Tag: Deploying

  • Report: Deploying police was ‘reasonable,’ but UMass Amherst leaders could have chosen other responses


    A report released by the University of Massachusetts, Amherst Thursday looked at how and why the school responded with a heavy police presence during campus demonstrations against the war in Gaza last year.

    The inquiry was conducted by attorneys at Prince Lobel, a Boston-based law firm hired by the University of Massachusetts.

    According to the report, UMass Amherst Chancellor Javier Reyes’s concerns were connected to safety and precedence.

    Investigators found Reyes’s decision to disband the protest using police “reasonable,” but the attorneys also questioned if the chancellor could have taken another approach.

    The bulk of the report tells the story of two protests, April 29 and May 7, 2024. It refers to encampments students set up on the South Lawn of the campus, and the events leading up to the encampments from the perspectives of the primary constituencies involved.

    The report states, “These detailed (and often, very personal) accounts cannot be fairly summarized in a few paragraphs, and we do not attempt to do so here. Rather, in this executive summary, we provide an overview of our findings and conclusions, which are narrowly tailored to answer the main questions of our charge: 1) what was the basis of the administration’s decision to direct law enforcement to remove the May 7 encampment; and 2) was that decision reasonable and prudent?”

    The May 7, 2024 protest ended with the arrest of 134 people.

    At the time, school administrators said the issue was not that students were protesting; it was the structures the set up on the protest site, like fencing, even after they were told by school officials to remove them.

    “[The school’s] assessment of the risks, especially of violence, was understandable based on the information it had about the encampments on its own campus and the chaos that was engulfing some other college campuses where encampments had been established,” the report said.

    Based on interviews with students and school leaders, the attorneys who wrote the report query if the protest have ended on its own.

    “Even if (as we have found) the chancellor’s decision was reasonable, there is still a question of whether another road should have been taken,” the report said.

    Administrators at the school pursued several methods of “de-escalation,” the report said, “all with the aim of having the encampment organizers immediately undertake the disassembly of the encampment.”

    School officials might have adopted a “wait and see” approach the report said. It would have given the chancellor and others the opportunity to assess whether events were moving in a positive or negative direction.

    The attorneys said they were struck by the analysis of one of the protest leaders, who felt the chancellor had an easier way out by simply letting the May 7 encampment remain.

    “When we asked her if the encampment had a “natural end point,” she quickly replied, ‘Oh my God, yes, finals were next week,’” the report said.

    In her estimation the encampment may have just “petered out.” This assessment was shared by a UMass Police Department official.

    Timing also contributed to a large and visible police force.

    At one point on May 7, Chancellor Reyes agreed to engage in a direct, face-to-face meeting with student protesters. It was not factored in to the school’s strategic plan for police to remove the encampment if other methods failed, and the meeting delayed a final warning to protestors.

    “As a result of the delay, a very large and visible police force was positioned behind [a building] for an extended period, which became known to the protesters, who themselves called for reinforcements,” the report said.

    By the time the police did intervene a little after 7p.m., “there were many more protesters and other people present, and the situation was highly fraught and complex. As the crowd grew, the [Massachusetts State Police] commanders on site called for more State Troopers. The State Troopers became the leading edge of the police enforcement of the Administration’s decision.”

    All of this — the delay, the assemblage of more protesters and police and a lack of communication between the chancellor and law enforcement — “likely resulted in many more arrests than would have occurred otherwise,” the report concludes.

    Those interviewed by the attorneys said they recognized that damage has been done; the trust between many students and the institution has been eroded.


    This story is developing and will be updated. It is a production of the New England News Collaborative and was originally published by New England Public Media.

    Disclosure: The license for NEPM’s main radio signal is held by UMass Amherst. The newsroom operates independently.



    In a recent report by an independent review panel, it was determined that the decision to deploy police during a student protest at UMass Amherst was deemed “reasonable.” However, the report also highlighted that campus leaders could have chosen other responses to de-escalate the situation.

    The panel found that the police response was appropriate given the circumstances, which included reports of violence and property damage during the protest. However, they also noted that UMass Amherst leaders could have explored alternative approaches to address the concerns of the protesters and prevent the situation from escalating.

    The report emphasized the importance of open communication and collaboration between campus leaders, students, and law enforcement to prevent similar incidents in the future. It also recommended that UMass Amherst implement training programs and protocols for handling protests and other campus events to ensure a peaceful and safe environment for all members of the community.

