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firespirited · 9 months

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Long post. Press j to skip.

I AM SICK OF THE STUPID AI DEBATES, does it imagine, is it based on copyrightable material, are my patterns in there?

That's not the point.

I briefly got into website design freelancing (less than 3 months) before burn out.

The main reason was that automation had begun for generating stylesheets in somewhat tasteful palettes, for automatically making html/xml (they really haven't learned to simplify and tidy code though, they just load 50 divs instead of one), for batch colourising design elements to match and savvy designers weren't building graphics from scratch and to spec unless it was their day job.

Custom php and database design died with the free bundled CMS packages that come with your host with massive mostly empty unused values.

No-one has talked about the previous waves of people automated out of work by website design generators, code generators, the fiverr atomisation of what would have been a designers job into 1 logo and a swatch inserted into a CMS by an unpaid intern. Reviews, tutorials, explanations and articles are generated by stealing youtube video captions, scraping fan sites and putting them on a webpage. Digitally processing images got automated with scripts stolen from fan creators who shared. Screencaps went from curated processed images made by a person to machine produced once half a second and uploaded indiscriminately. Media recaps get run into google translate and back which is why they often read as a little odd when you look up the first results.

This was people's work, some of it done out of love, some done for pay. It's all automated and any paid work is immediately copied/co-opted for 20 different half baked articles on sites with more traffic now. Another area of expertise I'd cultivated was deep dive research, poring over scans of magazines and analysing papers, fact checking. I manually checked people's code for errors or simplifications, you can get generators to do that too, even for php. I used to be an english-french translator.

The generators got renamed AI and slightly better at picture making and writing but it's the same concept.

The artists that designed the web templates are obscured, paid a flat fee by the CMS developpers, the CMS coders are obscured, paid for their code often in flat fees by a company that owns all copyright over the code and all the design elements that go with. That would have been me if I hadn't had further health issues, hiding a layer in one of the graphics or a joke in the code that may or may not make it through to the final product. Or I could be a proof reader and fact checker for articles that get barely enough traffic while they run as "multi snippets" in other publications.

The problem isn't that the machines got smarter, it's that they now encroach on a new much larger area of workers. I'd like to ask why the text to speech folks got a flat fee for their work for example: it's mass usage it should be residual based. So many coders and artists and writers got screwed into flat fee gigs instead of jobs that pay a minimum and more if it gets mass use.

The people willing to pay an artist for a rendition of their pet in the artist's style are the same willing to pay for me to rewrite a machine translation to have the same nuances as the original text. The same people who want free are going to push forward so they keep free if a little less special cats and translations. They're the same people who make clocks that last 5 years instead of the ones my great uncle made that outlived him. The same computer chips my aunt assembled in the UK for a basic wage are made with a lot more damaged tossed chips in a factory far away that you live in with suicide nets on the stairs.

There is so much more to 'AI' than the narrow snake oil you are being sold: it is the classic and ancient automation of work by replacing a human with a limited machine. Robot from serf (forced work for a small living)

It's a large scale generator just like ye olde glitter text generators except that threw a few pennies at the coders who made the generator and glitter text only matters when a human with a spark of imagination knows when to deploy it to funny effect. The issue is that artists and writers are being forced to gig already. We have already toppled into precariousness. We are already half way down the slippery slope if you can get paid a flat fee of $300 for something that could make 300k for the company. The generators are the big threat keeping folks afraid and looking at the *wrong* thing.

We need art and companies can afford to pay you for art. Gig work for artists isn't a safe stable living. The fact that they want to make machines to take that pittance isn't the point. There is money, lots of money. It's not being sent to the people who make art. It's not supporting artists to mess around and create something new. It's not a fight between you and a machine, it's a fight to have artists and artisans valued as deserving a living wage not surviving between gigs.

#saf #Rantings #Yes but can the machine think #I don't care. I don't care. I really don't care if the machine is more precise than the artisan #What happens to all our artisans?#Long post #Press j to skip

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jcmarchi · 5 days

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Saurabh Vij, CEO & Co-Founder of MonsterAPI – Interview Series

New Post has been published on https://thedigitalinsider.com/saurabh-vij-ceo-co-founder-of-monsterapi-interview-series/

Saurabh Vij, CEO & Co-Founder of MonsterAPI – Interview Series

Saurabh Vij is the CEO and co-founder of MonsterAPI. He previously worked as a particle physicist at CERN and recognized the potential for decentralized computing from projects like LHC@home.

MonsterAPI leverages lower cost commodity GPUs from crypto mining farms to smaller idle data centres to provide scalable, affordable GPU infrastructure for machine learning, allowing developers to access, fine-tune, and deploy AI models at significantly reduced costs without writing a single line of code.

Before MonsterAPI, he ran two startups, including one that developed a wearable safety device for women in India, in collaboration with the Government of India and IIT Delhi.

Can you share the genesis story behind MonsterGPT?

Our Mission has always been “to help software developers fine-tune and deploy AI models faster and in the easiest manner possible.” We realised that there are multiple complex challenges that they face when they want to fine-tune and deploy an AI model.

From dealing with code to setting up Docker containers on GPUs and scaling them on demand

And the pace at which the ecosystem is moving, just fine-tuning is not enough. It needs to be done the right way: Avoiding underfitting, overfitting, hyper-parameter optimization, incorporating latest methods like LORA and Q-LORA to perform faster and more economical fine-tuning. Once fine-tuned, the model needs to be deployed efficiently.

