULY 23, 2024

Continuing the 2024 trend of rapid LLM cost reduction, OpenAI’s GPT-4o mini averages about 140x cheaper than GPT-4 was at its release just 16 months ago while also performing better on most benchmarks. Why should you care?

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Towards AI

DECEMBER 19, 2024

Last Updated on December 20, 2024 by Editorial Team Author(s): Towards AI Editorial Team Originally published on Towards AI. Data preparation using Roboflow, model loading and configuration PaliGemma2 (including optional LoRA/QLoRA), and data loader creation are explained.

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PyImageSearch

OCTOBER 21, 2024

We will start by setting up libraries and data preparation. Setup and Data Preparation For this purpose, we will use the Pump Sensor Dataset , which contains readings of 52 sensors that capture various parameters (e.g., detection of potential failures or issues). temperature, pressure, vibration, etc.) What's next?

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Towards AI

JANUARY 12, 2024

Last Updated on January 12, 2024 by Editorial Team Author(s): Hang Yu Originally published on Towards AI. train_ratio = 0.9train_size = int(len(ratings)*train_ratio)ratings_train = ratings.sample(train_size, random_state=42)ratings_test = ratings[~ratings.index.isin(ratings_train.index)] Now, we have the data prepared.

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AWS Machine Learning Blog

FEBRUARY 18, 2025

The following sections further explain the main components of the solution: ETL pipelines to transform the log data, agentic RAG implementation, and the chat application. Creating ETL pipelines to transform log data Preparing your data to provide quality results is the first step in an AI project.

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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. Conclusion Vertex AI is a major improvement over Google Cloud’s machine learning and data science solutions.

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IBM Journey to AI blog

AUGUST 12, 2024

Wearable devices (such as fitness trackers, smart watches and smart rings) alone generated roughly 28 petabytes (28 billion megabytes) of data daily in 2020. And in 2024, global daily data generation surpassed 402 million terabytes (or 402 quintillion bytes). Massive, in fact.

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Towards AI

JUNE 25, 2024

Last Updated on June 25, 2024 by Editorial Team Author(s): Mena Wang, PhD Originally published on Towards AI. Image generated by Gemini Spark is an open-source distributed computing framework for high-speed data processing. This practice vastly enhances the speed of my data preparation for machine learning projects.

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Flipboard

MAY 29, 2025

This entails breaking down the large raw satellite imagery into equally-sized 256256 pixel chips (the size that the mode expects) and normalizing pixel values, among other data preparation steps required by the GeoFM that you choose. This routine can be conducted at scale using an Amazon SageMaker AI processing job.

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Tableau

MAY 3, 2022

Einstein Copilot for Tableau Einstein Copilot for Tableau superpowers analysts with a trusted AI assistant to help accelerate data-driven decision-making. Einstein Copilot for Tableau can also create visualizations from conversational prompts, and provide suggested questions to jumpstart data exploration. September 4, 2024

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AWS Machine Learning Blog

JUNE 5, 2025

The architecture incorporates best practices in MLOps, making sure that the different stages of the ML lifecyclefrom data preparation to production deploymentare optimized for performance and reliability. This new design accelerates model development and deployment, so Radial can respond faster to evolving fraud detection challenges.

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AWS Machine Learning Blog

NOVEMBER 22, 2023

In this code talk, learn how to prepare data at scale using built-in data preparation assistance, co-edit the same notebook in real time, and automate conversion of notebook code to production-ready jobs. You can also get behind the wheel yourself on November 30, when the track opens for the 2024 Open Racing.

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PyImageSearch

FEBRUARY 17, 2025

Table 1: Key Results from ViDoRe Benchmark (source: Emanuilov, 2024 ) What Is LLaVA? Instead of relying on static datasets, it uses GPT-4 to generate instruction-following data across diverse scenarios. LLaVA (Large Language and Vision Assistant) ( Liu et al., 2023 ) represents a significant leap forward in the multimodal AI landscape.

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Flipboard

DECEMBER 13, 2024

As of September 2024, the AI solution supports three core applications: Clearwater Intelligent Console (CWIC) Clearwaters customer-facing AI application. Data preparation Upload the assembled documents to an S3 bucket, making sure theyre in a format suitable for the fine-tuning process.

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PyImageSearch

SEPTEMBER 16, 2024

We will start by setting up libraries and data preparation. Setup and Data Preparation To start, we will first download the Credit Card Fraud Detection dataset, which contains details (e.g., Hence, we need robust and reliable fraud detection systems. for 3000+ credit card transactions. What's next? Kidriavsteva, and R.

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