    Overall, the report serves as a valuable learning opportunity for UMass Amherst to reflect on their response to the protest and to consider alternative approaches in the future. By fostering a culture of dialogue and understanding, campus leaders can work towards building a more inclusive and respectful community for all.

    Tags:

    1. UMass Amherst police deployment
    2. Campus security response
    3. Police intervention report
    4. UMass Amherst incident analysis
    5. Law enforcement decision making
    6. Campus safety assessment
    7. UMass Amherst crisis management
    8. Police deployment evaluation
    9. Campus security review
    10. UMass Amherst leadership response

    #Report #Deploying #police #reasonable #UMass #Amherst #leaders #chosen #responses

  • A Step-by-Step Guide to Building and Deploying Large Language Models: An Engineer’s Handbook

    A Step-by-Step Guide to Building and Deploying Large Language Models: An Engineer’s Handbook


    Building and deploying large language models can be a daunting task for engineers, especially with the rise of complex AI technologies like GPT-3 and BERT. However, with the right approach and tools, it can be a manageable and rewarding project. In this article, we will provide a step-by-step guide to help engineers navigate the process of building and deploying large language models effectively.

    Step 1: Define the Problem Statement

    Before starting to build a large language model, it is essential to clearly define the problem statement and the objectives of the project. This will help guide the development process and ensure that the model is tailored to meet the specific needs of the application.

    Step 2: Gather and Preprocess Data

    The next step is to gather the necessary data for training the language model. This can include text data from various sources such as books, articles, and online content. Once the data is collected, it needs to be preprocessed to remove any noise, standardize the text format, and tokenize the data for training.

    Step 3: Select a Model Architecture

    There are several pre-trained language models available, such as GPT-3, BERT, and Transformer, that can be fine-tuned for specific applications. Engineers should carefully evaluate these models and select the one that best fits the requirements of their project.

    Step 4: Train the Model

    Training a large language model requires significant computational resources and time. Engineers can use tools like TensorFlow or PyTorch to train the model on a GPU or TPU to speed up the process. It is also important to monitor the training process and fine-tune the hyperparameters to optimize the model’s performance.

    Step 5: Evaluate the Model

    Once the model is trained, it is essential to evaluate its performance on a validation dataset to ensure that it meets the desired accuracy and efficiency metrics. Engineers can use metrics like perplexity, BLEU score, and F1 score to evaluate the model’s performance.

    Step 6: Deploy the Model

    After the model is trained and evaluated, it can be deployed to a production environment for use in real-world applications. Engineers can use frameworks like TensorFlow Serving or Flask to deploy the model as a REST API for easy integration with other systems.

    In conclusion, building and deploying large language models requires careful planning, data preprocessing, model selection, training, evaluation, and deployment. By following this step-by-step guide, engineers can successfully navigate the complexities of building and deploying large language models and create powerful AI applications that leverage the power of natural language processing.


    #StepbyStep #Guide #Building #Deploying #Large #Language #Models #Engineers #Handbook,llm engineerʼs handbook: master the art of engineering large language
    models from concept to production

  • Google Cloud Cookbook: Practical Solutions for Building and Deploying Clo – GOOD

    Google Cloud Cookbook: Practical Solutions for Building and Deploying Clo – GOOD



    Google Cloud Cookbook: Practical Solutions for Building and Deploying Clo – GOOD

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    Google Cloud Cookbook: Practical Solutions for Building and Deploying Clo

    Are you looking for a comprehensive guide on how to build and deploy applications on Google Cloud Platform? Look no further than the Google Cloud Cookbook!

    This cookbook is packed with practical solutions, tips, and best practices for developers and IT professionals who want to leverage the power of Google Cloud for their projects. From setting up a project and configuring resources to deploying applications and managing data, this cookbook covers it all.

    With clear, step-by-step instructions and real-world examples, you’ll learn how to make the most of Google Cloud’s tools and services. Whether you’re a beginner or an experienced cloud developer, this cookbook has something for everyone.

    So why wait? Get your copy of the Google Cloud Cookbook today and start building and deploying applications like a pro!
    #Google #Cloud #Cookbook #Practical #Solutions #Building #Deploying #Clo #GOOD, cloud computing

  • Wee Hyong Tok – Deep Learning with Azure   Building and Deploying Art – S9000z

    Wee Hyong Tok – Deep Learning with Azure Building and Deploying Art – S9000z



    Wee Hyong Tok – Deep Learning with Azure Building and Deploying Art – S9000z

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    In this post, we will explore the work of Wee Hyong Tok, a renowned expert in deep learning with Azure. We will delve into his latest project, Building and Deploying Art – S9000z, where he showcases the power of artificial intelligence in creating breathtaking artwork.

