What are the best tools for scaling AI workloads in the cloud?

Leverage multiple cloud platforms simultaneously for AI workloads. By using a multi-cloud approach, you can ensure resilience and avoid vendor lock-in.

A lot of the cloud architectures offer auto scaling capabilities. If you are choosing a serverless service then usually cloud provider would take the responsibility of scaling up the AI models. For custom models deploying the models with kubernetes, or with a load balancer can help scale it up. You could do horizontal scaling (add more virtual machines or computes) or do vertical scaling(add more cpu/gpu).

Ray is the most popular Open-Source, first of its kind, unified, distributed compute framework specially for ML workloads. One can use AWS with Anyscale's Ray based fully-managed platform for that purpose. OpenAI apparently leverage the same. Microsoft offers its Azure AI infrastructure for the same. Purpose-built processors such as Intel's Xeon Scalable processors are great for end-to-end AI workload scale. NVIDIA's DGX is another great unified platform with a range of AI workload and performance platform to consider.

The choice of tools for scaling AI workloads in the cloud depends on specific needs, but some popular options include: Amazon SageMaker: Provides a comprehensive set of tools for training and deploying AI models on AWS. Google Cloud AI Platform: Offers managed services for machine learning, including training, serving, and data preprocessing. Microsoft Azure Machine Learning: A scalable and collaborative platform for building, training, and deploying models. Kubernetes: Used to orchestrate containerized AI workloads, ensuring scalability and flexibility across cloud providers. TensorFlow Serving: Specifically for serving machine learning models at scale, ensuring high performance and availability.

Selecting a robust cloud platform is pivotal when scaling AI workloads. Platforms like AWS, Azure, or Google Cloud offer a rich ecosystem of tools and services catering to diverse AI frameworks and libraries, facilitating seamless scalability and performance optimisation. Their infrastructure is engineered to handle complex models and voluminous data, ensuring reliable and secure operations. As I delve deeper, it's apparent that aligning the AI tech stack with the cloud architecture not only streamlines the deployment but significantly impacts cost-efficiency, making the journey of scaling AI in the cloud a strategic endeavor going beyond technical orchestration.

Choosing the right cloud platform for scaling AI workloads requires assessing AI-specific offerings, infrastructure robustness, data security measures, and cost efficiency. Every platform has its strengths, so it's crucial to align the choice with your specific needs and budget.

Choice of Cloud Platform should be based on - Understanding of Business Goal that will be enabled by AI - Cost of running AI services on the selected Cloud Platform - Capability in terms of native services that help bridge the talent gap between lack of AI experts/scientists who understand the type of AI Model being built versus what's available from the cloud provider

Several cloud-based tools and services can help you scale AI workloads effectively. The choice of tools depends on your specific use case, cloud platform, and requirements. 1. Kubeflow (Open Source): Kubeflow is an open-source platform that is designed to make it easy to build, train, and deploy machine learning models on Kubernetes. It can be run on various cloud providers. 2. TensorFlow Serving (Google Cloud): TensorFlow Serving is a part of the TensorFlow ecosystem and is designed specifically for serving machine learning models in production. 3. PyTorch (Open Source): PyTorch is an open-source deep learning framework that is widely used for building and training AI models.

Opt for AI services that allow customization to meet the unique requirements of your projects. While cloud providers and third-party vendors offer ready-made solutions, selecting services that enable tailoring to specific use cases is essential.

I really like the new @remote decorator feature in SageMaker. Basically, it lets you take your existing Python code and run it as a training job on SageMaker with minimal fuss. Just annotate the functions you want to execute remotely, provide some configs, and SageMaker sets up the full distributed environment for you. No need to learn new SDKs or APIs for SageMaker. I'd say it makes scaling up to the cloud feel like an extension of your local workflow, rather than a whole new paradigm. Less disruptive for sure!

Elastic Inference (AWS): AWS Elastic Inference allows you to attach just the right amount of GPU-powered inference acceleration to your AI workloads without over-provisioning. AutoML Services (Various Cloud Providers): Many cloud providers offer AutoML services, such as AWS AutoML, Google AutoML, and Azure AutoML. These services automate the machine learning process Serverless Computing (e.g., AWS Lambda, Azure Functions, Google Cloud Functions): Serverless platforms are useful for scaling AI workloads dynamically in response to traffic. Container Orchestration (e.g., Kubernetes): Kubernetes can be used to orchestrate containers running AI workloads, allowing for flexible and efficient scaling based on demand.

Worth checking Microsoft's ND H100 v5 VMs for the complex generative AI models scaling and Google's Multislice for AI training to up to tens of thousands of Cloud TPU chips. Intel's 4th Gen Xeon Scalable processor are another great example to be considered which supports a wide range of open standard frameworks. In addition to these, Nvidia DGXTM A100 Tensor Core GPUs are also helpful.

Actually, I disagree with the notion that AI services are always the best solution for scaling AI workloads. While they offer convenience, they may lack the flexibility required for custom or niche applications, potentially resulting in suboptimal performance or increased costs in the long run.

Prioritize comprehensive compatibility testing when selecting an AI framework. Instead of solely considering the framework's features, ensure that it aligns with your existing tech stack and infrastructure.

Apart from the AI frameworks mentioned here, I think one can utilize Apache Spark - a distributed framework for large-scale data processing. It also supports distributed training of machine learning tasks, thus suitable for scaling AI workloads.

I wanted to mention a few more great frameworks to consider such as Caffe that is specially coming with a preloaded set of trained neural networks. Theano, a python based frameworks delivers accuracy for networks that need high computational power. Nvidia's NeMo framework provides an accelerated workflow for training with 3D parallelism techniques and NeMo Guardrails is my favourite that helps LLMs aligned AI safety and security requirements. Wanted to mention LangChain - a framework for language models (LLMs) based application development for enterprises and a frameworks named Flowise in the context that allows building LLM Chain with a Prompt Template and LLM Model.

