Heartbeat

OCTOBER 23, 2023

The Role of Data Scientists and ML Engineers in Health Informatics At the heart of the Age of Health Informatics are data scientists and ML engineers who play a critical role in harnessing the power of data and developing intelligent algorithms.

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ODSC - Open Data Science

MARCH 27, 2023

ML Governance: A Lean Approach Ryan Dawson | Principal Data Engineer | Thoughtworks Meissane Chami | Senior ML Engineer | Thoughtworks During this session, you’ll discuss the day-to-day realities of ML Governance. Some of the questions you’ll explore include How much documentation is appropriate?

Machine Learning 98

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Pickl AI

JANUARY 29, 2024

Data Science extracts insights, while Machine Learning focuses on self-learning algorithms. Key takeaways Data Science lays the groundwork for Machine Learning, providing curated datasets for ML algorithms to learn and make predictions. Emphasises programming skills, understanding of algorithms, and expertise in Data Analysis.

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AWS Machine Learning Blog

FEBRUARY 12, 2024

The Ranking team at Booking.com plays a pivotal role in ensuring that the search and recommendation algorithms are optimized to deliver the best results for their users. Essential ML capabilities such as hyperparameter tuning and model explainability were lacking on premises. AWS_ACCOUNT] region = eu-central-1.

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ODSC - Open Data Science

FEBRUARY 24, 2023

Apache Superset GitHub | Website Apache Superset is a must-try project for any ML engineer, data scientist, or data analyst. Algorithm-visualizer GitHub | Website Algorithm Visualizer is an interactive online platform that visualizes algorithms from code. This tool automatically detects problems in an ML dataset.

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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. Conclusion Can’t wait to start learning from these incredible speakers and experts?

Machine Learning 52

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TheSequence

MARCH 10, 2024

We are witnessing glimpses of the potential impact of 'AI for science' with models such as those discovering new computer science and math algorithms, or the famous AlphaFold, which is actively used for discovering new proteins. Can AI help explain the universe? Is AI going to discover everything?

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Chatbots Life

MAY 12, 2023

As everything is explained from scratch but extensively I hope you will find it interesting whether you are NLP Expert or just want to know what all the fuss is about. We will discuss how models such as ChatGPT will affect the work of software engineers and ML engineers. and we will also explain how GPT can create jobs.

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O'Reilly Media

JANUARY 17, 2023

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. You know the drill: pull some data, carve it up into features, feed it into one of scikit-learn’s various algorithms. Building Models.

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Pickl AI

JULY 14, 2023

Evolution of AI The evolution of Artificial Intelligence (AI) spans several decades and has witnessed significant advancements in theory, algorithms, and applications. RL algorithms, such as Deep Q-Networks (DQNs) and AlphaGo, demonstrated significant accomplishments in game playing and control tasks.

Neural Network 52

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AWS Machine Learning Blog

NOVEMBER 30, 2023

Verifiable evaluation scores are provided across text generation, summarization, classification and question answering tasks, including customer-defined prompt scenarios and algorithms. It also integrates with Machine Learning and Operation (MLOps) workflows in Amazon SageMaker to automate and scale the ML lifecycle. What is FMEval?

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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. Switching gears, imagine yourself being part of a high-tech research lab working with Machine Learning algorithms.

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The MLOps Blog

NOVEMBER 14, 2023

Visualizing deep learning models can help us with several different objectives: Interpretability and explainability: The performance of deep learning models is, at times, staggering, even for seasoned data scientists and ML engineers. Data scientists and ML engineers: Creating and training deep learning models is no easy feat.

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AWS Machine Learning Blog

MAY 9, 2024

Responsible AI Organizations can navigate potential ethical dilemmas associated with generative AI by incorporating considerations such as fairness, explainability, privacy and security, robustness, governance, and transparency. Setting KPIs and metrics is pivotal to gauge effectiveness.

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The MLOps Blog

JUNE 27, 2023

Learn more The Best Tools, Libraries, Frameworks and Methodologies that ML Teams Actually Use – Things We Learned from 41 ML Startups [ROUNDUP] Key use cases and/or user journeys Identify the main business problems and the data scientist’s needs that you want to solve with ML, and choose a tool that can handle them effectively.