It made us realise that offering just a tool for a small part of the pipeline is not enough. A developer needs the entire optimised pipeline coupled with a great interface they are familiar with. From fine-tuning to evaluation and final deployment of their models.

I asked myself a question: As a former particle physicist, I understand the profound impact AI could have on scientific work, but I don’t know where to start. I have innovative ideas but lack the time to learn all the skills and nuances of machine learning and infrastructure.

What if I could simply talk to an AI, provide my requirements, and have it build the entire pipeline for me, delivering the required API endpoint?

This led to the idea of a chat-based system to help developers fine-tune and deploy effortlessly.

MonsterGPT is our first step towards this journey.

There are millions of software developers, innovators, and scientists like us who could leverage this approach to build more domain-specific models for their projects.

Could you explain the underlying technology behind the Monster API’s GPT-based deployment agent?

MonsterGPT leverages advanced technologies to efficiently deploy and fine-tune open source Large Language Models (LLMs) such as Phi3 from Microsoft and Llama 3 from Meta.

RAG with Context Configuration: Automatically prepares configurations with the right hyperparameters for fine-tuning LLMs or deploying models using scalable REST APIs from MonsterAPI.

LoRA (Low-Rank Adaptation): Enables efficient fine-tuning by updating only a subset of parameters, reducing computational overhead and memory requirements.

Quantization Techniques: Utilizes GPT-Q and AWQ to optimize model performance by reducing precision, which lowers memory footprint and accelerates inference without significant loss in accuracy.

vLLM Engine: Provides high-throughput LLM serving with features like continuous batching, optimized CUDA kernels, and parallel decoding algorithms for efficient large-scale inference.

Decentralized GPUs for scale and affordability: Our fine-tuning and deployment workloads run on a network of low-cost GPUs from multiple vendors from smaller data centres to emerging GPU clouds like coreweave for, providing lower costs, high optionality and availability of GPUs to ensure scalable and efficient processing.

Check out this latest blog for Llama 3 deployment using MonsterGPT:

How does it streamline the fine-tuning and deployment process?

MonsterGPT provides a chat interface with ability to understand instructions in natural language for launching, tracking and managing complete finetuning and deployment jobs. This ability abstracts away many complex steps such as:

Building a data pipeline

Figuring out right GPU infrastructure for the job

Configuring appropriate hyperparameters

Setting up ML environment with compatible frameworks and libraries

Implementing finetuning scripts for LoRA/QLoRA efficient finetuning with quantization strategies.

Debugging issues like out of memory and code level errors.

Designing and Implementing multi-node auto-scaling with high throughput serving engines such as vLLM for LLM deployments.

What kind of user interface and commands can developers expect when interacting with Monster API’s chat interface?

User interface is a simple Chat UI in which users can prompt the agent to finetune an LLM for a specific task such as summarization, chat completion, code generation, blog writing etc and then once finetuned, the GPT can be further instructed to deploy the LLM and query the deployed model from the GPT interface itself. Some examples of commands include:

Finetune an LLM for code generation on X dataset

I want a model finetuned for blog writing

Give me an API endpoint for Llama 3 model.

Deploy a small model for blog writing use case

This is extremely useful because finding the right model for your project can often become a time-consuming task. With new models emerging daily, it can lead to a lot of confusion.

How does Monster API’s solution compare in terms of usability and efficiency to traditional methods of deploying AI models?

Monster API’s solution significantly enhances usability and efficiency compared to traditional methods of deploying AI models.

For Usability:

Automated Configuration: Traditional methods often require extensive manual setup of hyperparameters and configurations, which can be error-prone and time-consuming. MonsterAPI automates this process using RAG with context, simplifying setup and reducing the likelihood of errors.

Scalable REST APIs: MonsterAPI provides intuitive REST APIs for deploying and fine-tuning models, making it accessible even for users with limited machine learning expertise. Traditional methods often require deep technical knowledge and complex coding for deployment.

Unified Platform: It integrates the entire workflow, from fine-tuning to deployment, within a single platform. Traditional approaches may involve disparate tools and platforms, leading to inefficiencies and integration challenges.

For Efficiency:

MonsterAPI offers a streamlined pipeline for LoRA Fine-Tuning with in-built Quantization for efficient memory utilization and vLLM engine powered LLM serving for achieving high throughput with continuous batching and optimized CUDA kernels, on top of a cost-effective, scalable, and highly available Decentralized GPU cloud with simplified monitoring and logging.

This entire pipeline enhances developer productivity by enabling the creation of production-grade custom LLM applications while reducing the need for complex technical skills.

Can you provide examples of use cases where Monster API has significantly reduced the time and resources needed for model deployment?

An IT consulting company needed to fine-tune and deploy the Llama 3 model to serve their client’s business needs. Without MonsterAPI, they would have required a team of 2-3 MLOps engineers with a deep understanding of hyperparameter tuning to improve the model’s quality on the provided dataset, and then host the fine-tuned model as a scalable REST API endpoint using auto-scaling and orchestration, likely on Kubernetes. Additionally, to optimize the economics of serving the model, they wanted to use frameworks like LoRA for fine-tuning and vLLM for model serving to improve cost metrics while reducing memory consumption. This can be a complex challenge for many developers and can take weeks or even months to achieve a production-ready solution. With MonsterAPI, they were able to experiment with multiple fine-tuning runs within a day and host the fine-tuned model with the best evaluation score within hours, without requiring multiple engineering resources with deep MLOps skills.

