AWS Machine Learning Blog

SEPTEMBER 11, 2024

The process begins with data preparation, followed by model training and tuning, and then model deployment and management. Data preparation is essential for model training and is also the first phase in the MLOps lifecycle.

AWS 120

AWS Data Preparation ML ML 120

Pickl AI

JULY 2, 2024

Summary: Demystify time complexity, the secret weapon for Data Scientists. Explore practical examples, tools, and future trends to conquer big data challenges. Introduction to Time Complexity for Data Scientists Time complexity refers to how the execution time of an algorithm scales in relation to the size of the input data.

Data Scientist 52

Data Scientist Algorithm Data Science Machine Learning 52

DagsHub

FEBRUARY 29, 2024

Data, is therefore, essential to the quality and performance of machine learning models. This makes data preparation for machine learning all the more critical, so that the models generate reliable and accurate predictions and drive business value for the organization.

Data Preparation 52

Data Preparation Machine Learning Machine Learning Data Governance 52

AWS Machine Learning Blog

DECEMBER 24, 2024

Trainium chips are purpose-built for deep learning training of 100 billion and larger parameter models. Model training on Trainium is supported by the AWS Neuron SDK, which provides compiler, runtime, and profiling tools that unlock high-performance and cost-effective deep learning acceleration.

AWS 109

AWS Clustering Deep Learning Deep Learning 109

Smart Data Collective

SEPTEMBER 16, 2020

Deep Learning, Machine Learning, and Automation. However, many data scientists and business analysts can’t readily lean on automated regression techniques like logistic regression and linear regression. From a predictive analytics standpoint, you can be surer of its utility.

Predictive Analytics 139

Predictive Analytics Analytics Analytics Decision Trees 139

Heartbeat

FEBRUARY 1, 2023

It maintains your entire machine-learning model (from the creative processes to the execution). MLOps is a highly collaborative effort that aims to manipulate, automate, and generate knowledge through machine learning. First, we have data scientists who are in charge of creating and training machine learning models.

Machine Learning 96

Machine Learning Machine Learning Data Scientist ML 96

Flipboard

NOVEMBER 24, 2023

In an increasingly digital and rapidly changing world, BMW Group’s business and product development strategies rely heavily on data-driven decision-making. With that, the need for data scientists and machine learning (ML) engineers has grown significantly. JuMa automatically provisions a new AWS account for the workspace.

ML 146

ML ML AWS AI 146

AWS Machine Learning Blog

SEPTEMBER 12, 2023

The following diagram shows the workflow for our email classifier project, but can also be generalized to other data science projects. Model deployment – After making sure that everything is running as expected, data scientists merge the develop branch into the primary branch. A test endpoint is deployed for testing purposes.

Data Science 133

Data Science Data Scientist AWS ML 133

AWS Machine Learning Blog

JUNE 5, 2025

On the model training side, data scientists often face bottlenecks due to limited resources, forcing them to wait for infrastructure availability or reduce the scope of their experiments. Secure data management is enforced by isolating datasets within Amazon Simple Storage Service (Amazon S3) buckets.

Machine Learning 59

Machine Learning Machine Learning AWS ML 59

AWS Machine Learning Blog

SEPTEMBER 18, 2024

Zeta’s AI innovations over the past few years span 30 pending and issued patents, primarily related to the application of deep learning and generative AI to marketing technology. It simplifies feature access for model training and inference, significantly reducing the time and complexity involved in managing data pipelines.

AWS 126

AWS Machine Learning Machine Learning ML 126

ODSC - Open Data Science

MAY 1, 2023

However, a new paradigm has entered the chat, as LLMs don’t follow the same rules and expectations of traditional machine learning models. As such, data scientists need to find a different approach for using MLOps to find structure and create a sense of order as LLMs are developed.

Data Scientist 97

Data Scientist Data Science Supervised Learning Data Preparation 97

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.

Machine Learning 134

Machine Learning Machine Learning ML ML 134

ODSC - Open Data Science

MAY 10, 2023

Feature engineering activities frequently focus on single-table data transformations, leading to the infamous “yawn factor.” Let’s be honest — one-hot-encoding isn’t the most thrilling or challenging task on a data scientist’s to-do list. One might say that tabular data modeling is the original data-centric AI!

