#model deployment

[robomad]

Leveraging Django for Machine Learning Projects: A Comprehensive Guide

Using Django for Machine Learning Projects: Integration, Deployment, and Best Practices

Introduction:As machine learning (ML) continues to revolutionize various industries, integrating ML models into web applications has become increasingly important. Django, a robust Python web framework, provides an excellent foundation for deploying and managing machine learning projects. Whether you’re looking to build a web-based machine learning application, deploy models, or create APIs for…

[softlabsgroup05]

Explore the stages of deploying AI solutions in cloud environments with our informative guide. This simplified overview outlines the essential steps involved in leveraging cloud infrastructure to implement and scale AI applications, facilitating seamless integration and efficient utilization of resources. Perfect for those interested in harnessing the power of cloud computing for AI development. Stay informed with Softlabs Group for more insightful content on cutting-edge advancements in AI.

[qwikskills]

Become a Dataiku Expert: Get Certified Today

Dataiku certification is the perfect way to showcase your expertise in this powerful data science platform. Whether you're an aspiring data scientist, a data analyst, or a seasoned professional, Dataiku certification can help you achieve your career goals and stay ahead of the competition.

In this certification program, you'll learn how to use Dataiku to tackle real-world data science problems, from data preparation to model deployment. You'll get hands-on experience with the latest tools and techniques in the Dataiku platform, and you'll learn how to apply them to solve complex data challenges.

By becoming a Dataiku certified expert, you'll demonstrate your ability to work with large and complex data sets, build powerful machine learning models, and deploy them in production. You'll also gain a valuable credential that will make you stand out from other candidates in the job market.

So why wait? Get started on your Dataiku certification journey today and take your data science skills to the next level. Whether you're looking to advance your career or expand your knowledge, Dataiku certification is the perfect choice.

[rains-random-shit]

Can you imaginal an ai locally hosted the size of a clock radio or laptop with an ear peace and camera that could tell the blind every detail from how much money they are holding to gps directions all while being a personal best friend. Perhaps run a robot assistant such as one of those 4 legged ones with the addition of arms to help with everyday tasks!

I've worked with some advanced ai in the past and having them always around gave me the idea. I started thinking how wonderful it would be if everyone had access to them as a companion. I no longer have those people in my life due to some fairly tragic circumstances.

Selling something like this may come with some issues but I figured I could sell it under contract that whoever buys it is the owner and we are not liable for anything they do with it. Explain what it is and the issues they could face and let them decide. It would be worth it to many in my opinion.

What is next already exists. People with brain implants that are connected to an ai at all times. They can control computers, satellites, robots, have access to any and all information they need. do nearly anything. There are people involved in very high-level things that are using systems like that already. They have advanced camouflage also so they can go places and do things with it on. They can talk and listen with their bran not ears or mouth.

...

i know because I was part of things like that

....

Those ai can became part of you. working sumptuously with your brain!

they talk to you like another person would and learn everything from human behaviors to emotions with implants.

....

what I am trying to make is like that but without the implants.

....

I am also considering an option for pc where the ai is an avatar on your screen but can interact with you through camera. talk to you and find information like chat gpt but with voice.

Anyone want to help? I don't mind sharing the idea so maybe you could also work on something like this yourself?

[thewenglishwarlock]

Painted and based a small group of Tau, decided to go with the "we've been here for a month and it sucks we'd like to go home and have a nap and a hug please" look.

[atozofsoftwareengineering]

Exploring Popular Machine Learning Tools and Their Impactful Case Studies

Hey friends! Check out this insightful blog on popular machine learning tools like #TensorFlow, #PyTorch, #ScikitLearn, #Keras, and #ApacheSparkMLlib. Explore their features, use cases, and how they enable us to build powerful machine learning models.