In what ways does Monster API’s approach democratize access to generative AI models for smaller developers and startups?

Small developers and startups often struggle to produce and use high-quality AI models due to a lack of capital and technical skills. Our solutions empower them by lowering costs, simplifying processes, and providing robust no-code/low-code tools to implement production-ready AI pipelines.

By leveraging our decentralized GPU cloud, we offer affordable and scalable GPU resources, significantly reducing the cost barrier for high-performance model deployment. The platform’s automated configuration and hyperparameter tuning simplify the process, eliminating the need for deep technical expertise.

Our user-friendly REST APIs and integrated workflow combine fine-tuning and deployment into a single, cohesive process, making advanced AI technologies accessible even to those with limited experience. Additionally, the use of efficient LoRA fine-tuning and quantization techniques like GPT-Q and AWQ ensures optimal performance on less expensive hardware, further lowering entry costs.

This approach empowers smaller developers and startups to implement and manage advanced generative AI models efficiently and effectively.

What do you envision as the next major advancement or feature that Monster API will bring to the AI development community?

We are working on a couple of innovative products to further advance our thesis: Help developers customise and deploy models faster, easier and in the most economical way.

Immediate next is a Full MLOps AI Assistant that performs research on new optimisation strategies for LLMOps and integrates them into existing workflows to reduce the developer effort on building new and better quality models while also enabling complete customization and deployment of production grade LLM pipelines.

Let’s say you need to generate 1 million images per minute for your use case. This can be extremely expensive. Traditionally, you would use the Stable Diffusion model and spend hours finding and testing optimization frameworks like TensorRT to improve your throughput without compromising the quality and latency of the output.

However, with MonsterAPI’s MLOps agent, you won’t need to waste all those resources. The agent will find the best framework for your requirements, leveraging optimizations like TensorRT tailored to your specific use case.

How does Monster API plan to continue supporting and integrating new open-source models as they emerge?

In 3 major ways:

Bring Access to the latest open source models

Provide the most simple interface for fine-tuning and deployments

Optimise the entire stack for speed and cost with the most advanced and powerful frameworks and libraries

Our mission is to help developers of all skill levels adopt Gen AI faster, reducing their time from an idea to the well polished and scalable API endpoint.

We would continue our efforts to provide access to the latest and most powerful frameworks and libraries, integrated into a seamless workflow for implementing end-to-end LLMOps. We are dedicated to reducing complexity for developers with our no-code tools, thereby boosting their productivity in building and deploying AI models.

To achieve this, we continuously support and integrate new open-source models, optimization frameworks, and libraries by monitoring advancements in the AI community. We maintain a scalable decentralized GPU cloud and actively engage with developers for early access and feedback. By leveraging automated pipelines for seamless integration, enhancing flexible APIs, and forming strategic partnerships with AI research organizations, we ensure our platform remains cutting-edge.

Additionally, we provide comprehensive documentation and robust technical support, enabling developers to quickly adopt and utilize the latest models. MonsterAPI keeps developers at the forefront of generative AI technology, empowering them to innovate and succeed.

What are the long-term goals for Monster API in terms of technology development and market reach?

Long term, we want to help the 30 million software engineers become MLops developers with the help of our MLops agent and all the tools we are building.

This would require us to build not just a full-fledged agent but a lot of fundamental proprietary technologies around optimization frameworks, containerisation method and orchestration.

We believe that a combination of great, simple interfaces, 10x more throughput and low cost decentralised GPUs has the potential to transform a developer’s productivity and thus accelerate GenAI adoption.

All our research and efforts are in this direction.

Thank you for the great interview, readers who wish to learn more should visit MonsterAPI.

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erpinformation · 2 months

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#amazonsagemake #AmazonSagemake

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govindhtech · 2 months

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Exploring AWS Batch for Large-Scale Simulations

Simulations play a precarious role in various industries, containing automotive, robotics, engineering, and scientific research. These simulations allow businesses and researchers to investigate complex systems, train machine learning models, and make expectations without the need for costly physical prototypes or time-consuming real-world experiments. AWS Batch multi-container jobs provide a valuable tool for running significant simulations efficiently and cost-effectively.

What is AWS Batch? AWS Batch is a fully managed batch computing service It dynamically provisions compute resources (such as EC2 instances or Fargate containers) based on the submitted batch jobs. Batch eliminates the need for manual infrastructure management. Users define job dependencies, resource requirements, and priorities, and AWS Batch handles the rest, including scheduling, execution, and monitoring. Why AWS Batch need? Using the AWS Management Console, CLIs, or SDKs, you package the code for your batch jobs, define their dependencies, and then submit your batch job using AWS Batch. AWS Batch makes it easy to integrate with a variety of well-known batch computing workflow engines and languages (such as Pegasus WMS, Luigi, Nextflow, Metaflow, Apache Airflow, and AWS Step Functions) once you provide the execution parameters and task requirements.

With the ability to employ On-Demand or Spot Instances depending on your work requirements, AWS Batch effectively and dynamically prepares and scales Amazon Elastic Container Service (ECS), Amazon Elastic Kubernetes Service (EKS), and AWS Fargate compute resources. To help you get started quickly, AWS Batch provides compute environment specifications and preset job queues.

