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

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. This allows you to keep track of your ML experiments.

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AWS Machine Learning Blog

AUGUST 22, 2024

It automatically keeps track of model artifacts, hyperparameters, and metadata, helping you to reproduce and audit model versions. The SageMaker Pipelines decorator feature helps convert local ML code written as a Python program into one or more pipeline steps. You can write your ML function as you would for any ML project.

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Marktechpost

JULY 25, 2024

It is ideal for ML engineers, data scientists, and technical leaders, providing real-world training for production-ready generative AI using Amazon Bedrock and cloud-native services. Participants learn when to apply finetuning, how to prepare data, and how to train and evaluate LLMs.

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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.

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AWS Machine Learning Blog

FEBRUARY 13, 2024

Let’s demystify this using the following personas and a real-world analogy: Data and ML engineers (owners and producers) – They lay the groundwork by feeding data into the feature store Data scientists (consumers) – They extract and utilize this data to craft their models Data engineers serve as architects sketching the initial blueprint.

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AWS Machine Learning Blog

APRIL 7, 2025

Repository Information**: Not shown in the provided excerpt, but likely contains metadata about the repository. **Summary**: Provides an overview of the file's purpose, format, and usage guidelines. Directory Structure**: Lists the directory structure of the repository, including file paths. On the endpoint details page, choose Delete.

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AWS Machine Learning Blog

SEPTEMBER 29, 2023

This guest post is co-written by Lydia Lihui Zhang, Business Development Specialist, and Mansi Shah, Software Engineer/Data Scientist, at Planet Labs. Planet and AWS’s partnership on geospatial ML SageMaker geospatial capabilities empower data scientists and ML engineers to build, train, and deploy models using geospatial data.

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Machine Learning Data Scientist ML Python 126

The MLOps Blog

MARCH 21, 2023

From gathering and processing data to building models through experiments, deploying the best ones, and managing them at scale for continuous value in production—it’s a lot. As the number of ML-powered apps and services grows, it gets overwhelming for data scientists and ML engineers to build and deploy models at scale.

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The MLOps Blog

JANUARY 23, 2023

Came to ML from software. Founded neptune.ai , a modular MLOps component for ML metadata store , aka “experiment tracker + model registry”. Most of our customers are doing ML/MLOps at a reasonable scale, NOT at the hyperscale of big-tech FAANG companies. How about the ML engineer? Let me explain.

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AWS Machine Learning Blog

NOVEMBER 14, 2024

However, model governance functions in an organization are centralized and to perform those functions, teams need access to metadata about model lifecycle activities across those accounts for validation, approval, auditing, and monitoring to manage risk and compliance. An experiment collects multiple runs with the same objective.

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ODSC - Open Data Science

SEPTEMBER 21, 2023

Secondly, to be a successful ML engineer in the real world, you cannot just understand the technology; you must understand the business. We should start by considering the broad elements that should constitute any ML solution, as indicated in the following diagram: Figure 1.2:

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Machine Learning ML Engineer ML Data Science 98

AWS Machine Learning Blog

JANUARY 10, 2024

Specialist Data Engineering at Merck, and Prabakaran Mathaiyan, Sr. ML Engineer at Tiger Analytics. The large machine learning (ML) model development lifecycle requires a scalable model release process similar to that of software development. This post is co-written with Jayadeep Pabbisetty, Sr.

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AWS Machine Learning Blog

JANUARY 13, 2023

We also share how TR enabled monitoring and governance for ML models created across different business units with a single pane of glass. Historically at TR, ML has been a capability for teams with advanced data scientists and engineers. Data service. The challenges.

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ODSC - Open Data Science

FEBRUARY 25, 2025

Topics Include: Agentic AI DesignPatterns LLMs & RAG forAgents Agent Architectures &Chaining Evaluating AI Agent Performance Building with LangChain and LlamaIndex Real-World Applications of Autonomous Agents Who Should Attend: Data Scientists, Developers, AI Architects, and ML Engineers seeking to build cutting-edge autonomous systems.

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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. In this post, we describe how Philips partnered with AWS to develop AI ToolSuite—a scalable, secure, and compliant ML platform on SageMaker.

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ODSC - Open Data Science

MARCH 27, 2023

Our expert speakers will cover a wide range of topics, tools, and techniques that data scientists of all levels can apply in their work. ODSC Europe is still a few months away, coming this June 14th-15th, but we couldn’t be more excited to announce our first group of sessions. Check a few of them out below.

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The MLOps Blog

MAY 10, 2023

It allows data scientists to packaging their trained models, their dependencies, and the infrastructure code required to serve the model into a reusable package called a “Bento” BentoML supports various machine learning frameworks and deployment platforms and provides a unified API for managing the lifecycle of the model.

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AWS Machine Learning Blog

AUGUST 31, 2023

Implementing these guardrails is getting harder for enterprises because the ML processes and activities within enterprises are becoming more complex due to the inclusion of deeply involved processes that require contributions from multiple stakeholders and personas.

