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. Note: The focus of this article is not to show you how you can create the best ML model but to explain how effectively you can save trained models.

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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.

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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.

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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.

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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. 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.

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Viso.ai

FEBRUARY 12, 2024

True to its name, Explainable AI refers to the tools and methods that explain AI systems and how they arrive at a certain output. In this blog, we’ll dive into the need for AI explainability, the various methods available currently, and their applications. Why do we need Explainable AI (XAI)?

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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.

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Data Analysis Data Science Business Intelligence Python 126

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.

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AWS Machine Learning Blog

FEBRUARY 26, 2024

Although there are many potential metrics that you can use to monitor LLM performance, we explain some of the broadest ones in this post. This could be an actual classifier that can explain why the model refused the request. Refer to the Python documentation for an example. The function sends that average to CloudWatch metrics.

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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.

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Machine Learning Computer Vision Deep Learning Python 153

AWS Machine Learning Blog

APRIL 7, 2025

Discover Llama 4 models in SageMaker JumpStart SageMaker JumpStart provides FMs through two primary interfaces: SageMaker Studio and the Amazon SageMaker Python SDK. Alternatively, you can use the SageMaker Python SDK to programmatically access and use SageMaker JumpStart models.

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Machine Learning Large Language Models Python Automation 62

Towards AI

MAY 30, 2024

Of course, I made a video giving more details about the book if you are curious: p.s. The only skill required for the book is some Python (or programming) knowledge. It starts from explaining what an LLM is in simpler terms, and takes you through a brief history of time in NLP to the most current state of technology in AI.

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Prompt Engineer Prompt Engineering LLM NLP 108

Towards AI

FEBRUARY 20, 2025

But who exactly is an LLM developer, and how are they different from software developers and ML engineers? If you are skilled in Python or computer vision, diffusion models, or GANS, you might be a great fit. Well, briefly, software developers focus on building traditional applications using explicit code. Meme of the week!

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O'Reilly Media

OCTOBER 19, 2021

All ML projects are software projects. If you peek under the hood of an ML-powered application, these days you will often find a repository of Python code. Arguably, high-level programming languages like Python are the most expressive and efficient ways that humankind has conceived to formally define complex processes.

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AWS Machine Learning Blog

OCTOBER 19, 2023

Amazon SageMaker provides purpose-built tools for ML teams to automate and standardize processes across the ML lifecycle. You can use SageMaker Data Wrangler to simplify and streamline dataset preprocessing and feature engineering by either using built-in, no-code transformations or customizing with your own Python scripts.

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AWS Machine Learning Blog

FEBRUARY 21, 2025

We use DSPy (Declarative Self-improving Python) to demonstrate the workflow of Retrieval Augmented Generation (RAG) optimization, LLM fine-tuning and evaluation, and human preference alignment for performance improvement. Clone the GitHub repository and follow the steps explained in the README. Set up a SageMaker notebook instance.

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The MLOps Blog

JUNE 27, 2023

Knowledge and skills in the organization Evaluate the level of expertise and experience of your ML team and choose a tool that matches their skill set and learning curve. For example, if your team is proficient in Python and R, you may want an MLOps tool that supports open data formats like Parquet, JSON, CSV, etc.,

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AWS Machine Learning Blog

FEBRUARY 28, 2024

Set up the SDK for Python (Boto3). medium instance with the Python 3 (Data Science) kernel. About the Authors Sanjeeb Panda is a Data and ML engineer at Amazon. Outside of his work as a Data and ML engineer at Amazon, Sanjeeb Panda is an avid foodie and music enthusiast.

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Metadata LLM Generative AI NLP 125

AWS Machine Learning Blog

JULY 24, 2024

It can also be done at scale, as explained in Operationalize LLM Evaluation at Scale using Amazon SageMaker Clarify and MLOps services. Fine-tuning an LLM can be a complex workflow for data scientists and machine learning (ML) engineers to operationalize. You can then select the best model based on the evaluation results.

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AWS Machine Learning Blog

APRIL 7, 2025

Explainability: Tracing provides insights into the agents decision-making process, helping you to understand the reasoning behind its actions. Imagine a large team of data scientists and ML engineers working on an agentic platform, as shown in the following image.

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Pickl AI

NOVEMBER 28, 2024

Summary: The blog discusses essential skills for Machine Learning Engineer, emphasising the importance of programming, mathematics, and algorithm knowledge. Key programming languages include Python and R, while mathematical concepts like linear algebra and calculus are crucial for model optimisation. during the forecast period.

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AWS Machine Learning Blog

JUNE 27, 2023

They needed a cloud platform and a strategic partner with proven expertise in delivering production-ready AI/ML solutions, to quickly bring EarthSnap to the market. We initiated a series of enhancements to deliver managed MLOps platform and augment ML engineering. Endpoints had to be deployed manually as well.