How AWS Batch Optimizes Your Industries AWS Batch provides a fully managed service for running batch computing jobs at scale. Its dynamic provisioning, resource optimization, and automated scheduling enhance operational efficiency for various industries. Here’s a closer look at how AWS Batch specifically benefits different sectors:

Automotive Accelerating Simulation Development: When working with autonomous vehicles (AV) and advanced driver assistance systems (ADAS), multi-container support allows engineers to develop simulations with modular components representing sensors, traffic, and 3D environments. This simplifies development, speeds up iteration, and eases debugging. Boosted Resource Management: AWS Batch switches the scaling, scheduling, and cost-efficiency parts of consecutively large-scale simulations, releasing automotive engineers to attention on innovation and problem-solving. Finance Rearrangement Operations and Dropping Errors: AWS Batch presets resource allocation and job scheduling for computationally intensive tasks like pricing, market analysis, and risk management. This automation reduces the potential for manual errors and optimizes the decision-making process. Enhanced Post-Trade Analytics: Batch enables the efficient end-of-day processing of massive data sets, providing critical insights for informed trading strategies in the next cycle. Fraud Detection: Batch integrates with AWS machine learning for the automated analysis of large datasets, helping uncover irregular patterns and potential fraud. Life Sciences Accelerating Drug Discovery: AWS Batch aids in streamlining multiple life sciences applications with its efficient job handling, including computational chemistry, modeling, molecular dynamics, and genomic sequencing analysis. This assists research scientists in the drug screening process, potentially leading to the development of more effective therapies. Optimized DNA Sequencing Analysis: Secondary analysis after the initial processing of genomic sequences can be automatically managed and streamlined through AWS Batch, minimizing errors and contributing to faster research results. Digital Media Scalable Content Creation: Batch facilitates the dynamic scaling of media packaging and the automation of media supply chains, reducing resource bottlenecks and manual intervention. Efficient Content Rendering and Transcoding: AWS Batch allows for the automation of content rendering and file-based transcoding workflows, leading to greater efficiency and less manual dependency. Key Takeaways

Across these diverse industries, AWS Batch delivers the following core benefits:

Simplified Management: Batch’s fully managed nature eliminates infrastructure management requirements. Modular Design: Multi-container support allows for flexible and modular simulations structure. Cost Optimization: Batch leverages options like Spot Instances and Savings Plans for optimal cost-efficiency. Focus on Core Business: By handling infrastructure and job scheduling complexities, AWS Batch allows organizations to concentrate on their core areas of expertise. Multi-Container Jobs: Key Benefits Modular Design: Multi-container jobs allow users to break simulations into smaller components—for example, a container representing the environment, one for sensors, and another for monitoring. This eases development and troubleshooting by separating different simulation elements.

Team Collaboration: Teams can work independently on their specific component, reducing bottlenecks and fostering collaboration.

Optimization: AWS Batch handles scaling, scheduling, and cost optimization. Users focus on simulation development rather than infrastructure.

No Additional Cost: This feature is available within AWS Batch without any extra charges.

How to Use AWS Batch Multi-Container Jobs Create Containers: Package simulation components into separate Docker containers.

Define Job: In the AWS Batch console, create a job definition, specifying:

Container images Resource requirements (CPU, memory) Dependencies between containers Submit Jobs: Jobs can be submitted via the console, API, or CLI.

Monitor: AWS Batch provides tools for monitoring job status, resource utilization, and logs.

Cost Optimization with AWS Batch Reserved Instances and Savings Plans: Reduce costs on predictable compute workloads. Spot Instances: Leverage cost-effective surplus compute capacity. Fargate: Pay only for the resources used by your containers. Automatic Scaling Scale your resources to match demand. Additional Considerations Data Management and Transfer: Efficiently manage data used and generated by simulations, potentially using services like Amazon S3.

Networking: Ensure appropriate networking configuration for communication between containers and with external resources.

Security: Implement security best practices (IAM roles, encryption) to protect sensitive data and your AWS environment.

AWS Batch multi-container jobs offer a dominant and flexible clarification for running large-scale simulations on AWS. By combining modularity, ease of management, and cost optimization, this article empowers businesses and researchers to accelerate innovation through simulation-driven development and experimentation.

Read more on Govindhtech.com

#aws #awsbatch #amazons2 #ec2 #amazon #technology #technews #govindhtech

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donellajane · 3 months

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Check out this listing I just added to my Poshmark closet: ❌SOLD❌URBAN OUTFITTERS Urban Renewal Remnants Ribbed Knit Cut-Out Dress Red XS.

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shrinkpackagingmachines · 4 months

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Fully Automatic Side Sealer with Shrink Tunnel Machine Manufacturer

Fully Automatic Side Sealer with Shrink Tunnel Machine Manufacturer – One of the first companies to import, produce, and export a wide range of packaging machinery is ACE Packaging Solutions. ACE Packaging Solutions offer a wide range of products, such as filling machines, cup and meal tray sealers, Fully Automatic Side Sealer with Shrink Tunnel Machine Manufacturer, L-SEALER, electromagnetic induction cappers, liquid packing machines, cling film wrapping sealers, cap closing machines, batch coders, stretch wrapping machines, shrink packaging machines, vacuum packaging machines, and filling machines. ACE Packaging Solutions apparel sector makes extensive use of the engineering products we offer.

