AWS Machine Learning Blog

MAY 23, 2023

This is a guest blog post co-written with Vik Pant and Kyle Bassett from PwC. Organizations with greater maturity in the ML domain adopt an ML operations (MLOps) paradigm that incorporates continuous integration, continuous delivery, continuous deployment, and continuous training.

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AWS Machine Learning Blog

JANUARY 10, 2024

Specialist Data Engineering at Merck, and Prabakaran Mathaiyan, Sr. ML Engineer at Tiger Analytics. Model developers often work together in developing ML models and require a robust MLOps platform to work in. Model developers often work together in developing ML models and require a robust MLOps platform to work in.

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Towards AI

AUGUST 18, 2023

Photo by sebastiaan stam on Unsplash The one tool that ML/MLOps engineers underestimate is Terraform. You will always use ONLY the… Read the full blog for free on Medium. Terraform 101: How to Use Terraform as an MLE to Automate a Production-Ready AWS Infrastructure This member-only story is on us.

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IBM Journey to AI blog

SEPTEMBER 19, 2023

Data science is an area of expertise that combines many disciplines such as mathematics, computer science, software engineering and statistics. Though you may encounter the terms “data science” and “data analytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts.

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AWS Machine Learning Blog

FEBRUARY 27, 2024

However, even though the pace of innovation is high, the different teams had developed their own ways of working and were in search of a new MLOps best practice. We decided to put in a joint effort to build a prototype on a best practice for MLOps.

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IBM Journey to AI blog

JULY 6, 2023

Other challenges include communicating results to non-technical stakeholders, ensuring data security, enabling efficient collaboration between data scientists and data engineers, and determining appropriate key performance indicator (KPI) metrics. Both data science and machine learning are used by data engineers and in almost every industry.

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AWS Machine Learning Blog

JANUARY 5, 2024

In this post, we discuss model development and MLOps framework implementation for one of Wipro’s customers that uses Amazon SageMaker and other AWS services. MLOps – Model monitoring and ongoing governance wasn’t tightly integrated and automated with the ML models.

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The MLOps Blog

MARCH 28, 2023

In this second installment of the series “Real-world MLOps Examples,” Paweł Pęczek , Machine Learning Engineer at Brainly , will walk you through the end-to-end Machine Learning Operations (MLOps) process in the Visual Search team at Brainly. Watch this video to learn how the Content AI team does MLOps. “If

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AWS Machine Learning Blog

SEPTEMBER 1, 2023

In this post, we discuss how to operationalize generative AI applications using MLOps principles leading to foundation model operations (FMOps). MLOps engineers are responsible for providing a secure environment for data scientists and ML engineers to productionize the ML use cases.

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Heartbeat

JANUARY 9, 2024

You can adapt foundation models to downstream tasks in the following ways: Prompt Engineering: Prompt engineering is a powerful technique that enables LLMs to be more controllable and interpretable in their outputs, making them more suitable for real-world applications with specific requirements and constraints.

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ODSC - Open Data Science

JANUARY 1, 2024

Over the years, ODSC has developed a close relationship with them, working together to host webinars, write blogs, and even collaborate on sessions at ODSC conferences. Below, you’ll find a rundown of all of our Microsoft and ODSC collaborative efforts, including past webinars & talks, blogs, and more. Where can I stay up to date?

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ODSC - Open Data Science

APRIL 20, 2023

How MLOps Work in the Era of Large Language Models In the era of large language models, how we approach MLOps may be a bit different than usual. Google CEO Sundar Pichai Says Search Engine Will Add AI Chat Google CEO Sundar Pichai says that the company will use AI to help beef up the offers of its popular search engine.

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AWS Machine Learning Blog

OCTOBER 2, 2023

This is Part 3 of our series where we design and implement an MLOps pipeline for visual quality inspection at the edge. In this post, we focus on how to automate the edge deployment part of the end-to-end MLOps pipeline. For other topics and use cases, refer to our Machine Learning and IoT blogs.

