Data Science Dojo

AUGUST 24, 2023

ML models have grown significantly in recent years, and businesses increasingly rely on them to automate and optimize their operations. However, managing ML models can be challenging, especially as models become more complex and require more resources to train and deploy. What is MLOps?

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Heartbeat

FEBRUARY 23, 2023

Motivation I was motivated to write this article because I found the current resources out there a bit nebulous. For example, we can test how many layers of convolution and pooling are optimal for a CNN, or we can optimize our design with different types and shapes of kernels.

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Mlearning.ai

MAY 27, 2023

Data scientists can create less complicated code to understand, debug, and maintain if they adhere to coding standards, write modular and reusable routines, and include error-handling techniques. The need to test code to verify its correctness and durability is emphasized in software engineering.

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AWS Machine Learning Blog

MARCH 1, 2023

Flywheel creates a data lake (in Amazon S3) in your account where all the training and test data for all versions of the model are managed and stored. This post demonstrates how you can build a custom text classifier (no prior ML knowledge needed) that can assign a specific label to a given text.

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DrivenData Labs

DECEMBER 13, 2022

split (str): indicates whether the row is part of the train set or the test set. Metadata is provided for all points in the train and test sets. The geographic points in the train and test sets are distinct. name == "United States of America" ]. Final score will be the average across the four US regions.

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The MLOps Blog

MARCH 28, 2023

And because it takes more than technologies and processes to succeed with MLOps, he will also share details on: 1 Brainly’s ML use cases, 2 MLOps culture, 3 Team structure, 4 And technologies Brainly uses to deliver AI services to its clients, Enjoy the article! Multiple AI teams also contribute to ML infrastructure initiatives.

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Heartbeat

FEBRUARY 29, 2024

Over the years, we’ve witnessed significant milestones in programming languages, each bringing about transformative changes in how we write code and build software systems. We’ll explore how LLMs reshape how we write code, their potential for increasing developer productivity, and what lies beyond LLMs in the ever-evolving programming world.

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Heartbeat

AUGUST 15, 2023

CNNs are particularly useful for handwritten recognition because they can handle writing style and size variations. Additionally, they offer tools for visualizing and analyzing the performance of the model during training and testing. Overall, CNNs have great potential for improving the accuracy of handwritten recognition using Python.

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Heartbeat

DECEMBER 7, 2023

I decided to write a series of blogs on current topics: the elements of data governance that I have been thinking about, reading, and following for a while. It must be rigorously tested to ensure quality, reliability, and interoperability. Just as we call the “factory of the universe,” the smallest consistent unit.

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AWS Machine Learning Blog

MAY 3, 2023

For any machine learning (ML) problem, the data scientist begins by working with data. Feature engineering refers to the process where relevant variables are identified, selected, and manipulated to transform the raw data into more useful and usable forms for use with the ML algorithm used to train a model and perform inference against it.

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AWS Machine Learning Blog

MARCH 27, 2024

Finally, we show you how to test and deploy your fine-tuned model with Provisioned Throughput. For testing your fine-tuned model before deployment, set model units to a value of 1 and set the commitment term to No commitment. Dani Mitchell is an AI/ML Specialist Solutions Architect at Amazon Web Services.

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AWS Machine Learning Blog

OCTOBER 20, 2023

Customers of every size and industry are innovating on AWS by infusing machine learning (ML) into their products and services. Recent developments in generative AI models have further sped up the need of ML adoption across industries.

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Mlearning.ai

JUNE 29, 2023

0.02)) else: layer.trainable = False In addition, we will apply funneling to the model by adding 3 dense layers with iterative drops in unit size to help reduce overfitting, improve the performance on test data, and make the training more efficient. convert('L') # Convert image to grayscale img_pixels = np.array(img).reshape(-1).astype('int')

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Mlearning.ai

FEBRUARY 16, 2023

Machine Learning Integration Opportunities Organizations harness machine learning (ML) algorithms to make forecasts on the data. ML models, in turn, require significant volumes of adequate data to ensure accuracy. Besides, the platform provides all the required integrations to help engineers prepare data and build ML models.

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AWS Machine Learning Blog

NOVEMBER 29, 2023

Amazon SageMaker Studio provides a fully managed solution for data scientists to interactively build, train, and deploy machine learning (ML) models. With this launch, you can programmatically run notebooks as jobs using APIs provided by Amazon SageMaker Pipelines , the ML workflow orchestration feature of Amazon SageMaker.

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AWS Machine Learning Blog

FEBRUARY 29, 2024

Creating scalable and efficient machine learning (ML) pipelines is crucial for streamlining the development, deployment, and management of ML models. Configuration files (YAML and JSON) allow ML practitioners to specify undifferentiated code for orchestrating training pipelines using declarative syntax.

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IBM Journey to AI blog

JULY 19, 2023

Code creation: Code co-pilot, code conversion, create technical documentation, test cases and more. Once fine tuned, the deployed model was used to inference on a test data to create the AE narratives (see Figure 2 for a sample). More than half of algorithms on the U.S.

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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. What does a modern technology stack for streamlined ML processes look like?

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AWS Machine Learning Blog

MARCH 1, 2023

This combination of great models and continuous adaptation is what will lead to a successful machine learning (ML) strategy. MLOps focuses on the intersection of data science and data engineering in combination with existing DevOps practices to streamline model delivery across the ML development lifecycle.