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rosieuv · 5 months

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I fucking hate sparx maths

I hate how it's layed out, I hate how you have to get the answers right for it to register as you doing it (which is stupid as GCSEs aren't about getting all the answers right, it's about getting enough right to reach a decent mark), I hate how the question changes slightly if you get the answer wrong twice, which makes it even more of a pain to solve and I hate how my maths teacher keeps forcing this stupid fucking website on us and if I don't do all the questions and get them right in a week then I get a late homework, which has caused me to get a detention and waste my precious lunch time. She even expects us to be using it to revise in our own time and gets mad at us when we only use it to complete the mandatory homework and nothing else. Her logic is: if you don't get the answer right, watch the video even though the videos aren't that helpful and before you press the play button (doesn't automatically play) the thumnail has all the working out so watching the video is a waste of time but it doesn't register on her side. And if you don't get the answer right 4 times then you're expected to message her on microsoft teams with a screenshot and your working, but why would I bother wasting my time like that knowing she's not going to help.

And then the questions, oh the questions! They've prepously made all the questions images so you can't copy and paste the text into google and look it up on gauth maths (even though you could just type it out word for word, which I've done before) and some of these questions are just so stupid it's irratating.

a stationery shop orders a batch of pens from a factory. the factory could make the batch of pens in 20 days using 12 machines. due to a fault, only 8 machines were used for the first 9 days. all 12 machines were used from day 10 onwards. work out the total number of days taken to make the batch of pens.

I couldn't wrap my head around it so I typed this into chat gpt to get help. It's an AI, it's built on maths in it's code, surely it could figure it out. And yes, it is cheating but I don't fucking care anymore. It's actually quite helpful as it went through step by step on how to complete it, which was helpful, and then gave me the answer. I put it in. Marked it as wrong. I rounded it from 17.33 to 18 as I figured it must register the decimal as a day. Nope. Now the question's changed.

If anyone from school sees this then I'm fucked, but just in case they do:

maths teacher that I won't name: if a website is good then people will willingly use it regardless of if there's mandatory homework. If it sucks and it's mandatory then you end up with people like me cheating just to do the bare minimum and get it out of the way so I don't have to look at it for the rest of the week. Like how people willingly started to use cars after they realised they were a lot better then horses and carriges. I don't care if I get into trouble for cheating, I just want to not get a detention (which I know I will because no matter my troubles there's always questions that get marked wrong no matter what). If a student is thinking about learning to hack just to shut down the sparx maths servers then I think it's time to switch to a better resource cause nobody likes it.

#maths #math #mathematics #gcse #sparx maths

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packagingmachinesusa · 5 months

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Efficient Packing Machines

We are involved in the manufacture of various other machines in our line-up. Our machines are popular for the longevity of their operations and other in-hand operations that come for completion at various other specifications and operations for the long run and high utility. Additionally, our machines make use of the best possible input components for a perfect output. Providing you the best range of multi product packaging machine, idly dosa batter packing machine, flour packing machine, spices packing machine, automatic pouch packing machine and masala powder packing machine with effective & timely delivery - automatic packaging machine.

We are the Manufacturer of Automatic Multi Product Packing machine. This machine is auto weighing, filling, Sealing, Batch coding, Pack counting , Batch cutting all in one machine. One more special feature is that in our machine you can produce the packet ranging from in one machine. We offer many benefits over its rigid counterpart, including a decreased carbon footprint, savings on shipping and storage, a more prominent shelf-presence, and a larger canvas for marketing. Applicable to almost every industry, both food and non-food, flexible packing machines increase both efficiency and your bottom line - gummy packaging machine.

Our industry-leading vertical form-fill-seal machines provide simplicity and efficiency for food and non-food applications. Features include: Durable stainless or washdown construction – perfect for withstanding the harshest cleaning and sanitation protocols. Versatile bag assembly – create today’s hottest bag styles, including quad seal and market-standard pillow bags. Integrate convenient options including zippers, tear notches, modified atmosphere packaging, one-way valve applicators, and more. For more information, please visit our site https://packagingmachinesusa.com/

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processxe · 9 months

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Automation and paper elimination in pharmaceuticals with electronic batch records

Batch Records Software

Process XE batch records software workflows are 100% configurable. No coding required.

What is a Batch Record?

A batch record is a record of the history of a manufactured product. It comprises processing instructions to assure its safety, quality, and GMP compliance.

As a result, pharmaceutical businesses choose to use Electronic Batch Record (EBR) in order to streamline production and reduce the potential of mistakes seeping in. This article demonstrates how the system facilitates document generation in the pharmaceutical industry, from bulk to packaging processes.

What is Electronic Batch Records all about?

The pharmaceutical business that wishes to apply innovative solutions in manufacturing is concerned with more than just the digitization of forms. The EBR system is also designed to reduce the time necessary to fill out form fields and eventually lead to automatic form completion by gathering data directly from machines. Data collection directly from equipment such as scales, pH meters, and conductivity meters is frequently required as well. Furthermore, the organization wishes to avoid mistakes and track deviations from the standard that occur during the manufacturing process.

The deployment of the Process XE system results in more than just efficient documentation digitalization. Operators are directed through the manufacturing process, which includes changeovers, quality, logistics, and maintenance. Process XE is being compiled using GMP by the system.