Data Scientist 87

Data Scientist Data Science Deep Learning Deep Learning 87

ODSC - Open Data Science

MARCH 18, 2025

Allen Downey, PhD, Principal Data Scientist at PyMCLabs Allen is the author of several booksincluding Think Python, Think Bayes, and Probably Overthinking Itand a blog about data science and Bayesian statistics. This years event is no different, and heres a rundown of 15 fan-favorite speakers who are returning onceagain.

Data Science 64

Data Science Machine Learning Machine Learning Data Scientist 64

AWS Machine Learning Blog

NOVEMBER 16, 2023

Amazon SageMaker provides purpose-built tools for machine learning operations (MLOps) to help automate and standardize processes across the ML lifecycle. Improve the quality and time to market for deep learning models in diagnostic medical imaging.

ML 130

ML ML AWS AI 130

IBM Journey to AI blog

MAY 9, 2023

After some impressive advances over the past decade, largely thanks to the techniques of Machine Learning (ML) and Deep Learning , the technology seems to have taken a sudden leap forward. It helps facilitate the entire data and AI lifecycle, from data preparation to model development, deployment and monitoring.

AI 110

AI AI Data Warehouse Machine Learning 110

AWS Machine Learning Blog

FEBRUARY 22, 2023

This post is co-written with Swagata Ashwani, Senior Data Scientist at Boomi. The exact steps to replicate this process are outlined Train and deploy deep learning models using JAX with Amazon SageMaker. Swagata Ashwani is a Senior Data Scientist at Boomi with over 6+ years experience in Data Science.

AWS 98

AWS ML ML Data Science 98

AWS Machine Learning Blog

SEPTEMBER 20, 2023

Key concepts Amazon SageMaker Studio is a web-based, integrated development environment (IDE) for machine learning. SageMaker Studio allows data scientists, ML engineers, and data engineers to prepare data, build, train, and deploy ML models on one web interface. dummy_input = torch.randn(1, 1, 28, 28).to(device)

ML 134

ML ML Azure AWS 134

AWS Machine Learning Blog

FEBRUARY 12, 2024

SageMaker pipeline steps The pipeline is divided into the following steps: Train and test data preparation – Terabytes of raw data are copied to an S3 bucket, processed using AWS Glue jobs for Spark processing, resulting in data structured and formatted for compatibility.

ML 137

ML ML AWS Machine Learning 137

AWS Machine Learning Blog

NOVEMBER 15, 2024

Data scientists and developers can quickly prototype and experiment with various ML use cases, accelerating the development and deployment of ML applications. SageMaker Studio is an IDE that offers a web-based visual interface for performing the ML development steps, from data preparation to model building, training, and deployment.

ML 112

ML ML Python AWS 112

AWS Machine Learning Blog

OCTOBER 19, 2023

Customers increasingly want to use deep learning approaches such as large language models (LLMs) to automate the extraction of data and insights. For many industries, data that is useful for machine learning (ML) may contain personally identifiable information (PII).

Machine Learning 124

Machine Learning Machine Learning ML ML 124

IBM Journey to AI blog

AUGUST 11, 2023

Because ML is becoming more integrated into daily business operations, data science teams are looking for faster, more efficient ways to manage ML initiatives, increase model accuracy and gain deeper insights. MLOps is the next evolution of data analysis and deep learning.

Data Science 68

Data Science Machine Learning Machine Learning ML 68

Towards AI

JUNE 27, 2023

RapidMiner RapidMiner, a renowned player in the realm of machine learning tools, offers an all-encompassing platform for a myriad of operations. Its functionalities span from deep learning to text mining, data preparation, and predictive analytics, ensuring a versatile utility for developers and data scientists alike.

Machine Learning 98

Machine Learning Machine Learning ML ML 98

ODSC - Open Data Science

APRIL 17, 2023

With all the talk about new AI-powered tools and programs feeding the imagination of the internet, we often forget that data scientists don’t always have to do everything 100% themselves. PyCaret allows data professionals to build and deploy machine learning models easily and efficiently.

Data Science 59

Data Science Machine Learning Machine Learning Deep Learning 59

Dataconomy

MARCH 27, 2023

RPA uses a graphical user interface (GUI) to interact with applications and websites, while ML uses algorithms and statistical models to analyze data. On the other hand, ML requires a significant amount of data preparation and model training before it can be deployed.