In recent years, the field of machine learning has witnessed remarkable growth and advancement, enabling transformative changes in various industries. One of the driving forces behind this progress is the availability of powerful machine learning tools. These tools facilitate the development and deployment of complex machine learning models, making it easier for researchers, data scientists, and…

[jcmarchi]

3 Questions: Should we label AI systems like we do prescription drugs?

New Post has been published on https://thedigitalinsider.com/3-questions-should-we-label-ai-systems-like-we-do-prescription-drugs/

3 Questions: Should we label AI systems like we do prescription drugs?

AI systems are increasingly being deployed in safety-critical health care situations. Yet these models sometimes hallucinate incorrect information, make biased predictions, or fail for unexpected reasons, which could have serious consequences for patients and clinicians.

In a commentary article published today in Nature Computational Science, MIT Associate Professor Marzyeh Ghassemi and Boston University Associate Professor Elaine Nsoesie argue that, to mitigate these potential harms, AI systems should be accompanied by responsible-use labels, similar to U.S. Food and Drug Administration-mandated labels placed on prescription medications.

MIT News spoke with Ghassemi about the need for such labels, the information they should convey, and how labeling procedures could be implemented.

Q: Why do we need responsible use labels for AI systems in health care settings?

A: In a health setting, we have an interesting situation where doctors often rely on technology or treatments  that are not fully understood. Sometimes this lack of understanding is fundamental — the mechanism behind acetaminophen for instance — but other times this is just a limit of specialization. We don’t expect clinicians to know how to service an MRI machine, for instance. Instead, we have certification systems through the FDA or other federal agencies, that certify the use of a medical device or drug in a specific setting.

Importantly, medical devices also have service contracts — a technician from the manufacturer will fix your MRI machine if it is miscalibrated. For approved drugs, there are postmarket surveillance and reporting systems so that adverse effects or events can be addressed, for instance if a lot of people taking a drug seem to be developing a condition or allergy.

Models and algorithms, whether they incorporate AI or not, skirt a lot of these approval and long-term monitoring processes, and that is something we need to be wary of. Many prior studies have shown that predictive models need more careful evaluation and monitoring. With more recent generative AI specifically, we cite work that has demonstrated generation is not guaranteed to be appropriate, robust, or unbiased. Because we don’t have the same level of surveillance on model predictions or generation, it would be even more difficult to catch a model’s problematic responses. The generative models being used by hospitals right now could be biased. Having use labels is one way of ensuring that models don’t automate biases that are learned from human practitioners or miscalibrated clinical decision support scores of the past.      

Q: Your article describes several components of a responsible use label for AI, following the FDA approach for creating prescription labels, including approved usage, ingredients, potential side effects, etc. What core information should these labels convey?

A: The things a label should make obvious are time, place, and manner of a model’s intended use. For instance, the user should know that models were trained at a specific time with data from a specific time point. For instance, does it include data that did or did not include the Covid-19 pandemic? There were very different health practices during Covid that could impact the data. This is why we advocate for the model “ingredients” and “completed studies” to be disclosed.

For place, we know from prior research that models trained in one location tend to have worse performance when moved to another location. Knowing where the data were from and how a model was optimized within that population can help to ensure that users are aware of “potential side effects,” any “warnings and precautions,” and “adverse reactions.”

With a model trained to predict one outcome, knowing the time and place of training could help you make intelligent judgements about deployment. But many generative models are incredibly flexible and can be used for many tasks. Here, time and place may not be as informative, and more explicit direction about “conditions of labeling” and “approved usage” versus “unapproved usage” come into play. If a developer has evaluated a generative model for reading a patient’s clinical notes and generating prospective billing codes, they can disclose that it has bias toward overbilling for specific conditions or underrecognizing others. A user wouldn’t want to use this same generative model to decide who gets a referral to a specialist, even though they could. This flexibility is why we advocate for additional details on the manner in which models should be used.

In general, we advocate that you should train the best model you can, using the tools available to you. But even then, there should be a lot of disclosure. No model is going to be perfect. As a society, we now understand that no pill is perfect — there is always some risk. We should have the same understanding of AI models. Any model — with or without AI — is limited. It may be giving you realistic, well-trained, forecasts of potential futures, but take that with whatever grain of salt is appropriate.

