Analytics Vidhya

FEBRUARY 21, 2022

Image designed by the author – Shanthababu Introduction Every ML Engineer and Data Scientist must understand the significance of “Hyperparameter Tuning (HPs-T)” while selecting your right machine/deep learning model and improving the performance of the model(s). This article was published as a part of the Data Science Blogathon.

ML Engineer 337

ML Engineer Data Scientist Deep Learning Data Science 337

AWS Machine Learning Blog

OCTOBER 22, 2024

Amazon SageMaker is a cloud-based machine learning (ML) platform within the AWS ecosystem that offers developers a seamless and convenient way to build, train, and deploy ML models. In 2023, SageMaker announced the release of the new SageMaker Studio, which offers two new types of applications: JupyterLab and Code Editor.

ML Engineer 111

ML Engineer Data Scientist Machine Learning ML 111

Towards AI

FEBRUARY 21, 2023

A sensible proxy sub-question might then be: Can ChatGPT function as a competent machine learning engineer? The Set Up If ChatGPT is to function as an ML engineer, it is best to run an inventory of the tasks that the role entails. ChatGPT’s job as our ML engineer […]

ML Engineer 98

ML Engineer ML ChatGPT Computer Vision 98

Marktechpost

JANUARY 31, 2024

Data scientists and ML engineers often need help to build full-stack applications. It is a Python-based framework for data scientists and machine learning engineers. These professionals typically have a firm grasp of data and AI algorithms. This is where Taipy comes into play.

Data Scientist 135

Data Scientist Machine Learning Python ML Engineer 135

Pickl AI

JANUARY 30, 2023

Hence for an individual who wants to excel as a data scientist, learning Python is a must. The role of Python is not just limited to Data Science. In fact, Python finds multiple applications. It’s a universal programming language that finds application in different technologies like AI, ML, Big Data and others.

Python 97

Python DevOps Machine Learning Data Science 97

Towards AI

AUGUST 7, 2024

From Solo Notebooks to Collaborative Powerhouse: VS Code Extensions for Data Science and ML Teams Photo by Parabol | The Agile Meeting Toolbox on Unsplash In this article, we will explore the essential VS Code extensions that enhance productivity and collaboration for data scientists and machine learning (ML) engineers.

Data Science 116

Data Science ML ML Engineer Data Scientist 116

AWS Machine Learning Blog

FEBRUARY 11, 2025

Deploying a Falcon 3 model through SageMaker JumpStart offers two convenient approaches: using the intuitive SageMaker JumpStart UI or implementing programmatically through the SageMaker Python SDK. Lets explore both methods to help you choose the approach that best suits your needs.

ML 81

ML Machine Learning Python Computer Vision 81

Flipboard

MARCH 7, 2023

This post presents and compares options and recommended practices on how to manage Python packages and virtual environments in Amazon SageMaker Studio notebooks. Amazon SageMaker Studio is a web-based, integrated development environment (IDE) for machine learning (ML) that lets you build, train, debug, deploy, and monitor your ML models.

Python 123

Python Data Science Data Scientist Automation 123

Marktechpost

DECEMBER 1, 2024

This is where Stacklock ‘s newly released Python library, Promptwright , aims to bridge the gap. Additionally, Promptwright includes a command line interface (CLI), making it convenient to execute dataset generation tasks directly from the terminal without writing additional Python scripts.

Python 90

Python LLM Data Scarcity Data Scientist 90

The MLOps Blog

MAY 10, 2023

How to save a trained model in Python? In this section, you will see different ways of saving machine learning (ML) as well as deep learning (DL) models. The first way to save an ML model is by using the pickle file. Saving trained model with pickle The pickle module can be used to serialize and deserialize the Python objects.

Python 105

Python Metadata ML Machine Learning 105

Unite.AI

JUNE 19, 2024

Essential Skills for Becoming an MLOps Engineer To thrive as an MLOps Engineer, you'll need to cultivate a diverse set of skills spanning multiple domains. Here are some of the essential skills to develop: Programming Languages : Proficiency in Python , Java , or Scala is crucial. Tutorials : Real Python.

Software Engineer 182

Software Engineer DevOps Machine Learning ML 182

ODSC - Open Data Science

MARCH 10, 2025

Training Sessions Bayesian Analysis of Survey Data: Practical Modeling withPyMC Allen Downey, PhD, Principal Data Scientist at PyMCLabs Alexander Fengler, Postdoctoral Researcher at Brown University Bayesian methods offer a flexible and powerful approach to regression modeling, and PyMC is the go-to library for Bayesian inference in Python.

Data Scientist 52

Data Scientist Data Science LLM Machine Learning 52

AWS Machine Learning Blog

JULY 8, 2024

TWCo data scientists and ML engineers took advantage of automation, detailed experiment tracking, integrated training, and deployment pipelines to help scale MLOps effectively. ML model experimentation is one of the sub-components of the MLOps architecture. For this proof of concept, pipelines were set up using this SDK.