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DevOps ML Machine Learning ML Engineer 109

Chatbots Life

MAY 12, 2023

As everything is explained from scratch but extensively I hope you will find it interesting whether you are NLP Expert or just want to know what all the fuss is about. We will discuss how models such as ChatGPT will affect the work of software engineers and ML engineers. and we will also explain how GPT can create jobs.

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ODSC - Open Data Science

FEBRUARY 22, 2024

Key points of this talk are: In this talk, we will focus on: The dangers of using post-hoc explainability methods as tools for decision making, and how traditional ML isn’t suited in situations where want to perform interventions on the system. In particular you will cover proper types, chaining, aggregation, debugging.

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AWS Machine Learning Blog

FEBRUARY 7, 2025

This post, part of the Governing the ML lifecycle at scale series ( Part 1 , Part 2 , Part 3 ), explains how to set up and govern a multi-account ML platform that addresses these challenges. ML engineers Develop model deployment pipelines and control the model deployment processes.

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ML Data Scientist ML Engineer Data Science 62

Mlearning.ai

AUGUST 18, 2023

TL;DR This series explain how to implement intermediate MLOps with simple python code, without introducing MLOps frameworks (MLflow, DVC …). As an ML engineer you’re in charge of some code/model. Python has different flavors, and some freedom about the location of scripts and components. Why is this a requirement?

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DevOps Python ML ML Engineer 52

AWS Machine Learning Blog

OCTOBER 15, 2024

In this post, we discuss Bria’s family of models, explain the Amazon SageMaker platform, and walk through how to discover, deploy, and run inference on a Bria 2.3 ML practitioners can deploy FMs to dedicated SageMaker instances from a network-isolated environment and customize models using SageMaker for model training and deployment.

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The MLOps Blog

OCTOBER 20, 2023

And that’s what we’re going to focus on in this article, which is the second in my series on Software Patterns for Data Science & ML Engineering. There, you can use infographics, custom visualizations, and broader ways to explain your ideas. Data on its own is not sufficient for a cohesive story. For one, Git diffs within.py

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AWS Machine Learning Blog

SEPTEMBER 23, 2024

Throughout this exercise, you use Amazon Q Developer in SageMaker Studio for various stages of the development lifecycle and experience firsthand how this natural language assistant can help even the most experienced data scientists or ML engineers streamline the development process and accelerate time-to-value.

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ML Computer Vision Data Scientist Machine Learning 78

TheSequence

MARCH 10, 2024

Can AI help explain the universe? Last week, computer scientist and physicist Stephen Wolfram published a long and detailed essay attempting to explain the potential and limits of AI in discovering new science. Is AI going to discover everything? What are the limits of AI when it comes to science?

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Chatbots ML LLM Computer Scientist 52

TheSequence

MAY 24, 2023

Since a lot of developers are working on Python we continued to trainStarCoder for about 35B tokens (~3% of full training) on the Python subset which lead to a significant performance boost. Could you explain the data curation and training process required for building such a model? What are the best benchmarks in the market?

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Auto-complete ML Python Machine Learning 59

AWS Machine Learning Blog

NOVEMBER 9, 2023

This collaboration ensures that your MLOps platform can adapt to evolving business needs and accelerates the adoption of ML across teams. Machine Learning Engineer with AWS Professional Services. She is passionate about developing, deploying, and explaining AI/ ML solutions across various domains.

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DataRobot Blog

JANUARY 10, 2023

We will be writing code in Python, but DataRobot Notebooks also supports R if that’s your preferred language. The built-in notebook environments come with the respective DataRobot client (Python or R) preinstalled, and DataRobot handles authenticating the client on the user’s behalf. Model Explainability for Responsible and Trusted AI.

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Pickl AI

JANUARY 29, 2024

ML focuses on algorithms like decision trees, neural networks, and support vector machines for pattern recognition. Skills Proficiency in programming languages (Python, R), statistical analysis, and domain expertise are crucial. AI Engineer, Machine Learning Engineer, and Robotics Engineer are prominent roles in AI.

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Data Science ML Machine Learning Artificial Intelligence 52

Heartbeat

SEPTEMBER 6, 2023

Comet Comet is a machine learning platform built to help data scientists and ML engineers track, compare, and optimize machine learning experiments. We can install this library in the same way as other Python libraries, using pip . We're committed to supporting and inspiring developers and engineers from all walks of life.

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AWS Machine Learning Blog

NOVEMBER 30, 2023

It also integrates with Machine Learning and Operation (MLOps) workflows in Amazon SageMaker to automate and scale the ML lifecycle. He leads a team of scientists working on Responsible AI and his research interests are Algorithmic Fairness and Explainable Machine Learning. What is FMEval? How can you get started?

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ODSC - Open Data Science

JANUARY 10, 2024

Takeaways include: The dangers of using post-hoc explainability methods as tools for decision-making, and where traditional ML falls short. You will also become familiar with the concept of LLM as a reasoning engine that can power your applications, paving the way to a new landscape of software development in the era of Generative AI.

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