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

FEBRUARY 24, 2023

Its goal is to help with a quick analysis of target characteristics, training vs testing data, and other such data characterization tasks. Apache Superset GitHub | Website Apache Superset is a must-try project for any ML engineer, data scientist, or data analyst.

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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. Which one is right for you depends on your goal.

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AWS Machine Learning Blog

NOVEMBER 14, 2024

Model governance involves overseeing the development, deployment, and maintenance of ML models to help ensure that they meet business objectives and are accurate, fair, and compliant with regulations.

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Heartbeat

OCTOBER 23, 2023

Revolutionizing Healthcare through Data Science and Machine Learning Image by Cai Fang on Unsplash Introduction In the digital transformation era, healthcare is experiencing a paradigm shift driven by integrating data science, machine learning, and information technology.

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Snorkel AI

OCTOBER 27, 2023

Instead of exclusively relying on a singular data development technique, leverage a variety of techniques such as promoting, RAG, and fine-tuning for the most optimal outcome. Focus on improving data quality and transforming manual data development processes into programmatic operations to scale fine-tuning.

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AWS Machine Learning Blog

APRIL 26, 2023

You may have gaps in skills and technologies, including operationalizing ML solutions, implementing ML services, and managing ML projects for rapid iterations. Ensuring data quality, governance, and security may slow down or stall ML projects. We recognize that customers have different starting points.

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AWS Machine Learning Blog

JANUARY 17, 2024

Solution overview As mentioned earlier, the AWS services that you can use for analysis of mobility data are Amazon S3, Amazon Macie, AWS Glue, S3 Object Lambda, Amazon Comprehend, and Amazon SageMaker geospatial capabilities. Data scientists can accomplish this process by connecting through Amazon SageMaker notebooks.

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Snorkel AI

OCTOBER 27, 2023

Instead of exclusively relying on a singular data development technique, leverage a variety of techniques such as promoting, RAG, and fine-tuning for the most optimal outcome. Focus on improving data quality and transforming manual data development processes into programmatic operations to scale fine-tuning.

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TensorFlow

MARCH 10, 2023

It can also include constraints on the data, such as: Minimum and maximum values for numerical columns Allowed values for categorical columns. Before a model is productionized, the Contract is agreed upon by the stakeholders working on the pipeline, such as the ML Engineers, Data Scientists and Data Owners.

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Pickl AI

NOVEMBER 28, 2024

Fundamental Programming Skills Strong programming skills are essential for success in ML. This section will highlight the critical programming languages and concepts ML engineers should master, including Python, R , and C++, and an understanding of data structures and algorithms. during the forecast period.

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Snorkel AI

JUNE 30, 2023

And usually what ends up happening is that some poor data scientist or ML engineer has to manually troubleshoot this in a Jupyter Notebook. So this path on the right side of the production icon is what we’re calling ML observability. We have four pillars that we use when thinking about ML observability.

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Snorkel AI

JUNE 30, 2023

And usually what ends up happening is that some poor data scientist or ML engineer has to manually troubleshoot this in a Jupyter Notebook. So this path on the right side of the production icon is what we’re calling ML observability. We have four pillars that we use when thinking about ML observability.

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Snorkel AI

OCTOBER 27, 2023

Instead of exclusively relying on a singular data development technique, leverage a variety of techniques such as promoting, RAG, and fine-tuning for the most optimal outcome. Focus on improving data quality and transforming manual data development processes into programmatic operations to scale fine-tuning.

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LLM Data Scientist Machine Learning Large Language Models 52

Mlearning.ai

MAY 23, 2023

It’s critical for beginners learn this, since it affects everything: workflows, data quality requirements, etc. Model mindset prioritizes the ML model that you are building. While product mindset focuses on the end data product: the minimum viable product. There are two approaches we see in MLOps. What is the Difference?

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Snorkel AI

JUNE 9, 2023

Transforming the Customer Experience with AI: Wayfair’s Data-Centric Way Wayfair ’s Archana Sapkota (ML Manager) and Vinny DeGenova (Associate Director of Machine Learning) shared insights on transforming the customer experience with AI, emphasizing the use of ML in understanding customers and catalog products.

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Snorkel AI

JUNE 9, 2023

Transforming the Customer Experience with AI: Wayfair’s Data-Centric Way Wayfair ’s Archana Sapkota (ML Manager) and Vinny DeGenova (Associate Director of Machine Learning) shared insights on transforming the customer experience with AI, emphasizing the use of ML in understanding customers and catalog products.