This greatly simplified the process of creating or changing forms. However, this still necessitates the use of authorized individuals; only those with the necessary access may make modifications to the forms. All of this is done to avoid mistakes or inappropriate processing.

What does the Process XE solution offer for Pharma?

Process XE provides an all-inclusive solution that includes machine data collecting, hardware, ERP integration, and infrastructure. This method also allows you to create MBR templates. Clients can develop Master Batch Record (MBR) templates by arranging all actions in the system in the correct sequence and assigning responsibility for each role. Each activity may be customized with several reporting data kinds such as numbers, text, and data retrieved from automation, among others. These results can subsequently be checked against the system's quality limitations.

Fields in forms that have been implemented include whether or not an action has been done. A production value column and information about when a given activity was begun or stopped, as well as a computation of the time spent on it, are also among the modifications. There is also an electronic signature placed on the form and comments or remarks made by the responsible person among the implementations.

What does a paperless procedure in the pharmaceutical sector look like?

The paperless process includes digitally directing the operator through particular portions of production, beginning with the activity display - the production to-do list. It also contains changeover information and documentation for each production phase (picture, text, and video). Using Forms, all paper documentation from production are digitized.

All sheets, tables, and reports are immediately fielded with data from current shifts thanks to Process XE. The system monitors and alerts the operator when the task must be finished. Any modifications or deviations are tracked using an audit trail.

To be released, each batch must go through the Process XE system's approval procedure. This relates to the workflow aspect. The persons who notify certain positions in the organization must sign each step with an electronic signature. If the current step in the workflow is allocated to a certain role, notifications are issued. People with the necessary rights may observe where the batch release process has halted and track KPIs for the time it takes to finish each step.

Digital Records Management with Electronic Batch Records

EBR works because of all of the criteria mentioned above. This provides digital documentation management throughout the manufacturing process. The system ensures that the essential data is entered into the stages. EBR generation yields a PDF file with finished reports and signatures.

What are the benefits of this for the pharmaceutical business?

To begin, the system reduces batch release time by 35% owing to workflow and automatic data collection. Material validation, frequent quality inspections, and step-by-step digital teaching also result in 15% reduced mistakes and waste. It also delivers a 7% boost to OTIF.

The use of EBR also facilitates speedier communication between business divisions, contributes to the removal of paper from the manufacturing process, and allows for round-the-clock monitoring of current batch release statuses.

Process XE provides the solutions your pharmaceutical company need to improve workflow efficiency and environmental friendliness. Please contact a member of the team for more personalized help.

Process XE is web based, flexible and user-friendly software solutions for Business process automation.

This Digital, Smart Manufacturing process automation is principally developed for Pharmaceutical, Life science and Healthcare industries to simplify their manufacturing execution system of various processes such as data capture, batch information exchange, batch production management, data security maintenance, data integrity and report production, ensuring compliance to cGMP requirements.

For more details on MES, Contact our sales team. <Book A Demo>

#batch records #batch records software #batch records pharmaceutical #batch records in pharmaceutical industry #electronic batch records

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mattved · 3 years

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Kanban

Kanban is now well known in IT management of large corporations. Where did it come from? Who popularized it? And what is the best software to make IT work for you?

It has lately been a big hit in IT - both development and operations.

What is a Kanban?

First of all, do not call it kanban board - it comes from 看 "Watch over" 板 "board" - and you do not want to be the person who types their Personal Identification Number number in an Automatic Teller Machine machine.

In its native field of manufacturing, Kanban boards serve as a visual aid in materials release and workforce management. In a large blackboard-like layout, cards are arranged for the floor manager and those concerned with medium-term management get a clear information on production factor volumes held in each stage of manufacturing process.

Often color-coded by delivery date, contract, or intermediate products sub-step, the cards go into pockets arranged in columns. These represent stations, which may range in size - from a workbench to an assembly line to external facilities.

Additional ways to divide the board exist. One example is horizontal division into green, yellow, and red sections between load thresholds. There may be lines splitting to-do, in-progress, and done batches for purposes of floor logistics. Some companies bring in more than just that - sticky buttons, date indicators, even electronic components such as barcode scanners and RFID tags for data aggregation.

The specific use may also vary from station to station - assembly may be waiting for multiple specific cards in a row to begin work on a batch, stations using smaller parts may store them directly inside Kanban bin rack. Materials will organize their board in a weekly layout, foremen will use them for monthly rota, shift managers for even distribution throughout the day, sales may even need to work on quarterly basis to assign lead times.

With all this, the plant manager and their superiors could get all the information they need from a single view - either from the balcony, using an optional binocular, or from the less tangible but handy on-line dashboard.

Kanban in IT

How is this applicable in IT, though? With the field's importance growing, there was a need to rid it of its reputation throughout the corporate world. People had long seen it as the factor slowing them down.

"My company PC is slow, why can I not just use my own?"

"it is connecting to what?!"

"A week? Am supposed to just sit here and pick my nose?"

"Yeah, IT fucked up again, take out pen and paper."

There are many approaches, dusting off the good old manufacturing principles being one of them. In an almost religious way, a fairly recent IT management novel – The Phoenix Project – recounts them and highlights those that do apply to IT Operations with a sequel doing the same for software development.

