Towards AI

AUGUST 16, 2023

Michael Dziedzic on Unsplash I am often asked by prospective clients to explain the artificial intelligence (AI) software process, and I have recently been asked by managers with extensive software development and data science experience who wanted to implement MLOps. Join thousands of data leaders on the AI newsletter.

Machine Learning 88

Machine Learning Software Development Data Drift AI 88

Heartbeat

OCTOBER 5, 2023

MLOps, often seen as a subset of DevOps (Development Operations), focuses on streamlining the development and deployment of machine learning models. Where is LLMOps in DevOps and MLOps In MLOps, engineers are dedicated to enhancing the efficiency and impact of ML model deployment.

Large Language Models 52

Large Language Models DevOps Prompt Engineer Prompt Engineering 52

The MLOps Blog

JANUARY 23, 2023

Lived through the DevOps revolution. If you’d like a TLDR, here it is: MLOps is an extension of DevOps. Not a fork: – The MLOps team should consist of a DevOps engineer, a backend software engineer, a data scientist, + regular software folks. Model monitoring tools will merge with the DevOps monitoring stack.

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DevOps Metadata Software Engineer Data Scientist 59

Snorkel AI

JULY 24, 2023

Suddenly, non-technical users witnessed the LLM-backed chatbot’s ability to regurgitate knowledge, explain jokes and write poems. The data-centric philosophy goes well beyond the point of training a model. In the following months, GenAI announcements came quickly.

Large Language Models 52

Large Language Models Artificial Intelligence Artificial Intelligence AI 52

Snorkel AI

JULY 24, 2023

Suddenly, non-technical users witnessed the LLM-backed chatbot’s ability to regurgitate knowledge, explain jokes and write poems. The data-centric philosophy goes well beyond the point of training a model. In the following months, GenAI announcements came quickly.

Large Language Models 52

Large Language Models Artificial Intelligence Artificial Intelligence AI 52

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. Data Science Layers.

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DevOps Software Engineer Data Scientist ML 138

AWS Machine Learning Blog

FEBRUARY 2, 2024

We calculate the following information based on the clustering output shown in the following figure: The number of dimensions in PCA that explain 95% of the variance The location of each cluster center, or centroid Additionally, we look at the proportion (higher or lower) of samples in each cluster, as shown in the following figure.

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ETL DevOps LLM Generative AI 91

Heartbeat

FEBRUARY 29, 2024

Furthermore, the software development process has evolved to embrace Agile methodologies, DevOps practices, and continuous integration/continuous delivery (CI/CD) pipelines. They can explain, answer coding-related questions, and offer guidance on best practices.

Software Engineer 52

Software Engineer Software Development LLM Large Language Models 52

AWS Machine Learning Blog

NOVEMBER 9, 2023

Building out a machine learning operations (MLOps) platform in the rapidly evolving landscape of artificial intelligence (AI) and machine learning (ML) for organizations is essential for seamlessly bridging the gap between data science experimentation and deployment while meeting the requirements around model performance, security, and compliance.

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Data Drift Auto-complete ML Automation 99

Mlearning.ai

MARCH 16, 2023

You have probably heard the term DevOps in conventional software development. Data Scientists and Machine Learning engineers in an act of “jealousy” adopted the concept and changed the term to MLOps. Another useful parameter is ` — scale` which dictates which scaling should be applied to the data.

Machine Learning 52

Machine Learning DevOps Software Development Data Scientist 52

Heartbeat

MARCH 5, 2024

Editor's Note: Heartbeat is a contributor-driven online publication and community dedicated to providing premier educational resources for data science, machine learning, and deep learning practitioners. We're committed to supporting and inspiring developers and engineers from all walks of life.

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Computer Vision Deep Learning Convolutional Neural Networks Machine Learning 52

IBM Journey to AI blog

SEPTEMBER 20, 2023

IBM watsonx.data is a fit-for-purpose data store built on an open lakehouse architecture to scale AI workloads for all of your data, anywhere. IBM watsonx.governance is an end-to-end automated AI lifecycle governance toolkit that is built to enable responsible, transparent and explainable AI workflows.

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Large Language Models DevOps AI Modeling Automation 225

Heartbeat

JULY 31, 2023

Editor’s Note: Heartbeat is a contributor-driven online publication and community dedicated to providing premier educational resources for data science, machine learning, and deep learning practitioners. We’re committed to supporting and inspiring developers and engineers from all walks of life.

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LLM Machine Learning Deep Learning Data Scientist 52

AWS Machine Learning Blog

OCTOBER 20, 2023

The functional architecture with different capabilities is implemented using a number of AWS services, including AWS Organizations , SageMaker, AWS DevOps services, and a data lake. The architecture maps the different capabilities of the ML platform to AWS accounts.

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ML Data Scientist ML Engineer Data Science 101

AWS Machine Learning Blog

APRIL 26, 2023

Data engineering – Identifies the data sources, sets up data ingestion and pipelines, and prepares data using Data Wrangler. Data science – The heart of ML EBA and focuses on feature engineering, model training, hyperparameter tuning, and model validation.

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ML Machine Learning Data Science Data Drift 88

The MLOps Blog

JUNE 27, 2023

This includes features for model explainability, fairness assessment, privacy preservation, and compliance tracking. With built-in components and integration with Google Cloud services, Vertex AI simplifies the end-to-end machine learning process, making it easier for data science teams to build and deploy models at scale.