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AWS Machine Learning Blog

OCTOBER 2, 2024

However, a more holistic organizational approach is crucial because generative AI practitioners, data scientists, or developers can potentially use a wide range of technologies, models, and datasets to circumvent the established controls. Tanvi Singhal is a Data Scientist within AWS Professional Services.

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AWS Machine Learning Blog

JULY 24, 2024

Fine-tuning an LLM can be a complex workflow for data scientists and machine learning (ML) engineers to operationalize. By logging your datasets with MLflow, you can store metadata, such as dataset descriptions, version numbers, and data statistics, alongside your MLflow runs.

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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.

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AWS Machine Learning Blog

MARCH 1, 2023

Solution overview The ML solution for LTV forecasting is composed of four components: the training dataset ETL pipeline, MLOps pipeline, inference dataset ETL pipeline, and ML batch inference. ML engineers no longer need to manage this training metadata separately.

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AWS Machine Learning Blog

MARCH 30, 2023

This allows for seamless communication of positional data and various outputs of Bundesliga Match Facts between containers in real time. The match-related data is collected and ingested using DFL’s DataHub. Both the Lambda function and the Fargate container publish the data for further consumption in the relevant MSK topics.

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AWS Machine Learning Blog

MAY 5, 2023

Solution overview Ground Truth is a fully self-served and managed data labeling service that empowers data scientists, machine learning (ML) engineers, and researchers to build high-quality datasets. For our example use case, we work with the Fashion200K dataset , released at ICCV 2017.

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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. Data scientists, ML engineers, IT staff, and DevOps teams must work together to operationalize models from research to deployment and maintenance.

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AWS Machine Learning Blog

NOVEMBER 30, 2023

Reports holistically summarize each evaluation in a human-readable way, through natural-language explanations, visualizations, and examples, focusing annotators and data scientists on where to optimize their LLMs and help make informed decisions. What is FMEval?

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The MLOps Blog

JANUARY 24, 2024

help data scientists systematically record, catalog, and analyze modeling artifacts and experiment metadata. is an experiment tracker for ML teams that struggle with debugging and reproducing experiments, sharing results, and messy model handover. Experiment trackers like neptune.ai Even though neptune.ai

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Viso.ai

MARCH 29, 2024

MLflow is an open-source platform designed to manage the entire machine learning lifecycle, making it easier for ML Engineers, Data Scientists, Software Developers, and everyone involved in the process. MLflow can be seen as a tool that fits within the MLOps (synonymous with DevOps) framework.

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AWS Machine Learning Blog

JUNE 13, 2023

This post is co-written with Jad Chamoun, Director of Engineering at Forethought Technologies, Inc. and Salina Wu, Senior ML Engineer at Forethought Technologies, Inc. We defined logic that would take in model metadata, format the endpoint deterministically based on the metadata, and check whether the endpoint existed.

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The MLOps Blog

AUGUST 3, 2023

This is Piotr Niedźwiedź and Aurimas Griciūnas from neptune.ai , and you’re listening to ML Platform Podcast. Stefan is a software engineer, data scientist, and has been doing work as an ML engineer. To a junior data scientist, it doesn’t matter if you’re using Airflow, Prefect , Dexter.

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AWS Machine Learning Blog

AUGUST 31, 2023

Implementing these guardrails is getting harder for enterprises because the ML processes and activities within enterprises are becoming more complex due to the inclusion of deeply involved processes that require contributions from multiple stakeholders and personas.

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ML Data Scientist Machine Learning Data Science 52

The MLOps Blog

MARCH 28, 2023

quality attributes) and metadata enrichment (e.g., This brings interpersonal challenges, and the AI/ML teams are encouraged to build good relationships with clients to help support the models by telling people how to use the solution instead of just exposing the endpoint without documentation or telling them how.

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Snorkel AI

JULY 5, 2023

RC : I have had ML engineers tell me, “You didn’t need to do feature selection anymore, and that you could just throw everything at the model and it will figure out what to keep and what to throw away.” So does that mean feature selection is no longer necessary? If not, when should we consider using feature selection?”

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Snorkel AI

JULY 5, 2023

RC : I have had ML engineers tell me, “You didn’t need to do feature selection anymore, and that you could just throw everything at the model and it will figure out what to keep and what to throw away.” So does that mean feature selection is no longer necessary? If not, when should we consider using feature selection?”

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The MLOps Blog

MARCH 15, 2023

Collaboration : Ensuring that all teams involved in the project, including data scientists, engineers, and operations teams, are working together effectively. Costs: Oftentimes, cost is the most important aspect of any ML model deployment. One Data Engineer: Cloud database integration with our cloud expert.

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AWS Machine Learning Blog

SEPTEMBER 18, 2024

By directly integrating with Amazon Managed Service for Prometheus and Amazon Managed Grafana and abstracting the management of hardware failures and job resumption, SageMaker HyperPod allows data scientists and ML engineers to focus on model development rather than infrastructure management.

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