Data Scientist 118

Data Scientist ML Engineer Machine Learning Data Science 118

IBM Journey to AI blog

AUGUST 11, 2023

How to use ML to automate the refining process into a cyclical ML process. Initiate updates and optimization—Here, ML engineers will begin “retraining” the ML model method by updating how the decision process comes to the final decision, aiming to get closer to the ideal outcome.

Data Science 170

Data Science DevOps Machine Learning Data Scientist 170

Marktechpost

JUNE 19, 2024

It includes three self-paced courses covering generative AI basics, prompt engineering, and tools like GitHub Co-pilot and ChatGPT, with hands-on projects to apply skills in real-world scenarios. AI Prompt Engineering for Beginners This course focuses on prompt engineering for AI language tools like ChatGPT.

Artificial Intelligence 124

Artificial Intelligence Artificial Intelligence Prompt Engineering Prompt Engineer 124

AWS Machine Learning Blog

JANUARY 23, 2025

The solution described in this post is geared towards machine learning (ML) engineers and platform teams who are often responsible for managing and standardizing custom environments at scale across an organization. She has worked in several product roles in Amazon for over 5 years.

Automation 106

Automation DevOps Machine Learning ML 106

O'Reilly Media

MARCH 18, 2025

Bringing AI into a company means you have new roles to fill (data scientist, ML engineer) as well as new knowledge to backfill in existing roles (product, ops). We know Python. All this AI stuff is Python. Their code may pass the Python interpreter, but it’s all Java constructs. How hard could it be?

Software Development 52

Software Development Python Data Scientist AI 52

Marktechpost

OCTOBER 10, 2024

ML engineers often need to handle issues like model drift and data pipeline integration. Tools Data Science Tools : Python (NumPy, pandas, SciPy), R, SQL, Tableau, Power BI, Apache Spark, Jupyter Notebooks, and tools for data wrangling and exploratory data analysis.

Data Science 108

Data Science Machine Learning Data Scientist ML Engineer 108

Flipboard

NOVEMBER 24, 2023

With that, the need for data scientists and machine learning (ML) engineers has grown significantly. Data scientists and ML engineers require capable tooling and sufficient compute for their work. It is powered by Amazon SageMaker Studio and provides JupyterLab for Python and Posit Workbench for R.

ML 153

ML DevOps Data Scientist ML Engineer 153

AWS Machine Learning Blog

JANUARY 28, 2025

FMEval is an open source LLM evaluation library, designed to provide data scientists and machine learning (ML) engineers with a code-first experience to evaluate LLMs for various aspects, including accuracy, toxicity, fairness, robustness, and efficiency.

LLM 105

LLM Large Language Models ML Algorithm 105

AWS Machine Learning Blog

SEPTEMBER 4, 2024

In this post, we introduce an example to help DevOps engineers manage the entire ML lifecycle—including training and inference—using the same toolkit. Solution overview We consider a use case in which an ML engineer configures a SageMaker model building pipeline using a Jupyter notebook.

DevOps 104

DevOps ML Engineer ML Metadata 104

AWS Machine Learning Blog

DECEMBER 19, 2023

Artificial intelligence (AI) and machine learning (ML) are becoming an integral part of systems and processes, enabling decisions in real time, thereby driving top and bottom-line improvements across organizations. However, putting an ML model into production at scale is challenging and requires a set of best practices.

Machine Learning 130

Machine Learning ML Engineer DevOps ML 130

AWS Machine Learning Blog

FEBRUARY 29, 2024

Configuration files (YAML and JSON) allow ML practitioners to specify undifferentiated code for orchestrating training pipelines using declarative syntax. The following are the key benefits of this solution: Automation – The entire ML workflow, from data preprocessing to model registry, is orchestrated with no manual intervention.

Automation 121

Automation Python Machine Learning ML 121

Flipboard

SEPTEMBER 4, 2023

Envision yourself as an ML Engineer at one of the world’s largest companies. You make a Machine Learning (ML) pipeline that does everything, from gathering and preparing data to making predictions. This is suitable for making a variety of Python applications with other dependencies being added to it at the user’s convenience.

Machine Learning 153

Machine Learning Computer Vision Deep Learning Python 153

AWS Machine Learning Blog

AUGUST 22, 2024

In this post, we show you how to convert Python code that fine-tunes a generative AI model in Amazon Bedrock from local files to a reusable workflow using Amazon SageMaker Pipelines decorators. You can use Amazon SageMaker Model Building Pipelines to collaborate between multiple AI/ML teams. We use Python to do this.

Generative AI 125

Generative AI Metadata Python ML 125

Towards AI

FEBRUARY 15, 2024

AI Engineers: Your Definitive Career Roadmap Become a professional certified AI engineer by enrolling in the best AI ML Engineer certifications that help you earn skills to get the highest-paying job. Author(s): Jennifer Wales Originally published on Towards AI. These include the ability to solve problems and communicate.