    Wee Hyong Tok’s project combines cutting-edge deep learning algorithms with Azure’s robust infrastructure to generate stunning, one-of-a-kind pieces of art. By training neural networks on vast datasets of images, he is able to teach the AI to recognize patterns and styles, ultimately producing unique and captivating artworks.

    Through his work, Wee Hyong Tok demonstrates the limitless possibilities of combining art and technology. By leveraging the capabilities of Azure, he is able to scale his projects and reach new levels of creativity and innovation.

    Follow along as we delve into the world of deep learning with Azure through the lens of Wee Hyong Tok and his groundbreaking project, Building and Deploying Art – S9000z. Get ready to be amazed by the intersection of art and artificial intelligence.
    #Wee #Hyong #Tok #Deep #Learning #Azure #Building #Deploying #Art #S9000z

  • Deploying ACI: The complete guide to planning, configuring, and managing: New

    Deploying ACI: The complete guide to planning, configuring, and managing: New



    Deploying ACI: The complete guide to planning, configuring, and managing: New

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    Are you looking to deploy Cisco’s Application Centric Infrastructure (ACI) in your organization but unsure where to start? Look no further! In this comprehensive guide, we will walk you through the entire process of planning, configuring, and managing your ACI deployment.

    First and foremost, it is essential to have a clear understanding of your organization’s requirements and goals for implementing ACI. This will help you determine the best approach for designing your ACI fabric and ensure that it meets the needs of your business.

    Next, you will need to carefully plan your ACI deployment, taking into account factors such as network topology, hardware requirements, and security considerations. It is crucial to work closely with your IT team and network administrators to ensure that your ACI fabric is configured correctly and optimized for performance.

    Once your ACI fabric is up and running, you will need to manage and monitor it to ensure that it continues to meet your organization’s needs. This includes tasks such as configuring policies, monitoring traffic flows, and troubleshooting any issues that may arise.

    By following this complete guide to planning, configuring, and managing your ACI deployment, you can ensure a smooth and successful implementation of this powerful networking solution. Stay tuned for more tips and best practices for deploying ACI in your organization!
    #Deploying #ACI #complete #guide #planning #configuring #managing, Cisco Cloud Computing

  • TensorFlow 2 Pocket Reference: Building and Deploying Machine Learning Models

    TensorFlow 2 Pocket Reference: Building and Deploying Machine Learning Models


    Price: $24.99 – $17.06
    (as of Dec 28,2024 04:28:12 UTC – Details)


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    Sharing the knowledge of experts

    O’Reilly’s mission is to change the world by sharing the knowledge of innovators. For over 40 years, we’ve inspired companies and individuals to do new things (and do them better) by providing the skills and understanding that are necessary for success.

    Our customers are hungry to build the innovations that propel the world forward. And we help them do just that.

    Publisher ‏ : ‎ O’Reilly Media; 1st edition (November 16, 2021)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 300 pages
    ISBN-10 ‏ : ‎ 1492089184
    ISBN-13 ‏ : ‎ 978-1492089186
    Item Weight ‏ : ‎ 6.7 ounces
    Dimensions ‏ : ‎ 4.25 x 0.54 x 7 inches


    Are you looking for a comprehensive guide to building and deploying machine learning models with TensorFlow 2? Look no further than the “TensorFlow 2 Pocket Reference”!

    This handy reference book covers everything you need to know about using TensorFlow 2, from the basics of building and training models to deploying them in production. Whether you’re a beginner just getting started with machine learning or an experienced practitioner looking to level up your skills, this book has something for everyone.