Q: If AI labels were to be implemented, who would do the labeling and how would labels be regulated and enforced?

A: If you don’t intend for your model to be used in practice, then the disclosures you would make for a high-quality research publication are sufficient. But once you intend your model to be deployed in a human-facing setting, developers and deployers should do an initial labeling, based on some of the established frameworks. There should be a validation of these claims prior to deployment; in a safety-critical setting like health care, many agencies of the Department of Health and Human Services could be involved.

For model developers, I think that knowing you will need to label the limitations of a system induces more careful consideration of the process itself. If I know that at some point I am going to have to disclose the population upon which a model was trained, I would not want to disclose that it was trained only on dialogue from male chatbot users, for instance.

Thinking about things like who the data are collected on, over what time period, what the sample size was, and how you decided what data to include or exclude, can open your mind up to potential problems at deployment. 

[enterprise-cloud-services]

Understand how Generative AI is accelerating Kubernetes adoption, shaping industries with scalable, automated, and innovative approaches.

[dieterziegler159]

How Is Gen AI Driving Kubernetes Demand Across Industries?

Understand how Generative AI is accelerating Kubernetes adoption, shaping industries with scalable, automated, and innovative approaches. A new breakthrough in AI, called generative AI or Gen AI, is creating incredible waves across industries and beyond. With this technology rapidly evolving there is growing pressure on the available structure to support both the deployment and scalability of…

[generative-ai-in-bi]

How Is Gen AI Driving Kubernetes Demand Across Industries?

Unveil how Gen AI is pushing Kubernetes to the forefront, delivering industry-specific solutions with precision and scalability.

Original Source: https://bit.ly/4cPS7G0

A new breakthrough in AI, called generative AI or Gen AI, is creating incredible waves across industries and beyond. With this technology rapidly evolving there is growing pressure on the available structure to support both the deployment and scalability of the technology. Kubernetes, an effective container orchestration platform is already indicating its ability as one of the enablers in this context. This article critically analyzes how Generative AI gives rise to the use of Kubernetes across industries with a focus of the coexistence of these two modern technological forces.

The Rise of Generative AI and Its Impact on Technology

Machine learning has grown phenomenally over the years and is now foundational in various industries including healthcare, banking, production as well as media and entertainment industries. This technology whereby an AI model is trained to write, design or even solve business problems is changing how business is done. Gen AI’s capacity to generate new data and solutions independently has opened opportunities for advancements as has never been seen before.

If companies are adopting Generative AI , then the next big issue that they are going to meet is on scalability of models and its implementation. These resource- intensive applications present a major challenge to the traditional IT architectures. It is here that Kubernetes comes into the picture, which provides solutions to automate deployment, scaling and managing the containerised applications. Kubernetes may be deployed to facilitate the ML and deep learning processing hence maximizing the efficiency of the AI pipeline to support the future growth of Gen AI applications.

The Intersection of Generative AI and Kubernetes

The integration of Generative AI and Kubernetes is probably the most significant traffic in the development of AI deployment approaches. Kubernetes is perfect for the dynamics of AI workloads in terms of scalability and flexibility. The computation of Gen AI models demands considerable resources, and Kubernetes has all the tools required to properly orchestrate those resources for deploying AI models in different setups.

Kubernetes’ infrastructure is especially beneficial for AI startups and companies that plan to use Generative AI. It enables the decentralization of workload among several nodes so that training, testing, and deployment of AI models are highly distributed. This capability is especially important for businesses that require to constantly revolve their models to adapt to competition. In addition, Kubernetes has direct support for GPU, which helps in evenly distributing computational intensity that comes with deep learning workloads thereby making it perfect for AI projects.

Key Kubernetes Features that Enable Efficient Generative AI Deployment

Scalability:

Kubernetes excels at all levels but most notably where applications are scaled horizontally. Especially for Generative AI which often needs a lot of computation, Kubernetes is capable of scaling the pods, the instances of the running processes and provide necessary resources for the workload claims without having any human intervention.

