Towards AI

AUGUST 25, 2023

In graduate school, a course in AI will usually have a quick review of the core ML concepts (covered in a previous course) and then cover searching algorithms, game theory, Bayesian Networks, Markov Decision Processes (MDP), reinforcement learning, and more. Any competent software engineer can implement any algorithm.

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AWS Machine Learning Blog

FEBRUARY 27, 2024

The SageMaker project template includes seed code corresponding to each step of the build and deploy pipelines (we discuss these steps in more detail later in this post) as well as the pipeline definition—the recipe for how the steps should be run. Pavel Maslov is a Senior DevOps and ML engineer in the Analytic Platforms team.

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TheSequence

NOVEMBER 5, 2023

📝 Editorial: AlphaFold-Latest is Pushing the Boundaries of Scientific Exploration The powering of scientific breakthroughs might be the purest definition of a Turing Test. 🛠 Real World ML LLM Architectures at GitHub GitHub ML engineers discuss the architecture of LLMs apps —> Read more.

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Pickl AI

JANUARY 29, 2024

Data Science Vs Machine Learning Vs AI Aspect Data Science Artificial Intelligence Machine Learning Definition Data Science is the field that deals with the extraction of knowledge and insights from data through various processes. AI Engineer, Machine Learning Engineer, and Robotics Engineer are prominent roles in AI.

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TheSequence

MAY 24, 2023

Were there any research breakthroughs in StarCoder, or would you say it was more of a crafty ML engineering effort? Evaluation is definitely in its infancy compared to natural language and will need to improve to better capture the user experience. What are the biggest next milestones for coding LLMs?

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AWS Machine Learning Blog

SEPTEMBER 20, 2023

This approach is beneficial if you use AWS services for ML for its most comprehensive set of features, yet you need to run your model in another cloud provider in one of the situations we’ve discussed. Key concepts Amazon SageMaker Studio is a web-based, integrated development environment (IDE) for machine learning. device), target.to(device)

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Mlearning.ai

FEBRUARY 9, 2023

Data Science is an umbrella role with common roles such as Data Analytics, research, ML model building, ML Ops, and ML engineering underneath. The definition of the role of a Data Scientist can be different between organizations and is usually dependent on the expectation of the company’s leadership.

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AWS Machine Learning Blog

SEPTEMBER 1, 2023

After the completion of the research phase, the data scientists need to collaborate with ML engineers to create automations for building (ML pipelines) and deploying models into production using CI/CD pipelines. Only prompt engineering is necessary for better results.

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AWS Machine Learning Blog

AUGUST 1, 2023

To start working with a model to learn about the capabilities of ML, all you need to do is open SageMaker Studio, find a pre-trained model you want to use in the Hugging Face Model Hub , and choose SageMaker as your deployment method. No ML engineering experience required. It’s as easy as that!

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AWS Machine Learning Blog

JANUARY 26, 2024

The goal of this post is to empower AI and machine learning (ML) engineers, data scientists, solutions architects, security teams, and other stakeholders to have a common mental model and framework to apply security best practices, allowing AI/ML teams to move fast without trading off security for speed.

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AWS Machine Learning Blog

JUNE 8, 2023

Ryan Gomes is a Data & ML Engineer with the AWS Professional Services Intelligence Practice. Prompts function as a form of context that helps direct the model toward generating relevant responses. He leads the NYC machine learning and AI meetup. In his spare time, he enjoys offshore sailing and playing jazz.

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Chatbots Life

MAY 12, 2023

We will discuss how models such as ChatGPT will affect the work of software engineers and ML engineers. Will ChatGPT replace software engineers? Will ChatGPT replace ML Engineers? Although the model acts as a highly-skilled, the profession definitely carries a lot of risks. What is ChatGPT capable of?

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The MLOps Blog

JANUARY 23, 2023

Machine Learning Operations (MLOps): Overview, Definition, and Architecture” By Dominik Kreuzberger, Niklas Kühl, Sebastian Hirschl Great stuff. If you haven’t read it yet, definitely do so. How about the ML engineer? MLOps engineer today is either an ML engineer (building ML-specific software) or a DevOps engineer.

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AWS Machine Learning Blog

NOVEMBER 9, 2023

Under Advanced Project Options , for Definition , select Pipeline script from SCM. She is passionate about developing, deploying, and explaining AI/ ML solutions across various domains. Prior to this role, she led multiple initiatives as a data scientist and ML engineer with top global firms in the financial and retail space.

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AWS Machine Learning Blog

APRIL 29, 2024

Metaflow overview Metaflow was originally developed at Netflix to enable data scientists and ML engineers to build ML/AI systems quickly and deploy them on production-grade infrastructure. How Metaflow integrates with Trainium From a Metaflow developer perspective, using Trainium is similar to other accelerators.

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The MLOps Blog

OCTOBER 3, 2023

Mikiko Bazeley: You definitely got the details correct. I joined FeatureForm last October, and before that, I was with Mailchimp on their ML platform team. I definitely don’t think I’m an influencer. I see so many of these job seekers, especially on the MLOps side or the ML engineer side.

