Smart Data Collective

SEPTEMBER 16, 2020

However, many data scientists and business analysts can’t readily lean on automated regression techniques like logistic regression and linear regression. This stems, largely, from the fact that there are certain data regulations in place when it comes to marketing tech and predictive analytics software.

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AWS Machine Learning Blog

SEPTEMBER 18, 2024

It simplifies feature access for model training and inference, significantly reducing the time and complexity involved in managing data pipelines. Additionally, Feast promotes feature reuse, so the time spent on data preparation is reduced greatly. Saurabh Gupta is a Principal Engineer at Zeta Global.

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AWS Machine Learning Blog

AUGUST 21, 2023

Amazon SageMaker Data Wrangler reduces the time it takes to collect and prepare data for machine learning (ML) from weeks to minutes. We are happy to announce that SageMaker Data Wrangler now supports using Lake Formation with Amazon EMR to provide this fine-grained data access restriction.

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ODSC - Open Data Science

MAY 10, 2023

Data-centric AI, in his opinion, is based on the following principles: It’s time to focus on the data — after all the progress achieved in algorithms means it’s now time to spend more time on the data Inconsistent data labels are common since reasonable, well-trained people can see things differently. The choice is yours.

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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. Check out the Kubeflow documentation. Can you render audio/video?

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ODSC - Open Data Science

OCTOBER 7, 2024

In today’s landscape, AI is becoming a major focus in developing and deploying machine learning models. It isn’t just about writing code or creating algorithms — it requires robust pipelines that handle data, model training, deployment, and maintenance. Model Training: Running computations to learn from the data.

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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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Tableau

SEPTEMBER 23, 2021

At Tableau, we wanted to understand use cases and common issues from our most advanced data scientists to general data consumers. While not exhaustive, here are additional capabilities to consider as part of your data management and governance solution: Data preparation. Data modeling.

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Tableau

SEPTEMBER 23, 2021

At Tableau, we wanted to understand use cases and common issues from our most advanced data scientists to general data consumers. While not exhaustive, here are additional capabilities to consider as part of your data management and governance solution: Data preparation. Data modeling.

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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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Data Engineering Data Engineer Data Engineering Data Engineering 52

AWS Machine Learning Blog

JUNE 22, 2023

New machines are added continuously to the system, so we had to make sure our model can handle prediction on new machines that have never been seen in training. Data preprocessing and feature engineering In this section, we discuss our methods for data preparation and feature engineering.

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Iguazio

DECEMBER 14, 2023

Who This Book Is For This book is for practitioners in charge of building, managing, maintaining, and operationalizing the ML process end to end: Data science / AI / ML leaders: Heads of Data Science, VPs of Advanced Analytics, AI Lead etc. Exploratory data analysis (EDA) and modeling.

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Iguazio

FEBRUARY 8, 2024

Data engineers, data scientists and other data professional leaders have been racing to implement gen AI into their engineering efforts. Data Pipeline - Manages and processes various data sources. Application Pipeline - Manages requests and data/model validations. LLMOps is MLOps for LLMs.

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Pickl AI

APRIL 10, 2023

In case of professional Data Analysts, who might be engaged in performing experiments on data, standard SQL tools are required. Data Analysts need deeper knowledge on SQL to understand relational databases like Oracle, Microsoft SQL and MySQL. Moreover, SQL is an important tool for conducting Data Preparation and Data Wrangling.

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DagsHub

APRIL 21, 2024

You need to make that model available to the end users, monitor it, and retrain it for better performance if needed. This collaboration of ML and operations teams is what you call MLOps and focuses on streamlining the process of deploying the ML models to production, along with maintaining and monitoring them.

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AWS Machine Learning Blog

NOVEMBER 15, 2023

Understanding up front which preprocessing techniques and algorithm types provide best results reduces the time to develop, train, and deploy the right model. It plays a crucial role in every model’s development process and allows data scientists to focus on the most promising ML techniques.

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Algorithm AWS ML ML 126

Pickl AI

NOVEMBER 28, 2024

Model Evaluation and Tuning After building a Machine Learning model, it is crucial to evaluate its performance to ensure it generalises well to new, unseen data. Model evaluation and tuning involve several techniques to assess and optimise model accuracy and reliability.

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Heartbeat

NOVEMBER 28, 2023

However, as your model development process becomes more complex and involves numerous experiments and iterations, keeping track of your progress, managing experiments, and collaborating effectively with team members becomes increasingly challenging. First, ML models are becoming increasingly complex and require a lot of data to train.

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The MLOps Blog

NOVEMBER 30, 2022

It requires significant effort in terms of data preparation, exploration, processing, and experimentation, which involves trying out algorithms and hyperparameters. It is so because these algorithms have proven great results on a benchmark dataset, whereas your business problem and hence your data is different.

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The MLOps Blog

MAY 9, 2023

The platform typically includes components for the ML ecosystem like data management, feature stores, experiment trackers, a model registry, a testing environment, model serving, and model management. They include: 1 Data (or input) pipeline. 2 Model (or training) pipeline.

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AWS Machine Learning Blog

NOVEMBER 29, 2023

Data scientists can analyze detailed results with SageMaker Clarify visualizations in Notebooks, SageMaker Model Cards, and PDF reports. The following figure shows end-to-end LLMOps lifecycle: In LLMOps the main differences compared to MLOps are model selection and model evaluation involving different processes and metrics.

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Iguazio

MARCH 20, 2025

This includes responsible AI, Gartners concept of AI TRiSM (Trust, Risk and Security in AI Models) and Sovereign AI. AI engineering - AI is being democratized for developers and engineers, expanding beyond the limited pool of data scientists. AI Agents and multi-agent systems.

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