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Snorkel AI

JUNE 30, 2023

And usually what ends up happening is that some poor data scientist or ML engineer has to manually troubleshoot this in a Jupyter Notebook. So this path on the right side of the production icon is what we’re calling ML observability. We have four pillars that we use when thinking about ML observability.

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Snorkel AI

OCTOBER 12, 2023

Data-Driven Government: A Fireside Chat with the Former U.S. Chief Data Scientist In this fireside chat as Snorkel AI CEO and co-founder Alex Ratner and DJ Patil, the Former U.S. Chief Data Scientist dive into data science’s history, impact, and challenges in the United States government.

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Heartbeat

OCTOBER 30, 2023

Ensuring Long-Term Performance and Adaptability of Deployed Models Source: [link] Introduction When working on any machine learning problem, data scientists and machine learning engineers usually spend a lot of time on data gathering , efficient data preprocessing , and modeling to build the best model for the use case.

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Snorkel AI

OCTOBER 12, 2023

Data-Driven Government: A Fireside Chat with the Former U.S. Chief Data Scientist In this fireside chat as Snorkel AI CEO and co-founder Alex Ratner and DJ Patil, the Former U.S. Chief Data Scientist dive into data science’s history, impact, and challenges in the United States government.

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The MLOps Blog

MAY 17, 2023

From data processing to quick insights, robust pipelines are a must for any ML system. Often the Data Team, comprising Data and ML Engineers , needs to build this infrastructure, and this experience can be painful. However, efficient use of ETL pipelines in ML can help make their life much easier.

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Snorkel AI

OCTOBER 12, 2023

Data-Driven Government: A Fireside Chat with the Former U.S. Chief Data Scientist In this fireside chat as Snorkel AI CEO and co-founder Alex Ratner and DJ Patil, the Former U.S. Chief Data Scientist dive into data science’s history, impact, and challenges in the United States government.

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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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Pickl AI

JUNE 6, 2024

Data scrubbing is often used interchangeably but there’s a subtle difference. Cleaning is broader, improving data quality. This is a more intensive technique within data cleaning, focusing on identifying and correcting errors. Data scrubbing is a powerful tool within this cleaning service.

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Viso.ai

JANUARY 30, 2024

During machine learning model training, there are seven common errors that engineers and data scientists typically run into. This is a bigger deal with raw or unstructured data that engineers and developers might be using to feed the machine learning program. 6: Data Drift What is Data Drift?

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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. This includes data quality, privacy, and compliance.

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Snorkel AI

JULY 5, 2023

Organizations struggle in multiple aspects, especially in modern-day data engineering practices and getting ready for successful AI outcomes. One of them is that it is really hard to maintain high data quality with rigorous validation. More features mean more data consumed upstream. Catch the sessions you missed!

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Snorkel AI

JULY 5, 2023

Organizations struggle in multiple aspects, especially in modern-day data engineering practices and getting ready for successful AI outcomes. One of them is that it is really hard to maintain high data quality with rigorous validation. More features mean more data consumed upstream. Catch the sessions you missed!

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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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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

AUGUST 11, 2023

Getting a workflow ready which takes your data from its raw form to predictions while maintaining responsiveness and flexibility is the real deal. At that point, the Data Scientists or ML Engineers become curious and start looking for such implementations.

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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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Towards AI

NOVEMBER 8, 2024

. — Peter Norvig, The Unreasonable Effectiveness of Data. Edited Photo by Taylor Vick on Unsplash In ML engineering, data quality isn’t just critical — it’s foundational. Since 2011, Peter Norvig’s words underscore the power of a data-centric approach in machine learning. Using biased or low-quality data?

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The MLOps Blog

JANUARY 26, 2024

I started my ML journey as an analyst back in 2016. Since then, I’ve worked as a data scientist for a multinational company and an MLOps engineer for an early-stage startup before moving to Mailchimp in May 2021. Technical projects must be aligned with business objectives. This was my team.)

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AWS Machine Learning Blog

NOVEMBER 13, 2024

With the unification of SageMaker Model Cards and SageMaker Model Registry, architects, data scientists, ML engineers, or platform engineers (depending on the organization’s hierarchy) can now seamlessly register ML model versions early in the development lifecycle, including essential business details and technical metadata.

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