Written in a tone of Eliyahu Moshe Goldratt's excellent piece on operations management -- The Goal -- it introduces a character of an "unconcerned" advisor. In this case, he is not as distant to the matter but appears on the main set, forcing the protagonist to observe their own company's manufacturing division.

And through these elementary observation, authors make it stand out. They explain how IT operations can structure tasks by type into infrastructure, user hardware, and software, by urgency, frequency, responsible teams, and finally steps. And that while being overly optimistic about Kanban.

The Phoenix Project Critique

Firstly, we've seen solutions to the matter in business IT ages ago in form of ticketing systems as well as project management suites. While the book is aware of both, they point at how difficult these are to follow. Company employees get access to them and slap them with overload of issues, tickets, feature requests, and cries for help. There always needs to be somebody to filter these, shielding their team from redundant or unnecessary workload. Ask for approval where it is needed, store the ticket in appropriate area, and finally assign the correct person to deal with them. And that is where it gets could get difficult.

Kanban is a pretty way to see who is busy with what and how much they can promise given the constraints. Showing this to the manager will shut them up for a while, maybe even make them understand how overloaded this business critical department is at any point.

The operations/project/product manager will also happily work with them - they can make tasks, break them down, split between teams, set deadlines and so on. Provided they have a 4K screen, there may even be enough space for them to see the whole thing.

For the developer, nowadays tied to their screen, it is not as great. With multiple columns showing up on the screen and limited width for some meaningful description of often difficult task, good 70% of the screen area becomes useless.

I may be happy to know that there are tasks coming once a prerequisite is complete but why waste an entire column with it where an expandable list with a counter would do? Upcoming tasks need work-hour estimates and have deadlines I need to track - both will do better in a simple orderable table. Backlog is not really a category for me, it's still a to-do. And completed tasks are great to check on to understand my impact but never helpful if they make my window horizontally scrollable.

The Ideal Kanban Software

Atlassian does have an okay environment in Jira but overloads it with a bit too many features I should be allowed to hide. Trello, Jira's sibling targeted at start-ups, takes the flashy stuff from her but only allows one task to be viewed at a time and does not really allow for the contingency we would love. Through the very open API, we can at least bring the relevant bits out but is it worth the time? And money?

Wekan does the same with an open-source license and a little better interface but still doesn't cut it for more complex operations with many details.

For a very similar yet more barebones solution, check out KanBoard. Being an open-source PHP project, it is very hackable on top of the great deal of ready-made plugins. It even allows toggling away from kanban view to a list-mode!

For a more user-friendly open-source, take a look at taiga.io. It also offers views on the tasks and allows filtering using all attributes. There is also a collapsed Kanban mode for top-level overview, native zoom, dynamic sectioning, sense of contingency and time-management features. And a half-assed git integration.

Step aside from kanban-first apps, Asana may come handy for teams more concerned with business operations. One can toggle Gantt view, visualize dependencies and write-up on tasks. Progress reports are more clear. Ironically, working in it gets time-consuming and it won't satisfy the technical needs of an IT department. It integrates well, sure, but is it worth the money?

For less support-oriented purposes, Notion is a great pick, bringing together task management with knowledge base, scheduling, and even allows for some custom table-form based micro apps.

Finally, there is the recently fallen big brother to them all - Phabricator by Phacility. It was an end-to-end solution for product-oriented companies in ITs with a great potential outside IT. Unfortunately, it is now, for whatever the reason is, gone.

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workflownocode · 11 months

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Open Source Workflow Automation Tools

The workflow automation tools drastically increase efficiency and add value back to core business processes. From follow-ups and replies to creating and assigning tasks, submitting monthly invoices or even approving vacation requests, workflow software automates repetitive and time-consuming human tasks so that employees can focus on high-impact projects.

Rather than working from manual instructions, open source workflow automation tools work by using a series of rules and logic to perform tasks automatically. This mimics the way in which humans work, following a pattern of “if A happens, then do B.” It’s a system that can eliminate tedious tasks so that human error can be avoided and tasks can be completed faster.

Some open source workflow automation tools are designed for a particular type of application, such as a data processing pipeline. For example, the open-source tool Luigi helps developers build complex pipelines of batch jobs that may involve recursively chaining tasks and handling failures, dumping data to or from databases, running machine learning algorithms and more.

Other workflow automation tools offer a more general approach. vtenext, for example, allows users to define a workflow with simple drag-and-drop functionality and then set the automation rules that govern it. This platform works with a variety of applications and provides project analytics, such as a Gantt chart overview, to help teams track progress.

youtube

Another option is n8n, which offers a visual interface and makes it easy to connect different apps to create automated workflows. This so-called iPaaS software (integration platform as a service) supports over 200 apps, and is constantly growing. The app list includes tools like Hubspot, Asana, GSuite and Office356, all of which can be easily connected to n8n via its clear flowchart view.

SITES WE SUPPORT

No Code Workflow – Wix

#workflowsoftware #Youtube

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govindhtech · 7 months

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The top six use cases for Kubernetes

Kubernetes, the most popular open-source container orchestration technology, is a milestone in cloud-native technologies. Kubernetes, developed privately at Google and released publicly in 2014, has helped enterprises automate operational processes related to the deployment, scalability, and management of containerized applications. Kubernetes is the standard for container management, but many firms utilize it for other purposes.

Kubernetes builds on containers, which are essential for modern microservices, cloud-native applications, and DevOps workflows.