AI Engineer 111

AI Engineer Software Engineer Artificial Intelligence Artificial Intelligence 111

ODSC - Open Data Science

FEBRUARY 20, 2023

Here are a few other training sessions you can check out during the event: An Introduction to Data Wrangling with SQL: Sheamus McGovern | CEO and ML Engineer | ODSC Advanced Fraud Modeling & Anomaly Detection with Python & R: Aric LaBarr, PhD | Associate Professor of Analytics | Institute for Advanced Analytics at NC State University Machine (..)

Data Science 96

Data Science Machine Learning ML Engineer Python 96

ODSC - Open Data Science

FEBRUARY 2, 2023

Computer Science and Computer Engineering Similar to knowing statistics and math, a data scientist should know the fundamentals of computer science as well. While knowing Python, R, and SQL are expected, you’ll need to go beyond that. Employers aren’t just looking for people who can program.

Data Science 98

Data Science Data Scientist Big Data Machine Learning 98

AWS Machine Learning Blog

OCTOBER 28, 2024

Create a SageMaker Model Monitor schedule Next, you use the Amazon SageMaker Python SDK to create a model monitoring schedule. You can use this framework as a starting point to monitor your custom metrics or handle other unique requirements for model quality monitoring in your AI/ML applications. About the Authors Joe King is a Sr.

ML 109

ML Metadata Data Scientist DevOps 109

AWS Machine Learning Blog

DECEMBER 9, 2024

MLflow , a popular open-source tool, helps data scientists organize, track, and analyze ML and generative AI experiments, making it easier to reproduce and compare results. SageMaker is a comprehensive, fully managed ML service designed to provide data scientists and ML engineers with the tools they need to handle the entire ML workflow.

ML 96

ML Data Scientist Machine Learning Software Engineer 96

AWS Machine Learning Blog

FEBRUARY 11, 2025

in SageMaker JumpStart SageMaker JumpStart provides FMs through two primary interfaces: SageMaker Studio and the SageMaker Python SDK. SageMaker Studio is a comprehensive IDE that offers a unified, web-based interface for performing all aspects of the machine learning (ML) development lifecycle. Discover Meta SAM 2.1 models today.

Computer Vision 81

Computer Vision ML Python Automation 81

ODSC - Open Data Science

FEBRUARY 24, 2023

Sweetviz GitHub | Website Sweetviz is an open-source Python library that generates beautiful, high-density visualizations to kickstart EDA (Exploratory Data Analysis) with just two lines of code. Apache Superset GitHub | Website Apache Superset is a must-try project for any ML engineer, data scientist, or data analyst.

Data Analysis 126

Data Analysis Data Science Business Intelligence Python 126

Flipboard

OCTOBER 9, 2023

Getting Used to Docker for Machine Learning Introduction Docker is a powerful addition to any development environment, and this especially rings true for ML Engineers or enthusiasts who want to get started with experimentation without having to go through the hassle of setting up several drivers, packages, and more.

Machine Learning 153

Machine Learning Computer Vision Deep Learning Python 153

AWS Machine Learning Blog

DECEMBER 10, 2024

Discover Pixtral 12B in SageMaker JumpStart You can access Pixtral 12B through SageMaker JumpStart in the SageMaker Studio UI and the SageMaker Python SDK. Access to accelerated instances (GPUs) for hosting the model. In this section, we go over how to discover the models in SageMaker Studio.

ML 102

ML Generative AI Computer Vision ML Engineer 102

Marktechpost

AUGUST 8, 2023

In the ever-evolving landscape of machine learning, feature management has emerged as a key pain point for ML Engineers at Airbnb. Transforming Data with Flexibility With Chronon’s SQL-like transformations and time-based aggregations, ML practitioners have the freedom to process data with ease.

Machine Learning 98

Machine Learning ML Engineer Data Ingestion ML 98

Marktechpost

JULY 25, 2024

Introduction to LLMs in Python Difficulty Level: Intermediate This hands-on course teaches you to understand, build, and utilize Large Language Models (LLMs) for tasks like translation and question-answering. Students learn about key innovations, ethical challenges, and hands-on labs for generating text with Python.

Large Language Models 113

Large Language Models Prompt Engineering Prompt Engineer Chatbots 113

ODSC - Open Data Science

DECEMBER 7, 2023

We’ll also discuss some of the benefits of using set union(), and we’ll see why it’s a popular tool for Python developers. ODSC Highlights Watch Our Top Virtual Sessions from ODSC West 2023 Here From keynote sessions to half-day workshops, these are the top-rated sessions from ODSC West 2023 that you can now watch on-demand.

Data Scientist 98

Data Scientist Explainable AI Data Science Python 98