    With clear explanations, code examples, and best practices, “TensorFlow 2 Pocket Reference” will help you master the ins and outs of TensorFlow 2 in no time. So why wait? Pick up your copy today and start building and deploying powerful machine learning models with ease.
    #TensorFlow #Pocket #Reference #Building #Deploying #Machine #Learning #Models

  • Pretrain Vision and Large Language Models in Python: End-to-end techniques for building and deploying foundation models on AWS

    Pretrain Vision and Large Language Models in Python: End-to-end techniques for building and deploying foundation models on AWS


    Price: $53.19
    (as of Dec 27,2024 12:48:01 UTC – Details)




    ASIN ‏ : ‎ B0BFFDF4MQ
    Publisher ‏ : ‎ Packt Publishing; 1st edition (May 31, 2023)
    Publication date ‏ : ‎ May 31, 2023
    Language ‏ : ‎ English
    File size ‏ : ‎ 12078 KB
    Text-to-Speech ‏ : ‎ Enabled
    Screen Reader ‏ : ‎ Supported
    Enhanced typesetting ‏ : ‎ Enabled
    X-Ray ‏ : ‎ Not Enabled
    Word Wise ‏ : ‎ Not Enabled
    Print length ‏ : ‎ 258 pages


    Pretrain Vision and Large Language Models in Python: End-to-end techniques for building and deploying foundation models on AWS

    In this post, we will explore the process of pretraining vision and large language models in Python using end-to-end techniques. We will cover the steps involved in building and deploying foundation models on AWS, a popular cloud computing platform.

    Pretraining vision and language models has become an essential step in the development of cutting-edge AI applications. By pretraining these models on large datasets, we can leverage transfer learning to build more accurate and efficient models for a wide range of tasks.

    To get started, we will walk through the process of setting up a development environment in Python, including installing the necessary libraries and tools. We will then explore how to pretrain vision and language models using popular frameworks such as PyTorch and TensorFlow.

    Next, we will discuss best practices for fine-tuning pretrained models on custom datasets to improve performance and accuracy. We will cover techniques such as data augmentation, hyperparameter tuning, and model evaluation.

    Finally, we will demonstrate how to deploy our pretrained models on AWS using services such as Amazon SageMaker. We will walk through the steps involved in packaging our model for deployment, setting up a hosting environment, and serving predictions to end users.

    By the end of this post, you will have a solid understanding of how to pretrain vision and language models in Python and deploy them on AWS. Whether you are a seasoned AI practitioner or just getting started with machine learning, this post will provide you with the knowledge and tools you need to build and deploy state-of-the-art models for your own projects.
    #Pretrain #Vision #Large #Language #Models #Python #Endtoend #techniques #building #deploying #foundation #models #AWS

  • Cloud Native Devops With Kubernetes: Building, Deploying, and Scaling Modern…

    Cloud Native Devops With Kubernetes: Building, Deploying, and Scaling Modern…



    Cloud Native Devops With Kubernetes: Building, Deploying, and Scaling Modern…

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    Cloud Native Devops With Kubernetes: Building, Deploying, and Scaling Modern Applications

    In today’s fast-paced world of technology, businesses are constantly looking for ways to build, deploy, and scale their applications quickly and efficiently. Cloud native development has emerged as a key approach to achieving this goal, with Kubernetes being a popular platform for managing containerized applications.

    In this post, we will explore the key concepts of cloud native DevOps with Kubernetes, and how businesses can leverage this powerful combination to streamline their development processes and increase agility.

    Building Modern Applications with Kubernetes

    Kubernetes is an open-source platform that automates the deployment, scaling, and management of containerized applications. By using Kubernetes, developers can easily build, package, and deploy their applications in a consistent and reliable manner.

    With Kubernetes, developers can define the desired state of their applications using declarative configuration files, and Kubernetes will automatically manage the deployment and scaling of the application to ensure that it meets the specified requirements.

    Deploying Applications with Kubernetes

    One of the key benefits of using Kubernetes for application deployment is its ability to scale applications quickly and efficiently. Kubernetes can automatically scale applications based on resource usage, ensuring that the application can handle varying levels of traffic without manual intervention.

    Kubernetes also provides a number of features to help developers deploy applications securely, such as network policies, secrets management, and role-based access control. This helps to ensure that applications are deployed in a secure and reliable manner.

    Scaling Applications with Kubernetes

    In addition to deployment, Kubernetes also provides powerful scaling capabilities that allow developers to easily scale their applications up or down based on demand. Kubernetes can automatically detect changes in resource usage and scale applications accordingly, ensuring that applications are always running at optimal performance levels.

    By leveraging the scaling capabilities of Kubernetes, businesses can ensure that their applications can handle sudden spikes in traffic without affecting performance or reliability. This can help businesses to deliver a better user experience and increase customer satisfaction.