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The MLOps Blog

MARCH 28, 2023

Each time they modify the code, the definition of the pipeline changes. Regarding other teams, they may approach testing ML models differently, especially in tabular ML use cases, by testing on sub-populations of the data. They have production and training code bases in GitHub repositories.

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ODSC - Open Data Science

JULY 19, 2023

Machine Learning Operations (MLOps) can significantly accelerate how data scientists and ML engineers meet organizational needs. A well-implemented MLOps process not only expedites the transition from testing to production but also offers ownership, lineage, and historical data about ML artifacts used within the team.

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AWS Machine Learning Blog

SEPTEMBER 18, 2023

ML engineers must handle parallelization, scheduling, faults, and retries manually, requiring complex infrastructure code. In this post, we discuss the benefits of using Ray and Amazon SageMaker for distributed ML, and provide a step-by-step guide on how to use these frameworks to build and deploy a scalable ML workflow.

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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. I term it as a feature definition store. I also recently found out, you are the CEO of DAGWorks.

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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.” That is definitely a problem. So does that mean feature selection is no longer necessary? And I can get us started here.

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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.” That is definitely a problem. So does that mean feature selection is no longer necessary? And I can get us started here.

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The MLOps Blog

MARCH 15, 2023

For an experienced Data Scientist/ML engineer, that shouldn’t come as so much of a problem. But there is definitely room for improvement in our deployment as well. You need to create a framework or write custom code to create the training/retraining pipeline on top of the experimentation tracking facilities provided by MLflow.

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AWS Machine Learning Blog

FEBRUARY 12, 2024

Package model for inference – Using a processing job, if the evaluation results are positive, the model is packaged, stored in Amazon S3, and made ready for upload to the internal ML portal. Explain – SageMaker Clarify generates an explainability report. Two distinct repositories are used.

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AWS Machine Learning Blog

MAY 13, 2024

For more details on the definition of various forms of this score, please refer to part 1 of this blog. His expertise is in reproducible and end-to-end AI/ML methods, practical implementations, and helping global healthcare customers formulate and develop scalable solutions to interdisciplinary problems.

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O'Reilly Media

JANUARY 17, 2023

This mindset has followed me into my work in ML/AI. Because if companies use code to automate business rules, they use ML/AI to automate decisions. Given that, what would you say is the job of a data scientist (or ML engineer, or any other such title)? But first, let’s talk about the typical ML workflow.

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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. The new category is often called MLOps.

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Snorkel AI

OCTOBER 12, 2023

In this session, Snorkel AI ML Engineer Ashwini Ramamoorthy explores how data-centric AI can be leveraged to simplify and streamline this process. To start, they highlight common but underrated challenges related to label schema definition, high cardinality, and multi-label problem formulations.

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Snorkel AI

OCTOBER 12, 2023

In this session, Snorkel AI ML Engineer Ashwini Ramamoorthy explores how data-centric AI can be leveraged to simplify and streamline this process. To start, they highlight common but underrated challenges related to label schema definition, high cardinality, and multi-label problem formulations.

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The MLOps Blog

JULY 18, 2023

You need to have a structured definition around what you’re trying to do so your data annotators can label information for you. In our early days, we definitely landed on the notion that there are really two critical pieces to all meeting notes. But there are definitely some caveats with using those systems.

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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. The main challenges were integrating a preprocessing step and accommodating two model artifacts per model definition.

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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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The MLOps Blog

AUGUST 11, 2023

At that point, the Data Scientists or ML Engineers become curious and start looking for such implementations. Here, the DAGs represent workflows comprising units embodying job definitions for operations to be carried out, known as Steps.

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Unite.AI

FEBRUARY 22, 2024

The very definition of ethical AI is subjective, giving rise to crucial questions about who should have the authority to decide what constitutes Responsible AI. Incident response strategies encompass a systematic approach to identifying, addressing, and mitigating potential issues that may arise during AI system deployment and usage.

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The MLOps Blog

AUGUST 4, 2023

Cross-functional collaboration Machine learning projects often involve collaboration between data scientists, ML engineers, and software engineers. To make sure the build file definition is correct, let’s run: pants tailor --check update-build-files --check :: As expected, we get: “No required changes to BUILD files found.”

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AWS Machine Learning Blog

NOVEMBER 29, 2023

Data scientists collaborate with ML engineers to transition code from notebooks to repositories, creating ML pipelines using Amazon SageMaker Pipelines, which connect various processing steps and tasks, including pre-processing, training, evaluation, and post-processing, all while continually incorporating new production data.

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The MLOps Blog

DECEMBER 29, 2022

Navigating through current ML frameworks Stephen: Right. Kyle, you definitely touched upon this already. Pietra, in chat, also notes that before ML frameworks like TensorFlow, you had to go really low-level and code in a native CUDA. Kyle: Yes, I can speak that you definitely can. So, you definitely can.

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