DagsHub

NOVEMBER 1, 2023

They specialize in developing cutting-edge, AI-based drug delivery solutions, which integrate machine learning (henceforth: ML) and nanotechnology. They use ML, particularly graph neural networks, to analyze the interactions within molecules and develop models that predict chemical and biological properties.

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The MLOps Blog

AUGUST 11, 2023

There comes a time when every ML practitioner realizes that training a model in Jupyter Notebook is just one small part of the entire project. At that point, the Data Scientists or ML Engineers become curious and start looking for such implementations. What are ML pipeline architecture design patterns?

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AWS Machine Learning Blog

JANUARY 6, 2023

In Part 1 , we discussed the applications of GNNs and how to transform and prepare our IMDb data into a knowledge graph (KG). In Part 2 , we demonstrated how to use Amazon Neptune ML (in Amazon SageMaker ) to train the KG and create KG embeddings.

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Heartbeat

JUNE 27, 2023

Photo by Resource Database on Unsplash Introduction Neural networks have been operating on graph data for over a decade now. Neural networks leverage the structure and properties of graph and work in a similar fashion. Graph Neural Networks are a class of artificial neural networks that can be represented as graphs.

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AWS Machine Learning Blog

MAY 17, 2024

You can try out this model with SageMaker JumpStart, a machine learning (ML) hub that provides access to algorithms and models so you can quickly get started with ML. What is SageMaker JumpStart With SageMaker JumpStart, ML practitioners can choose from a growing list of best-performing foundation models.

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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. technical architecture that simplifies the process of managing real-time ML models and features.

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ODSC - Open Data Science

SEPTEMBER 27, 2023

MLOps: Monitoring and Managing Drift Oliver Zeigermann | Machine Learning Architect | Freelancer In this 2 part workshop, you’ll simulate production on a machine learning model, detect drift, and analyze and decide on the necessary steps to resolve it. MLOps v LMOps — What’s Different?

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Flipboard

MARCH 13, 2023

This post is co-written with Mahima Agarwal, Machine Learning Engineer, and Deepak Mettem, Senior Engineering Manager, at VMware Carbon Black VMware Carbon Black is a renowned security solution offering protection against the full spectrum of modern cyberattacks.

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DagsHub

APRIL 7, 2024

These pipelines cover the entire lifecycle of an ML project, from data ingestion and preprocessing, to model training, evaluation, and deployment. Adopted from [link] In this article, we will first briefly explain what ML workflows and pipelines are. around the world to streamline their data and ML pipelines.

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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. Participants will walk away with a solid grasp of feature stores, equipped with the knowledge to drive meaningful insights and enhancements in their real-world ML platforms and projects.

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AWS Machine Learning Blog

NOVEMBER 30, 2023

Amazon SageMaker makes it straightforward to deploy machine learning (ML) models for real-time inference and offers a broad selection of ML instances spanning CPUs and accelerators such as AWS Inferentia. The endpoint uniformly distributes incoming requests to ML instances using a round-robin algorithm.

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AWS Machine Learning Blog

DECEMBER 13, 2023

Machine learning (ML) models do not operate in isolation. To deliver value, they must integrate into existing production systems and infrastructure, which necessitates considering the entire ML lifecycle during design and development. Building a robust MLOps pipeline demands cross-functional collaboration.

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AWS Machine Learning Blog

MAY 10, 2023

Amazon SageMaker provides a broad selection of machine learning (ML) infrastructure and model deployment options to help meet your ML inference needs. New generations of CPUs offer a significant performance improvement in ML inference due to specialized built-in instructions. 4xlarge c6g.4xlarge 4xlarge c6i.4xlarge

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AWS Machine Learning Blog

NOVEMBER 24, 2023

Amazon SageMaker Canvas is a rich, no-code Machine Learning (ML) and Generative AI workspace that has allowed customers all over the world to more easily adopt ML technologies to solve old and new challenges thanks to its visual, no-code interface. For more information, refer to Manage billing and cost in SageMaker Canvas.

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O'Reilly Media

OCTOBER 19, 2021

This is both frustrating for companies that would prefer making ML an ordinary, fuss-free value-generating function like software engineering, as well as exciting for vendors who see the opportunity to create buzz around a new category of enterprise software. What does a modern technology stack for streamlined ML processes look like?

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AWS Machine Learning Blog

SEPTEMBER 18, 2023

Machine learning (ML) is becoming increasingly complex as customers try to solve more and more challenging problems. This complexity often leads to the need for distributed ML, where multiple machines are used to train a single model. SageMaker is a fully managed service for building, training, and deploying ML models.

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AWS Machine Learning Blog

MARCH 1, 2023

This post is co-written with Suhyoung Kim, General Manager at KakaoGames Data Analytics Lab. Statistical methods and machine learning (ML) methods are actively developed and adopted to maximize the LTV. These challenges are typically faced when we implement ML solutions and deploy them into a production environment.

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ODSC - Open Data Science

FEBRUARY 28, 2023

To deliver on their commitment to enhancing human ingenuity, SAS’s ML toolkit focuses on automation and more to provide smarter decision-making. Blueprint Blueprint utilizes its expertise in data management, including analytics, migration, governance, and centralization to enable its clients to get the most from their data.

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AWS Machine Learning Blog

OCTOBER 2, 2023

We show you how to use AWS IoT Greengrass to manage model inference at the edge and how to automate the process using AWS Step Functions and other AWS services. AWS IoT Greengrass is an Internet of Things (IoT) open-source edge runtime and cloud service that helps you build, deploy, and manage edge device software.