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AWS Machine Learning Blog

DECEMBER 13, 2023

ML operations, known as MLOps, focus on streamlining, automating, and monitoring ML models throughout their lifecycle. Building a robust MLOps pipeline demands cross-functional collaboration. With the right processes and tools, MLOps enables organizations to reliably and efficiently adopt ML across their teams.

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Mlearning.ai

AUGUST 16, 2023

In this blog, I will discuss how to use weights & biases with your machine learning projects. image by Analytics Vidhya ( [link] ) Weights & Biases have been used by machine learning engineers, researchers, and data scientists to manage machine learning projects. Weights & Biases will do for us.

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The MLOps Blog

JANUARY 11, 2023

Overall, implementing MLOps in an IoT edge company is important because IoT systems often involve relatively large volumes of data, high levels of complexity, and real-time decision-making. If the machine learning tasks required by your use cases can be implemented using AI services, then you don’t need an MLOps solution.

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ODSC - Open Data Science

DECEMBER 28, 2023

Listen to the first three episodes of ODSC’s Ai X Podcast here: Spotify SoundCloud Apple Industry, Opinion, Career Advice All of the Microsoft and ODSC Partnership Offerings Here’s a rundown of all collaborative efforts between Microsoft and ODSC, including webinars, blogs, conference talks, and more. Register here!

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AWS Machine Learning Blog

FEBRUARY 13, 2024

In our entire partnership, AWS has set the bar on customer obsession and delivering results—working with us the whole way to realize promised benefits.” – Keshav Kumar, Head of Engineering at BigBasket. About the Authors Santosh Waddi is a Principal Engineer at BigBasket, brings over a decade of expertise in solving AI challenges.

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AWS Machine Learning Blog

FEBRUARY 22, 2023

Boomi is an enterprise-level software as a service (SaaS) independent software vendor (ISV) that creates developer enablement tooling for software engineers. Boomi’s ML and data engineering teams needed the solution to be deployed quickly, in a repeatable and consistent way, at scale. Why SageMaker?

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ODSC - Open Data Science

JUNE 15, 2023

How to Fine-Tune and Serve LLMs Simply, Quickly, and Cost-Effectively Using Ray + DeepSpeed + HuggingFace In this blog, we share a practical approach on how you can use the combination of HuggingFace, DeepSpeed, and Ray to build a system for fine-tuning and serving LLMs, in 40 minutes for less than $7 for a 6 billion parameter model.

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ODSC - Open Data Science

FEBRUARY 8, 2024

A Beginner’s Guide to Build Your Own LLM-Based Solutions With the KNIME AI Extension In this blog, learn how you can build your own LLM-based solutions using KNIME, a low-code/no-code analytics platform. Register now for 50% off!

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Heartbeat

JANUARY 12, 2024

Let's get started… Alpaca Dataset — Overview Alpaca is a dataset of 52,000 instructions generated by OpenAI's text-davinci-003 engine. We're committed to supporting and inspiring developers and engineers from all walks of life. and they are widely employed to fine-tune LLM models.

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Bugra Akyildiz

DECEMBER 18, 2022

Before going further, a new announcement for embeddings also came from OpenAI: openai.com/blog/new-and-i… nnIt is cross-modal, and it is 1/500th of the price of the old embedding model DaVinci. ","username":"bugraa","name":"Bugra It uplifts a PoC into a production-ready service.

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AWS Machine Learning Blog

DECEMBER 29, 2022

In this blog post, we will show you how to use both of these services together to efficiently perform analysis on massive data sets in the cloud while addressing the challenges mentioned above. In the blog today, we will be executing the following steps: Cloning the sample repository with the required packages. Solution overview.