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Machine Learning Metadata Data Quality Data Scientist 134

Marktechpost

JULY 17, 2023

The company’s H20 Driverless AI streamlines AI development and predictive analytics for professionals and citizen data scientists through open source and customized recipes. The platform makes collaborative data science better for corporate users and simplifies predictive analytics for professional data scientists.

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Machine Learning Data Mining Data Scientist Artificial Intelligence 93

The MLOps Blog

APRIL 17, 2023

It should be possible to locate where the data and models for an experiment came from, so your data scientists can explore the events of the experiment and the processes that led to them. This unlocks two significant benefits: Reproducibility : Ensuring every experiment your data scientists run is reproducible.

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Metadata Data Scientist Explainability ML 52

DataRobot Blog

FEBRUARY 11, 2022

These agents apply the concept familiar in the DevOps world—to run models in their preferred environments while monitoring all models centrally. All models built within DataRobot MLOps support ethical AI through configurable bias monitoring and are fully explainable and transparent. Governance and Trust.

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Data Drift Machine Learning DevOps Data Scientist 52

AWS Machine Learning Blog

APRIL 11, 2023

These customers need to balance governance, security, and compliance against the need for machine learning (ML) teams to quickly access their data science environments in a secure manner. We explain the process and network flow, and how to easily scale this architecture to multiple accounts and Amazon SageMaker domains.

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IDP Explainability Big Data ML 70

The MLOps Blog

OCTOBER 3, 2023

So I was able to get from growth hacking to data analytics, then data analytics to data science, and then data science to MLOps. I switched from analytics to data science, then to machine learning, then to data engineering, then to MLOps. How do I get this model in production?

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ML Data Scientist Machine Learning Data Science 52

Flipboard

MARCH 7, 2023

The following sections explain each of four environment customization approaches in detail, provide hands-on examples, and recommend use cases for each option. Check that the SageMaker image selected is a Conda-supported first-party kernel image such as “Data Science.” Choose Open Launcher.

Python 93

Python Automation Data Scientist ML 93

Heartbeat

NOVEMBER 28, 2023

MLOps is a set of principles and practices that combine software engineering, data science, and DevOps to ensure that ML models are deployed and managed effectively in production. MLOps encompasses the entire ML lifecycle, from data preparation to model deployment and monitoring. This is where MLOps comes in.

Machine Learning 52

Machine Learning ML Deep Learning Neural Network 52

Heartbeat

MARCH 19, 2024

It involves establishing a standard workflow for training LLMs, fine-tuning (hyper) parameters, deploying them, and collecting and analyzing data (aka response monitoring). This is, in fact, a baseline, and the actual LLMOps workflow usually involves more stakeholders like prompt engineers, researchers, etc.

Large Language Models 52

Large Language Models Prompt Engineer Prompt Engineering Natural Language Processing 52

AWS Machine Learning Blog

AUGUST 29, 2023

This architecture design represents a multi-account strategy where ML models are built, trained, and registered in a central model registry within a data science development account (which has more controls than a typical application development account). He is passionate about Statistics, NLP and Model Explainability in AI/ML.

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Data Scientist Data Quality Python ML 72

Pickl AI

MAY 15, 2024

Dreaming of a Data Science career but started as an Analyst? This guide unlocks the path from Data Analyst to Data Scientist Architect. So if you are looking forward to a Data Science career , this blog will work as a guiding light.

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Data Scientist Data Science Machine Learning Data Quality 52

The MLOps Blog

MAY 9, 2023

Model analysis and validation This component: 1 Gauges the model’s ability to generalize to unseen data. 2 Analyzes the model’s interpretability/explainability to help you understand the quality and biases of the model or models you plan to deploy. Is it a black-box model, or can the decisions be explained? Kale v0.7.0.

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ML Machine Learning Metadata Automation 98

Unite.AI

MARCH 21, 2024

After that, I worked for startups for a few years and then spent a decade at Palo Alto Networks, eventually becoming a VP responsible for development, QA, DevOps, and data science. Can you explain the concept of ‘data democracy' in the context of today's AI-driven business environment?

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DevOps Data Quality Machine Learning Generative AI 147

Mlearning.ai

JUNE 16, 2023

Advise on getting started on topics Recommend get started materials Explain an implementation Explain general concepts in specific industry domain (e.g. Basic programming tasks can at least be prepared, I am seeing the trend of AI-assisted developers. Here are some articles 1 , 2 , 3. New developers should learn basic concepts (e.g.

Software Development 52

Software Development Generative AI ChatGPT Prompt Engineer 52

The MLOps Blog

DECEMBER 28, 2022

Hence, the ML teams must have a mix of strong data architects and engineering experts that can successfully operationalize the ML model. MLOps cycle | Source How to organize ML team Centralized ML team People from different fields like engineering, product, DevOps, and ML all come together under one big team.

ML 78

ML Data Scientist ML Engineer Machine Learning 78

The MLOps Blog

MARCH 21, 2023

By storing all model-training-related artifacts, your data scientists will be able to run experiments and update models iteratively. Versioning Your data science team will benefit from using good MLOps practices to keep track of versioning, particularly when conducting experiments during the development stage.

Machine Learning 69

Machine Learning Data Scientist ML Metadata 69

AWS Machine Learning Blog

JANUARY 13, 2023

TR’s AI Platform microservices are built with Amazon SageMaker as the core engine, AWS serverless components for workflows, and AWS DevOps services for CI/CD practices. Data service. A traditional ML project lifecycle starts with finding data. Bring a single pane of glass for ML activities. Conclusion.

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ML Data Scientist Machine Learning Metadata 75