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ODSC - Open Data Science

JANUARY 25, 2023

The Azure ML team has long focused on bringing you a resilient product, and its latest features take one giant leap in that direction, as illustrated in the graph below (Figure 1). Continue reading to learn more about Azure ML’s latest announcements. This is the motivation behind several of Azure ML’s latest features.

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AWS Machine Learning Blog

DECEMBER 7, 2023

In this post, we describe the end-to-end workforce management system that begins with location-specific demand forecast, followed by courier workforce planning and shift assignment using Amazon Forecast and AWS Step Functions. It allows for better control and efficient resource management.

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ODSC - Open Data Science

OCTOBER 20, 2023

You’ll use MLRun, Langchain, and Milvus for this exercise and cover topics like the integration of AI/ML applications, leveraging Python SDKs, as well as building, testing, and tuning your work. In this session, you’ll explore how you can use graph representations for real-world business problems.

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AWS Machine Learning Blog

FEBRUARY 15, 2024

With the use of cloud computing, big data and machine learning (ML) tools like Amazon Athena or Amazon SageMaker have become available and useable by anyone without much effort in creation and maintenance. It also allows you to deploy and share these models with ML and MLOps specialists after creation.

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Heartbeat

NOVEMBER 8, 2023

In a modest machine learning project, it may take a lot of work to manage these processes. This is where Comet comes in as a versatile machine learning experiment management platform that can help manage machine learning projects. It provides a single platform for managing machine learning experiments. What is Comet?

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DagsHub

DECEMBER 5, 2023

This is where ML experiment tracking comes into play! What is ML Experiment Tracking? ML experiment tracking is the process of recording, organizing, and analyzing the results of ML experiments. How to Choose the ML Experiment Tracking Tool that Fits Your Needs?

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AWS Machine Learning Blog

FEBRUARY 29, 2024

Creating scalable and efficient machine learning (ML) pipelines is crucial for streamlining the development, deployment, and management of ML models. In this post, we present a framework for automating the creation of a directed acyclic graph (DAG) for Amazon SageMaker Pipelines based on simple configuration files.

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AWS Machine Learning Blog

APRIL 1, 2024

Machine learning (ML) research has proven that large language models (LLMs) trained with significantly large datasets result in better model quality. However, training or fine-tuning these large models for a custom use case requires a large amount of data and compute power, which adds to the overall engineering complexity of the ML stack.

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AWS Machine Learning Blog

OCTOBER 31, 2023

This post was co-written with Anthony Medeiros, Manager of Solutions Engineering and Architecture for North America Artificial Intelligence, and Blake Santschi, Business Intelligence Manager, from Schneider Electric. Schneider Electric is a leader in digital transformation of energy management and industrial automation.

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ODSC - Open Data Science

MAY 29, 2023

Be sure to check out his talk, “ Production ML for Mission-Critical Applications ,” there! Using an ML model in a product, service, or business process means that the model results need to be repeatable and predictable. Editor’s note: Robert Crowe is a speaker for ODSC Europe this June.

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ODSC - Open Data Science

FEBRUARY 16, 2023

They store and manage data either on-premise or in the cloud. Business analysts, IT teams, and managers access the stored data to determine how they want to analyze it. The end user presents the data in a visualization format, such as a graph or table. Companies use application software to sort the data based on user requests.

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AWS Machine Learning Blog

NOVEMBER 14, 2023

However, this introduces a new challenge: how to properly manage these new types of fine-tuned models. In this post, we walk through best practices for managing LoRA fine-tuned models on Amazon SageMaker to address this emerging question. Should you combine the base model and adapter or keep them separate? Python 3.10

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AWS Machine Learning Blog

JANUARY 30, 2024

It could contain information in the form of text, or embedded in graphs, tables, and pictures. This provides a powerful search capability to extract contextually relevant content from visual elements like tables and graphs along with text. However, real-world data exists in multiple modalities, such as text, images, video, and audio.

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AWS Machine Learning Blog

MAY 10, 2023

Among the data scientist community, TensorBoard is a popular toolkit that allows data scientists to visualize and analyze various aspects of their machine learning (ML) models and training processes. It provides a suite of tools for visualizing training metrics, examining model architectures, exploring embeddings, and more. x_test / 255.0

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The MLOps Blog

MAY 9, 2023

One of the most prevalent complaints we hear from ML engineers in the community is how costly and error-prone it is to manually go through the ML workflow of building and deploying models. Building end-to-end machine learning pipelines lets ML engineers build once, rerun, and reuse many times. If all goes well, of course ?

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AWS Machine Learning Blog

JANUARY 13, 2023

Because they are in a highly regulated domain, HCLS partners and customers seek privacy-preserving mechanisms to manage and analyze large-scale, distributed, and sensitive data. Federated learning mitigates these challenges by collaboratively training ML models that use distributed data, without the need to share or centralize them.

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AWS Machine Learning Blog

APRIL 19, 2024

This provides a powerful search capability to extract contextually relevant content from visual elements like tables and graphs along with text. Amazon OpenSearch Ingestion (OSI) is a fully managed, serverless data collector that delivers data to OpenSearch Service domains and OpenSearch Serverless collections.

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AWS Machine Learning Blog

AUGUST 10, 2023

Amazon SageMaker is a fully managed machine learning (ML) platform that offers a comprehensive set of services that serve end-to-end ML workloads. As recommended by AWS as a best practice , customers have used separate accounts to simplify policy management for users and isolate resources by workloads and account.

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