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AWS Machine Learning Blog

NOVEMBER 16, 2023

This is a joint blog with AWS and Philips. Amazon SageMaker provides purpose-built tools for machine learning operations (MLOps) to help automate and standardize processes across the ML lifecycle. Philips is a health technology company focused on improving people’s lives through meaningful innovation.

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AWS Machine Learning Blog

APRIL 3, 2024

Search engines and recommendation systems powered by generative AI can improve the product search experience exponentially by understanding natural language queries and returning more accurate results. Store embeddings into the Amazon OpenSearch Serverless as the search engine. First, let’s perform an image search based on text input.

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Towards AI

SEPTEMBER 27, 2023

Experiments, Model Training & Evaluation: Exploring 6 Key MLOps Questions using AWS SageMaker This article is part of the AWS SageMaker series for exploration of ’31 Questions that Shape Fortune 500 ML Strategy’. Refer to Data Transformation and Feature Engineering for details on adding transformation.

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AWS Machine Learning Blog

OCTOBER 2, 2023

In Part 1 of this series, we drafted an architecture for an end-to-end MLOps pipeline for a visual quality inspection use case at the edge. The following diagram shows the high-level architecture of the MLOps pipeline we defined in the beginning of this series. If you haven’t read it yet, we recommend checking out Part 1.

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Towards AI

JUNE 5, 2023

In May 2021, Khalid Salama, Jarek Kazmierczak, and Donna Schut from Google published a white paper titled “Practitioners Guide to MLOps”. The white paper goes into great depth on the concept of MLOps, its lifecycle, capabilities, and practices. There are hundreds of blogs written on the same topic.

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Heartbeat

MARCH 14, 2024

Artificial Intelligence (AI) and Machine Learning (ML) are revolutionary technologies with the potential to transform the field of engineering. This article will explore how they're reshaping industries and engineering practices unimaginably. Indium Software Why AI and ML in Engineering?

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Snorkel AI

MARCH 27, 2024

Selecting the right model from a range of architectures and sizes, curating data, engineering optimal prompts, tuning models for specific tasks, grounding model outputs in real-world data, optimizing hardware – these are just a few of the novel challenges that large models introduce.

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DataRobot Blog

JULY 27, 2022

Many engineering teams don’t pay the necessary attention to it. We combined the resources and expertise of DataRobot MLOps and Algorithmia to achieve the best results. The Demo: Autoscaling with MLOps. Operationalize ML Faster with MLOps Automation. See Autoscaling with MLOps in Action.

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Heartbeat

AUGUST 4, 2023

Performing this process well is now defined as a profession: prompt engineering. Thus, downstream segmentation tasks are solved with prompt engineering. Data Engine For SAM to be effective with new data, the model must be trained on diverse masks that go beyond the typical segmentation datasets.

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Heartbeat

FEBRUARY 13, 2023

Comet is an MLOps platform that offers a suite of tools for machine-learning experimentation and data analysis. The integration of these frameworks allows Comet users to quickly and easily gain insights from their data, making it an essential tool for data scientists and machine-learning engineers. What is Comet?

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ODSC - Open Data Science

JUNE 29, 2023

What’s Next In this blog post, we have explored some of the capabilities of whylogs for data validation. Felipe has published a series of blog articles about MLOps, Monitoring, and Natural Language Processing in publications such as Towards Data Science, Analytics Vidhya, and Google Cloud Community.

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AWS Machine Learning Blog

FEBRUARY 15, 2024

It also allows you to deploy and share these models with ML and MLOps specialists after creation. Engineers look for abnormal data when investigating root causes for a fault or as warning indicators for future faults, and performance managers examine abnormal data to identify potential improvements.

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AWS Machine Learning Blog

FEBRUARY 26, 2024

You can also explore ways to operationalize LLM evaluations at scale in Operationalize LLM Evaluation at Scale using Amazon SageMaker Clarify and MLOps services. About the Authors Bruno Klein is a Senior Machine Learning Engineer with AWS Professional Services Analytics Practice.

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