    In conclusion, cloud native DevOps with Kubernetes is a powerful approach to building, deploying, and scaling modern applications. By leveraging the capabilities of Kubernetes, businesses can streamline their development processes, increase agility, and deliver applications that are secure, reliable, and scalable.
    #Cloud #Native #Devops #Kubernetes #Building #Deploying #Scaling #Modern.., cloud computing

  • Programming PyTorch for Deep Learning: Creating and Deploying Deep Learning Appl

    Programming PyTorch for Deep Learning: Creating and Deploying Deep Learning Appl



    Programming PyTorch for Deep Learning: Creating and Deploying Deep Learning Appl

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    ications

    Are you interested in creating and deploying deep learning applications using PyTorch? Look no further! In this post, we will dive into the world of programming PyTorch for deep learning and explore how you can create and deploy powerful deep learning applications.

    PyTorch is a popular open-source machine learning library for Python that allows you to build and train deep learning models with ease. Whether you are a beginner or an experienced data scientist, PyTorch offers a flexible and intuitive platform for developing cutting-edge deep learning applications.

    To get started, you will need to install PyTorch on your machine. You can do this by following the installation instructions on the official PyTorch website. Once you have PyTorch installed, you can begin building your deep learning models.

    One of the key features of PyTorch is its dynamic computation graph, which allows you to define and modify your neural network architecture on the fly. This flexibility makes it easy to experiment with different model architectures and hyperparameters, leading to faster and more efficient model training.

    In addition to model development, PyTorch also provides tools for deploying your deep learning applications. Whether you want to deploy your model on a local machine, a cloud server, or a mobile device, PyTorch offers a range of deployment options to suit your needs.

    So, if you are ready to take your deep learning skills to the next level, consider programming PyTorch for deep learning. With its powerful features and flexible architecture, PyTorch is the perfect tool for creating and deploying cutting-edge deep learning applications.
    #Programming #PyTorch #Deep #Learning #Creating #Deploying #Deep #Learning #Appl

  • The Ultimate Guide To Chat GPT – Understanding, Training and Deploying A Speech Recognition Model: Discover the power of AI language models with “The Ultimate Guide to Chat GPT

    The Ultimate Guide To Chat GPT – Understanding, Training and Deploying A Speech Recognition Model: Discover the power of AI language models with “The Ultimate Guide to Chat GPT


    Price: $7.99
    (as of Dec 26,2024 16:35:52 UTC – Details)




    ASIN ‏ : ‎ B0BVDK4QBG
    Publication date ‏ : ‎ February 9, 2023
    Language ‏ : ‎ English
    File size ‏ : ‎ 495 KB
    Text-to-Speech ‏ : ‎ Enabled
    Screen Reader ‏ : ‎ Supported
    Enhanced typesetting ‏ : ‎ Enabled
    X-Ray ‏ : ‎ Not Enabled
    Word Wise ‏ : ‎ Enabled
    Print length ‏ : ‎ 136 pages


    In this comprehensive guide, we will explore everything you need to know about Chat GPT, a powerful speech recognition model powered by artificial intelligence. From understanding the basics of AI language models to training and deploying your own Chat GPT model, this guide will equip you with the knowledge and tools to leverage the power of AI in your projects.

    First, we will delve into the fundamentals of AI language models and how they work. We will explain the concept of natural language processing (NLP) and how AI models like Chat GPT are able to understand and generate human-like text. You will learn about the architecture of Chat GPT and how it processes input data to generate coherent and contextually relevant responses.

    Next, we will guide you through the process of training your own Chat GPT model. We will discuss the importance of selecting the right data for training, fine-tuning the model for specific tasks, and evaluating the performance of your model. You will learn how to optimize hyperparameters, handle overfitting, and troubleshoot common issues that may arise during training.

    Finally, we will show you how to deploy your Chat GPT model in real-world applications. Whether you want to integrate it into a chatbot, virtual assistant, or customer service platform, we will provide you with step-by-step instructions on how to deploy and scale your model for maximum impact.

    By the end of this guide, you will have a deep understanding of Chat GPT and the potential it holds for revolutionizing the way we interact with machines. Whether you are a developer, data scientist, or AI enthusiast, “The Ultimate Guide to Chat GPT” will empower you to harness the power of AI language models in your own projects.
    #Ultimate #Guide #Chat #GPT #Understanding #Training #Deploying #Speech #Recognition #Model #Discover #power #language #models #Ultimate #Guide #Chat #GPT

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