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. Experience working in data analysis, software development, and business is also crucial for an AI engineer.

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AWS Machine Learning Blog

SEPTEMBER 25, 2024

models 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 ML development lifecycle. Discover Llama 3.2 Deploy Llama 3.2

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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.

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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.

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Pickl AI

APRIL 19, 2023

Different industries from education, healthcare to marketing, retail and ecommerce require Machine Learning Engineers. Job market will experience a rise of 13% by 2026 for ML Engineers Why is Machine Learning Important? It includes learning Python, R, Java, C++, SQL, etc. Consequently.

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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. What if we decouple the dependencies of the software we write from the hardware it works on? What Are Containers?

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AWS Machine Learning Blog

JANUARY 17, 2023

In Studio, you can load data, adjust ML models, move in between steps to adjust experiments, compare results, and deploy ML models for inference. The AWS Cloud Development Kit (AWS CDK) is an open-source software development framework to create AWS CloudFormation stacks through automatic CloudFormation template generation.

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The MLOps Blog

SEPTEMBER 7, 2023

Have you ever talked to your Front-end or Back-end engineer peers and noticed how much they care about code quality? Writing legible, reusable, and efficient code has always been a challenge in the software development community. This situation is not different in the ML world. Aside neptune.ai What’s next?

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O'Reilly Media

OCTOBER 19, 2021

While there isn’t an authoritative definition for the term, it shares its ethos with its predecessor, the DevOps movement in software engineering: by adopting well-defined processes, modern tooling, and automated workflows, we can streamline the process of moving from development to robust production deployments. Versioning.

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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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AWS Machine Learning Blog

APRIL 25, 2023

With this new capability of the SageMaker Python SDK, data scientists can onboard their ML code to the SageMaker Training platform in a few minutes. In this release, you can run your local machine learning (ML) Python code as a single-node Amazon SageMaker training job or multiple parallel jobs.

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AWS Machine Learning Blog

OCTOBER 15, 2024

ML practitioners can deploy FMs to dedicated SageMaker instances from a network-isolated environment and customize models using SageMaker for model training and deployment. You can now discover and deploy Bria models in Amazon SageMaker Studio or programmatically through the SageMaker Python SDK.

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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 a software developer the more versed you are in DevOps the better you can foresee issues, fix bugs and be a valued team member. As an ML engineer you’re in charge of some code/model.

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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. Experiment Tracking: Numerous configuration options (data, hyperparameters, pre-processing) impact ML outcomes.

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AWS Machine Learning Blog

SEPTEMBER 29, 2023

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. This is a powerful tool for data scientists and developers who want to work with satellite imagery and other geospatial data.

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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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The MLOps Blog

AUGUST 4, 2023

Machine learning monorepo | Source: Author A monorepo (short for monolithic repository) is a software development strategy where code for many projects is stored in the same repository. or as narrow as a couple of Python projects developed by a small team thrown into a single repository. Here are a few notable examples.

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AWS Machine Learning Blog

NOVEMBER 21, 2023

NVIDIA Triton Inference Server provides two different kind backends: one for hosting models on GPU, and a Python backend where you can bring your own custom code to be used in preprocessing and postprocessing steps. Tarun Sharma is a Software Development Manager leading Amazon Music Search Relevance.

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ODSC - Open Data Science

JANUARY 10, 2024

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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AWS Machine Learning Blog

AUGUST 1, 2024

By demonstrating the process of deploying fine-tuned models, we aim to empower data scientists, ML engineers, and application developers to harness the full potential of FMs while addressing unique application requirements. AI/ML Specialist Solutions Architect working on Amazon Web Services.

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AWS Machine Learning Blog

OCTOBER 2, 2023

Use case: Inspecting the quality of metal tags As an ML engineer, it’s important to understand the business case you are working on. In this example, we’re using the Ultralytics YOLOv8 Python package and model architecture to train and export an object detection model to the ONNX ML model format for portability.

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AWS Machine Learning Blog

JUNE 8, 2023

This is your Custom Python Hook speaking!" Victor Rojo is a highly experienced technologist who is passionate about the latest in AI, ML, and software development. Ryan Gomes is a Data & ML Engineer with the AWS Professional Services Intelligence Practice. He leads the NYC machine learning and AI meetup.

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The MLOps Blog

MARCH 15, 2023

AWS Glue consists of a metadata repository known as Glue catalog, an engine to generate the Scala or Python code for the ETL Job, and also does job monitoring, scheduling, and so on. Git is a distributed version control system for software development. In our case, this tool helped us to build and test the ML models.

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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. Zhuling Bai is a Software Development Engineer at Amazon Web Services.

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AWS Machine Learning Blog

APRIL 29, 2024

Second, open source Metaflow provides the necessary software infrastructure to build production-grade ML/AI systems in a developer-friendly manner. It provides an approachable, robust Python API for the full infrastructure stack of ML/AI, from data and compute to workflows and observability.

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AWS Machine Learning Blog

JULY 23, 2024

models in SageMaker JumpStart SageMaker JumpStart provides FMs through two primary interfaces: Amazon SageMaker Studio and the SageMaker Python SDK. Alternatively, you can use the SageMaker Python SDK to programmatically access and utilize SageMaker JumpStart models. Christopher Whitten is a software developer on the JumpStart team.

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The MLOps Blog

MARCH 21, 2023

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. In this comprehensive guide, we’ll explore everything you need to know about machine learning platforms, including: Components that make up an ML platform.

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AWS Machine Learning Blog

APRIL 19, 2024

of the SageMaker ACK Operators adds support for inference components , which until now were only available through the SageMaker API and the AWS Software Development Kits (SDKs). You can use the new inference capabilities from Amazon SageMaker Studio , the SageMaker Python SDK , AWS SDKs , and AWS Command Line Interface (AWS CLI).

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