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The MLOps Blog

JUNE 27, 2023

Knowledge and skills in the organization Evaluate the level of expertise and experience of your ML team and choose a tool that matches their skill set and learning curve. Model monitoring and performance tracking : Platforms should include capabilities to monitor and track the performance of deployed ML models in real-time.

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Mlearning.ai

FEBRUARY 16, 2023

How to effectively safeguard your ML experiments Photo by Clément Hélardot on Unsplash Many years ago… I was staring at my screen, scrutinizing every little wiggle on my Tensorboard. What are your tests telling you?” But I hadn’t comprehensively tested it. You write the instructions/rules for the computer to follow.

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AWS Machine Learning Blog

OCTOBER 16, 2023

As an AI-powered solution, Veriff needs to create and run dozens of machine learning (ML) models in a cost-effective way. This approach was initially used for all company services, including microservices that run expensive computer vision ML models. Some of these models required deployment on GPU instances.

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Google Research AI blog

MAY 31, 2023

It improves bit by bit, one little step at a time — editing, running unit tests, fixing build errors, addressing code reviews, editing some more, appeasing linters , and fixing more errors — until finally it becomes good enough to merge into a code repository.

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AWS Machine Learning Blog

SEPTEMBER 14, 2023

This has the advantage of writing the code in a more natural, object-oriented way, and still uses SageMaker capabilities to run training jobs on a remote cluster with minimal changes. Continue writing code in your choice of IDE and invoke SageMaker training jobs. No need to switch Integrated development environments (IDEs).

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AWS Machine Learning Blog

FEBRUARY 21, 2023

After you build, train, and evaluate your machine learning (ML) model to ensure it’s solving the intended business problem proposed, you want to deploy that model to enable decision-making in business operations. A/B testing is used in scenarios where closed loop feedback can directly tie model outputs to downstream business metrics.

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The MLOps Blog

MAY 31, 2023

And eCommerce companies have a ton of use cases where ML can help. The problem is, with more ML models and systems in production, you need to set up more infrastructure to reliably manage everything. And because of that, many companies decide to centralize this effort in an internal ML platform. But how to build it?

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AWS Machine Learning Blog

DECEMBER 5, 2023

The IAM role used by Amazon Comprehend custom classification and custom entity recognition has permissions to access the dataset from the test bucket only. You can prepare separate training and testing datasets for Amazon Comprehend custom classifier training and model evaluation. You can then adjust the throughput up or down.

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AWS Machine Learning Blog

DECEMBER 13, 2023

Deploy a fine-tuned Model on Inf2 using Amazon SageMaker AWS Inferentia2 is purpose-built machine learning (ML) accelerator designed for inference workloads and delivers high-performance at up to 40% lower cost for generative AI and LLM workloads over other inference optimized instances on AWS.

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AWS Machine Learning Blog

NOVEMBER 22, 2023

For example, development and test environments for your IDP solution are typically only used for 8 hours a day during the work week. By stopping development and test environment resources when not in use, such as outside of the 40 working hours per week, you can reduce costs by 75% compared to running them continuously for 168 hours per week.

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AWS Machine Learning Blog

MARCH 14, 2024

Beyond hardware, data cleaning and processing, model architecture design, hyperparameter tuning, and training pipeline development demand specialized machine learning (ML) skills. You can privately customize FMs with your own data through a visual interface without writing any code. This makes the output more interesting and grounded.

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AWS Machine Learning Blog

NOVEMBER 30, 2023

It also integrates with Machine Learning and Operation (MLOps) workflows in Amazon SageMaker to automate and scale the ML lifecycle. The Prompt Template is where you can tune and shape your prompt to test different templates as you would like. About the authors Ram Vegiraju is a ML Architect with the SageMaker Service team.

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AWS Machine Learning Blog

APRIL 12, 2023

Unplanned downtime of a baggage handling system, whether be it a conveyor belt, carousel, or sorter unit, can disrupt airport operations. Amazon Lookout for Equipment analyzes data from equipment sensors to create an ML model automatically for your equipment based on asset specific data—no data science skills necessary.

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The MLOps Blog

AUGUST 4, 2023

Situations described above arise way too often in ML teams, and their consequences vary from a single developer’s annoyance to the team’s inability to ship their code as needed. Let’s dive into the world of monorepos, an architecture widely adopted in major tech companies like Google, and how they can enhance your ML workflows.

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The MLOps Blog

AUGUST 3, 2023

This article was originally an episode of the ML Platform Podcast , a show where Piotr Niedźwiedź and Aurimas Griciūnas, together with ML platform professionals, discuss design choices, best practices, example tool stacks, and real-world learnings from some of the best ML platform professionals. Stefan: Yeah.

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AWS Machine Learning Blog

JANUARY 13, 2023

Machine learning (ML) presents an opportunity to address some of these concerns and is being adopted to advance data analytics and derive meaningful insights from diverse HCLS data for use cases like care delivery, clinical decision support, precision medicine, triage and diagnosis, and chronic care management.

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The MLOps Blog

APRIL 20, 2023

We’ll dive deep into the essential lessons, tried-and-tested techniques, and real-world examples that will help you tackle the unique challenges you expect to face as a Computer Vision Engineer. Model conversion, deployment setup, testing, and maintenance Successfully deploying a computer vision model involves several key steps.

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AWS Machine Learning Blog

APRIL 10, 2023

You don’t have to be an expert in machine learning (ML) to appreciate the value of large language models (LLMs). ML practitioners keep improving the accuracy and capabilities of these models. Hardware: Inferentia AWS Inferentia, specifically designed for inference by AWS, is a high-performance and low-cost ML inference accelerator.

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