ML 133

ML ML Machine Learning Machine Learning 133

ODSC - Open Data Science

NOVEMBER 19, 2024

Here’s a breakdown of ten top sessions from this year’s conference that data professionals should consider. Topological Deep Learning Made Easy with TopoX with Dr. Mustafa Hajij Slides In these AI slides, Dr. Mustafa Hajij introduced TopoX, a comprehensive Python suite for topological deep learning.

Deep Learning 52

Deep Learning Deep Learning Data Science AI 52

ODSC - Open Data Science

OCTOBER 7, 2024

Understanding the MLOps Lifecycle The MLOps lifecycle consists of several critical stages, each with its unique challenges: Data Ingestion: Collecting data from various sources and ensuring it’s available for analysis. Data Preparation: Cleaning and transforming raw data to make it usable for machine learning.

Machine Learning 52

Machine Learning Machine Learning AI AI 52

IBM Journey to AI blog

OCTOBER 20, 2023

Artificial intelligence platforms enable individuals to create, evaluate, implement and update machine learning (ML) and deep learning models in a more scalable way. AI platform tools enable knowledge workers to analyze data, formulate predictions and execute tasks with greater speed and precision than they can manually.

AI 90

AI AI Machine Learning Machine Learning 90

AWS Machine Learning Blog

MARCH 29, 2023

Data ingestion HAYAT HOLDING has a state-of-the art infrastructure for acquiring, recording, analyzing, and processing measurement data. Model training and optimization with SageMaker automatic model tuning Prior to the model training, a set of data preparation activities are performed. Hayat” means “life” in Turkish.

ML 98

ML ML AWS Machine Learning 98

AWS Machine Learning Blog

SEPTEMBER 1, 2023

These data owners are focused on providing access to their data to multiple business units or teams. Data science team – Data scientists need to focus on creating the best model based on predefined key performance indicators (KPIs) working in notebooks. The following figure illustrates their journey.

AI 130

AI AI ML ML 130

AWS Machine Learning Blog

JANUARY 6, 2023

A DataBrew job extracts the data from the TR data warehouse for the users who are eligible to provide recommendations during renewal based on the current subscription plan and recent activity. Hesham Fahim is a Lead Machine Learning Engineer and Personalization Engine Architect at Thomson Reuters.

AWS 98

AWS Data Warehouse ML ML 98

Heartbeat

JUNE 5, 2023

Machine Learning Frameworks Comet integrates with a wide range of machine learning frameworks, making it easy for teams to track and optimize their models regardless of the framework they use. Ludwig Ludwig is a machine learning framework for building and training deep learning models without the need for writing code.

ML 59

ML ML Machine Learning Machine Learning 59

Heartbeat

MAY 29, 2023

In this article, we will explore the essential steps involved in training LLMs, including data preparation, model selection, hyperparameter tuning, and fine-tuning. We will also discuss best practices for training LLMs, such as using transfer learning, data augmentation, and ensembling methods.

Machine Learning 78

Machine Learning Machine Learning Natural Language Processing Data Preparation 78

Becoming Human

MAY 15, 2023

Note : Now, Start joining Data Science communities on social media platforms. These communities will help you to be updated in the field, because there are some experienced data scientists posting the stuff, or you can talk with them so they will also guide you in your journey.

Data Science 75

Data Science Machine Learning Machine Learning Database 75

Pickl AI

JULY 12, 2024

Summary: This guide explores Artificial Intelligence Using Python, from essential libraries like NumPy and Pandas to advanced techniques in machine learning and deep learning. TensorFlow and Keras: TensorFlow is an open-source platform for machine learning.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Python Natural Language Processing 52

AWS Machine Learning Blog

MARCH 8, 2023

Amazon SageMaker is a fully managed machine learning (ML) service. With SageMaker, data scientists and developers can quickly and easily build and train ML models, and then directly deploy them into a production-ready hosted environment. All code for this post is available in the GitHub repo.

ML 135

ML ML AWS Python 135

Heartbeat

JANUARY 12, 2024

It leverages sentence transformers to embed the text data and fine-tunes the head layer to perform the classification task. SetFit's two-stage training process —  src Few-Shot Training — Data Preparation As explained, we are all set to train the SetFit model with a handful of data.

ML 52

ML ML Deep Learning Deep Learning 52