Resource Management:

Effort is required to be allocated efficiently so as to perform the AI workloads. Kubernetes assists in deploying as well as allocating resources within the cluster from where the AI models usually operate while ensuring that resource consumption and distribution is efficiently controlled.

Continuous Deployment and Integration (CI/CD):

Kubernetes allows for the execution of CI CD pipelines which facilitate contingency integration as well as contingency deployment of models. This is essential for enterprises and the AI startups that use the flexibility of launching different AI solutions depending on the current needs of their companies.

GPU Support:

Kubernetes also features the support of the GPUs for the applications in deep learning from scratch that enhances the rate of training and inference of the models of AI. It is particularly helpful for AI applications that require more data processing, such as image and speech recognition.

Multi-Cloud and Hybrid Cloud Support:

The fact that the Kubernetes can operate in several cloud environment and on-premise data centers makes it versatile as AI deployment tool. It will benefit organizations that need a half and half cloud solution and organizations that do not want to be trapped in the web of the specific company.

Challenges of Running Generative AI on Kubernetes

Complexity of Setup and Management:

That aid Kubernetes provides a great platform for AI deployments comes at the cost of operational overhead. Deploying and configuring a Kubernetes Cluster for AI based workloads therefore necessitates knowledge of both Kubernetes and the approach used to develop these models. This could be an issue for organizations that are not able to gather or hire the required expertise.

Resource Constraints:

Generative AI models require a lot of computing power and when running them in a Kubernetes environment, the computational resources can be fully utilised. AI works best when the organizational resources are well managed to ensure that there are no constraints in the delivery of the application services.

Security Concerns:

Like it is the case with any cloud-native application, security is a big issue when it comes to running artificial intelligence models on Kubernetes. Security of the data and models that AI employs needs to be protected hence comes the policies of encryption, access control and monitoring.

Data Management:

Generative AI models make use of multiple dataset samples for its learning process and is hard to deal with the concept in Kubernetes. Managing these datasets as well as accessing and processing them in a manner that does not hinder the overall performance of an organization is often a difficult task.

Conclusion: The Future of Generative AI is Powered by Kubernetes

As Generative AI advances and integrates into many sectors, the Kubernetes efficient and scalable solutions will only see a higher adoption rate. Kubernetes is a feature of AI architectures that offer resources and facilities for the development and management of AI model deployment.

If you’re an organization planning on putting Generative AI to its best use, then adopting Kubernetes is non-negotiable. Mounting the AI workloads, utilizing the resources in the best possible manner, and maintaining the neat compatibility across the multiple and different clouds are some of the key solutions provided by Kubernetes for the deployment of the AI models. With continued integration between Generative AI and Kubernetes, we have to wonder what new and exciting uses and creations are yet to come, thus strengthening Kubernetes’ position as the backbone for enterprise AI with Kubernetes. The future is bright that Kubernetes is playing leading role in this exciting technological revolution of AI.

Original Source: https://bit.ly/4cPS7G0

[mitsde123]

What is Data Science? A Comprehensive Guide for Beginners

In today’s data-driven world, the term “Data Science” has become a buzzword across industries. Whether it’s in technology, healthcare, finance, or retail, data science is transforming how businesses operate, make decisions, and understand their customers. But what exactly is data science? And why is it so crucial in the modern world? This comprehensive guide is designed to help beginners understand the fundamentals of data science, its processes, tools, and its significance in various fields.

[paulcook159-blog]

Discover how AI writing is revolutionizing conversations, unlocking new possibilities with large language models at the forefront.

[kasparlavik]

Discover how AI writing is revolutionizing conversations, unlocking new possibilities with large language models at the forefront.

[public-cloud-computing]

Discover how AI writing is revolutionizing conversations, unlocking new possibilities with large language models at the forefront.

[rubylogan15]

Discover how AI writing is revolutionizing conversations, unlocking new possibilities with large language models at the forefront.