In general, acceleration equals increased costs. The more services and frameworks we use to make it easier to deliver end-to-end intelligence, the higher the egress and ingress, storage, compute, and network costs. One approach is to learn from the "hybrid" cloud and take the approach of "hybrid" training. Here some aspects of the training that could (and in some cases MUST) be done on-premise. While there are aspects of the training for the cloud because they need faster thinner scaling to test larger combinatorial permutations of theses. The bottom line, over acceleration, is over-clouding, and over-clouding is over-spending. Find the right hybrid config for the training workload, and as a byproduct keep some training data on-premise.

AI accelerator are predominantly two types - the data center and the edge. Cerebras has delivered the pioneering WSE - the biggest chip ever built, for DL systems trusted by industry. Others incl Intel Xeon Scalable Processors, Max Series, Habana Gaudi and Gaudi2 delivers edge and cloud with high performance DL. Nvidia's Deep Learning Accelerator (DLA) hardware and software AI platform at the edge, Nvidia DGX H100 GPUs are widely used as powerful accelerators, used by OpenAI ChatGPT, Nvidia H100 NVL is a specialized chip for real-time LLM inferencing, Google has recently announced Nvidia L4 - a single-slot and low-profile accelerator for AI, video and graphics. Microsoft soon coming with their own Athena chips to disrupt the market too.

Much like DevSecOps, and fully streamlined "ops pipeline" that is fluid and flawless is difficult to achieve and sustain. AI Orchestration can be thought of as MLOps DevSecOps and "then some" which makes it more difficult to achieve. I have found realistic expectations about how perfect the end-to-end pipeline will be in the short-term, medium-term, and long-term help. With AI (right now) there is a lot of learning of unknowns which triggers changes in the pipeline, this can be daily. The suggestion is to evolve the pipeline as the understanding of what is needed matures. And accept, that the pipeline might never be perfect, and if it is, you might have wasted resources on perfection just for perfection. Speaking from experience.

All of the cloud providers are having their own cloud native AI/ML workload orchestration services. AWS comes up with its MLOps Workload Orchestrator solution that also allows architectural adherence patterns for operationalization of ML model workloads. One can leverage Azure Databricks for MLOps orchestration. Google Cloud Platform provides the Vertex AI Pipelines to automate, monitor, and govern enterprise ML workloads in a serverless way.

AI orchestration tools are crucial for managing and coordinating the various components and tasks involved in artificial intelligence workflows. Apache Airflow: Apache Airflow is an open-source workflow automation and scheduling platform that is widely used for orchestrating AI pipelines. Kubeflow Pipelines: Kubeflow Pipelines is an open-source platform for building, deploying, and managing scalable, portable machine learning workflows using Kubernetes. MLflow: MLflow is an open-source platform for managing end-to-end machine learning lifecycles. Step Functions (AWS): AWS Step Functions is a serverless orchestration service that can be used to coordinate AWS services and build serverless workflows.

AI orchestration tools can help organizations to scale their AI deployments and improve the efficiency and reliability of their AI workflows. They can also help to reduce the risk of errors and ensure that AI models are deployed in a responsible and ethical manner. Federated learning allows multiple devices to collaborate in training a model without sharing their data. This makes it possible to train models on large datasets that are distributed across many devices, without compromising on data privacy.

The tools mentioned here are great, but they are complex to learn and master. Optimizing a model is a bit more hand artisan than tool mastery. In many cases, the tools only get you so far, and then someone really intimate with the underlying syntax, systems, math, and APIs has to "go in manually" and finalize things, this person can be thought of as a "model whisperer" of sorts. How to get more whisperers? Documentation. There are so many things that one has to remember about how we got here, to then be able to take us through the last mile to perfection, that it is impossible to get it right unless everything is written down and easy to find. So I would add a strong documentation tool and a documenting discipline to the toolset.

Adhere to your preferred cloud provider, utilising a managed service coupled with an API abstraction layer. In doing so, the majority of the scaling processes will be done automatically. For example, a combination of AWS SageMaker and AWS Bedrock cab be a good choice in many situations. Keep it simple.

Cloud is a great enabler for many companies. Especially for managed services - by "managed services" I mean a solution that you "just use it" (don't worry about operations). If you're starting out, I'd recommend going for cloud-managed services as well. They are typically more expensive, but you end up saving by not spending time (and paying engineers) to maintain infrastructure, operations, and updating the service. When choosing your cloud provider, I'd definitely look at what managed services they offer, and choose the "simplest" cloud that meets your needs. If you have too many options it's possible to deploy infrastructure accidentally, have that g under the radar and potentially increase your costs.

Kubernetes is like a smart manager for your AI work. It helps you organize and adjust your AI models easily. You can put your AI into containers and use Kubernetes to make them bigger or smaller based on how much work they need to do. Kubernetes also does helpful things like spreading the work evenly and fixing problems on its own. You can also think about using services like AWS SageMaker, Google AI Platform, or Azure Machine Learning to make AI work even simpler by letting someone else take care of some of the hard parts.

One key best practice is to use AI frameworks that are designed to simplify and accelerate the development and deployment of AI models. AI frameworks provide a variety of tools and libraries that can help to automate many of the tasks involved in AI development, such as data preparation, feature engineering, and model building.Use Gen AI. One of the benefits of using Gen AI is that it can help to scale AI workloads. Gen AI models can be used to generate large amounts of data that can be used to train and improve AI models. This can be especially useful for tasks where it is difficult or expensive to collect real-world data.