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AWS Machine Learning Blog

MARCH 22, 2023

Behind the scenes, Rekognition Custom Labels automatically loads and inspects the training data, selects the right ML algorithms, trains a model, and provides model performance metrics. The workflow allows application developers and ML engineers to automate the custom label classification steps for any computer vision use case.

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Heartbeat

SEPTEMBER 18, 2023

Unleashing Innovation and Success: Comet — The Trusted ML Platform for Enterprise Environments Machine learning (ML) is a rapidly developing field, and businesses are increasingly depending on ML platforms to fuel innovation, improve efficiency, and mine data for insights. We pay our contributors, and we don’t sell ads.

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AWS Machine Learning Blog

JANUARY 5, 2024

The customer used this pipeline for small and medium scale models, which included using various types of open-source algorithms. One of the key benefits of SageMaker is that various types of algorithms can be brought into SageMaker and deployed using a bring your own container (BYOC) technique.

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The MLOps Blog

DECEMBER 28, 2022

Organization Gigaforce Inc Industry InsurTech provider Team size Gigaforce built an ML team three years ago in 2020 and has a team size of 5-7. Team composition The team comprises domain experts, data engineers, data scientists, and ML engineers.

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Heartbeat

FEBRUARY 1, 2023

Machine Learning Operations (MLOps) are the aspects of ML that deal with the creation and advancement of these models. In this article, we’ll learn everything there is to know about these operations and how ML engineers go about performing them. What is MLOps? This contributes to models being deployed quickly and consistently.

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Heartbeat

OCTOBER 30, 2023

Ideally, the responsibilities of the ML engineering team should be completed once the model is deployed. But, as a data scientist or an ML engineer, you focus on the solutions rather than problems, right? We're committed to supporting and inspiring developers and engineers from all walks of life.

Machine Learning 52

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Viso.ai

FEBRUARY 11, 2024

Tutorials and explainers can also be helpful. To recap, Detectron2 uses a specific kind of region-based CNN with algorithmic tools to process objects in an image. Part of what’s exciting about Detectron2 is the still-emerging capability to use these segmentation tools on your data.

Computer Vision 52

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AWS Machine Learning Blog

APRIL 26, 2023

The first is by using low-code or no-code ML services such as Amazon SageMaker Canvas , Amazon SageMaker Data Wrangler , Amazon SageMaker Autopilot , and Amazon SageMaker JumpStart to help data analysts prepare data, build models, and generate predictions. Determine the ML algorithm, if known or possible.

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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. Data preprocessing.

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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

MARCH 14, 2023

Read more How to Solve Reproducibility in ML Machine learning reproducibility checklist: solutions we adapted As ML engineers we make sure that every problem should have one or multiple possible solutions, as is the case for ML reproducibility challenges. neptune.ai | Source Tools like neptune.ai , Comet , MLFlow , etc.

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AWS Machine Learning Blog

NOVEMBER 9, 2023

We build a model to predict the severity (benign or malignant) of a mammographic mass lesion trained with the XGBoost algorithm using the publicly available UCI Mammography Mass dataset and deploy it using the MLOps framework. Machine Learning Engineer with AWS Professional Services.

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The MLOps Blog

MAY 10, 2023

When working on real-world machine learning (ML) use cases, finding the best algorithm/model is not the end of your responsibilities. Note: The focus of this article is not to show you how you can create the best ML model but to explain how effectively you can save trained models. values y = dataset.iloc[:, 4 ].values

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The MLOps Blog

JULY 18, 2023

Sabine: Right, so, Jason, to kind of warm you up a bit… In 1 minute, how would you explain conversational AI? Then we subsequently try to run audio fingerprinting type algorithms on top of it so that we can actually identify specifically who those people are if we’ve seen them in the past. Jason: Yeah, that’s really true.

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The MLOps Blog

MARCH 15, 2023

ML model explainability: Make sure the ML model is interpretable and understandable by the developers as well as other stakeholders and that the value addition provided can be easily quantified. For an experienced Data Scientist/ML engineer, that shouldn’t come as so much of a problem.

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The MLOps Blog

DECEMBER 29, 2022

How would you explain deploying models on GPU in one minute? I could give you an example if you were to go build out serverless GPUs I can explain how your next three months would basically be. Does it mean that the production code has to be rewritten by, for example, ML engineers manually to be optimized for GPU with each update?

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