Docker was the first open-source technology to popularize containerized application development, deployment, and management. Docker lacked an automated “orchestration” tool, making application scaling time-consuming and complicated for data science teams. Kubernetes (K8s) automates containerized application administration to solve these problems.

Kubernetes uses containers, pods, and nodes. Multiple Linux containers can run in a pod for scaling and failure tolerance. Kubernetes clusters abstract physical hardware infrastructure and run pods on nodes.

Kubernetes’ declarative, API-driven architecture has freed DevOps and other teams from manual processes to work more independently and efficiently. Google gave Kubernetes to the open-source, vendor-neutral Cloud Native Computing Foundation (CNCF) in 2015 as a seed technology.

Kubernetes manages Docker and most other container runtimes in production today. Most developers use Kubernetes container orchestration instead of Docker Swarm.

Top public cloud providers like IBM, AWS, Azure, and Google support Kubernetes, an open-source technology. Kubernetes may run on Linux or Windows-based bare metal servers and VMs in private cloud, hybrid cloud, and edge environments.

Top 6 Kubernetes use cases

Six top Kubernetes use cases show how it’s changing IT infrastructure.

1. Massive app deployment

Millions of users visit popular websites and cloud computing apps daily. Autoscaling is a major benefit of Kubernetes for large-scale cloud app deployment. Applications automatically adapt to demand changes with minimal downtime using this method. Kubernetes lets programs run continuously and adapt to web traffic variations when demand fluctuates. This balances workload resources without over- or under-provisioning.

Kubernetes uses horizontal pod autoscaling (HPA) to scale the number of pod replicas (self-healing clones) for a deployment to load balance CPU use and custom metrics. This reduces traffic surges, hardware faults, and network outages.

HPA is different from Kubernetes vertical pod autoscaling (VPA), which adds memory or CPU to running pods for the workload.

2. Powerful computing

Government, research, finance, and engineering use high-performance computing (HPC), which processes huge data to execute difficult computations. HPC makes instantaneous data-driven judgments with powerful processors at high speeds. Automating market trading, weather prediction, DNA sequencing, and aircraft flight simulation are HPC applications.

HPC-heavy industries distribute HPC calculations across hybrid and multicloud systems using Kubernetes. The flexibility of Kubernetes allows batch job processing in high-performance computing workloads, improving data and code portability.

3. Machine learning/AI

Building and deploying AI and ML systems involves massive data and complicated procedures like high-performance computing and big data processing. Machine learning on Kubernetes simplifies ML lifecycle management and scaling and lowers manual intervention.

Kubernetes can automate health checks and resource planning in AI and ML predictive maintenance workflows. Kubernetes can expand ML workloads to meet user needs, manage resources, and reduce expenses.

Kubernetes speeds up the deployment of big language models, which automate high-level natural language processing (NLP) like text classification, sentiment analysis, and machine translation. As more companies adopt generative AI, Kubernetes is used to run and expand models for high availability and fault tolerance.

Kubernetes allows ML and generative AI model training, testing, scheduling, and deployment with flexibility, portability, and scalability.

4. Microservices managing

Modern cloud-native design uses microservices, which are loosely coupled and independently deployable smaller components, or services. Large retail e-commerce websites have multiple microservices. Order, payment, shipment, and customer services are common. Each service communicates with others via its REST API.

Kubernetes was created to manage the complexity of managing independent microservices components. In case of failure, Kubernetes’ built-in high availability (HA) functionality keeps operations running. Kubernetes self-heals if a containerized app or component fails. Self-healing can rapidly reload the app or component to the desired state, ensuring uptime and reliability.

5. Multicloud and hybrid deployments

Kubernetes was designed to be utilized anywhere, making it easier to transition on-premises apps to hybrid and multicloud settings. Software developers can use Kubernetes’ built-in commands to deploy apps efficiently. Kubernetes can scale apps up and down based on environment requirements.

Since it isolates infrastructure details from applications, Kubernetes is portable across on-premises and cloud environments. This eliminates platform-specific app dependencies and simplifies application migration between cloud providers and data centers.

6. Enterprise DevOps

Business success depends on enterprise DevOps teams updating and deploying software quickly. Team agility is improved by Kubernetes’ software system development and maintenance. Software developers and other DevOps stakeholders can inspect, access, deploy, update, and optimize their container ecosystems using the Kubernetes API.

Continuous integration (CI) and continuous delivery (CD) are essential to software development. CI/CD simplifies application coding, testing, and deployment in DevOps by providing a single repository and automation tools to merge and test code. Cloud-native CI/CD pipelines leverage Kubernetes to automate container deployment across cloud infrastructure environments and optimize resource utilization.

Kubernetes’ future

As shown by its many value-driven use cases outside container orchestration, Kubernetes is vital to IT infrastructure. This is why many companies use Kubernetes. In a 2021 CNCF Cloud Native Survey , 96% of enterprises used or evaluated Kubernetes, a record high. The same study found that 73% of survey respondents in Africa use Kubernetes in production, indicating its growing popularity.

IBM/Kubernetes

Container deployment, updates, service discovery, storage provisioning, load balancing, health monitoring, and more are scheduled and automated by Kubernetes. IBM helps clients update their apps and optimize their IT infrastructure with Kubernetes and other cloud-native solutions.

IBM Cloud Kubernetes Service lets you deploy secure, highly available clusters natively.