Amazon Web Services (AWS) offers three important Model Deployment Services for model developers: SageMaker, Lambda, and Elastic Container Service (ECS). These services have critical advantages and disadvantages, influencing model developer's adoption decisions. This comparative analysis reviews the merits and drawbacks of these services. This analysis found that Lambda AWS service leads in efficiency, autoscaling aspects, and integration during model development. However, ECS was found to be outstanding in terms of flexibility, scalability, and infrastructure control; conversely, ECS is better suited when it comes to managing complex container environments during model development, as well as addressing budget concerns -- it is, therefore, the preferred option for model developers whose objective is to achieve complete freedom and framework flexibility with horizontal scaling. ECS is better suited to ensuring performance requirements align with project goals and constraints. The AWS service selection process considered factors that include but are not limited to load balance and cost-effectiveness. ECS is a better choice when model development begins from the abstract. It offers unique benefits, such as the ability to scale horizontally and vertically, making it the best preferable tool for model deployment.

RStudio on Amazon SageMaker is the industry's first fully managed RStudio Workbench integrated development environment (IDE) in the cloud. You can quickly launch the familiar RStudio IDE and dial up and down the underlying compute resources without interrupting your work, making it easy to build machine learning (ML) and analytics solutions in R at scale. In conjunction with tools like RStudio on SageMaker, users are analyzing, transforming, and preparing large amounts of data as part of the data science and ML workflow. Data scientists and data engineers use Apache Spark, Hive, and Presto running on Amazon EMR for large-scale data processing. Using RStudio on SageMaker and Amazon EMR together, you can continue to use the RStudio IDE for analysis and development, while using Amazon EMR managed clusters for larger data processing.

Section 4 (Days 11 – 18): we will learn: (1) machine learning regression fundamentals including simple/multiple linear regression and least sum of squares, (2) build our first simple linear regression model in Scikit-Learn, (3) list all available built-in algorithms in SageMaker, (4) build, train, test and deploy a machine learning regression model using SageMaker Linear Learner algorithm, (5) list machine learning regression algorithms KPIs such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Percentage Error (MPE), Coefficient of Determination (R2), and adjusted R2, (6) Launch a training job using the AWS Management Console and deploy an endpoint without writing any code, (7) cover the theory and intuition behind XG-Boost algorithm and how to use it to solve regression type problems in Scikit-Learn and using SageMaker Built-in algorithms, (8) learn how to train an XG-boost algorithm in SageMaker using AWS JumpStart, assess trained ...

Now if you remember, next in the workflow is building the model. To go back to the hub, AKA the SageMaker dashboard, you'll notice that notebooks are next. If you're familiar with it, SageMaker notebooks are basically managed Jupyter Notebook setups. Jupyter Notebook is what IPython notebooks rebranded to a few years back, if you've never heard of it. They also compete with a service called Zeplin, but SageMaker uses a pre-installed managed version of Jupyter. Now, just know that, although you're gonna build your notebook in SageMaker, Jupyter Notebooks are actually an open-sourced application that you can download and run yourself or run your in-house servers, several SageMakers, so you're not getting locked in.

Last year, we announced the general availability of RStudio on Amazon SageMaker, the industry's first fully managed RStudio Workbench integrated development environment (IDE) in the cloud. You can quickly launch the familiar RStudio IDE and dial up and down the underlying compute resources without interrupting your work, making it easy to build machine learning (ML) and analytics solutions in R at scale. Many of the RStudio on SageMaker users are also users of Amazon Redshift, a fully managed, petabyte-scale, massively parallel data warehouse for data storage and analytical workloads. It makes it fast, simple, and cost-effective to analyze all your data using standard SQL and your existing business intelligence (BI) tools. The use of RStudio on SageMaker and Amazon Redshift can be helpful for efficiently performing analysis on large data sets in the cloud.

This blog focuses on scaling many model training. While much of the buzz is around large model training, in recent years, more and more companies have found themselves needing to train and deploy many smaller machine learning models, often hundreds or thousands. Our team has worked with hundreds of companies looking to scale machine learning in production, and this blog post aims to cover the motivation and some best practices for training many models. Using the approaches described here, companies have seen order-of-magnitude performance and scalability wins (e.g., 12x for Instacart, 9x for Anastasia) relative to frameworks like Celery, AWS Batch, AWS SageMaker, Vertex AI, Dask, and more. While cutting edge applications of machine learning are leading to an explosion in model size, the need for many models cuts across industries.

SageMaker is a fully managed machine learning service on the AWS cloud. The motivation behind this platform is to make it easy to build robust machine learning pipelines on top of managed AWS cloud services. Unfortunately, the abstractions that lead to its simplicity make it quite difficult to customize. This article will explain how you can inject your custom training and inference code into a prebuilt SageMaker pipeline. Our main goal is to enable Intel AI Analytics Toolkit accelerated software in SageMaker pipelines.

Developing and training successful machine learning (ML) fraud models requires access to large amounts of high-quality data. Sourcing this data is challenging because available datasets are sometimes not large enough or sufficiently unbiased to usefully train the ML model and may require significant cost and time. Regulation and privacy requirements further prevent data use or sharing even within an enterprise organization. The process of authorizing the use of, and access to, sensitive data often delays or derails ML projects. Alternatively, we can tackle these challenges by generating and using synthetic data.

Seeking to bring greater security to AI systems, Protect AI today raised $13.5 million in a seed-funding round co-led by Acrew Capital and Boldstart Ventures with participation from Knollwood Capital, Pelion Ventures and Aviso Ventures. Ian Swanson, the co-founder and CEO, said that the capital will be put toward product development and customer outreach as Protect AI emerges from stealth. Protect AI claims to be one of the few security companies focused entirely on developing tools to defend AI systems and machine learning models from exploits. Its product suite aims to help developers identify and fix AI and machine learning security vulnerabilities at various stages of the machine learning life cycle, Swanson explains, including vulnerabilities that could expose sensitive data. "As machine learning models usage grows exponentially in production use cases, we see AI builders needing products and solutions to make AI systems more secure, while recognizing the unique needs and threats surrounding machine learning code," Swanson told TechCrunch in an email interview. "We have researched and uncovered unique exploits and provide tools to reduce risk inherent in [machine learning] pipelines."

As businesses grapple with growing volumes of data collected and generated by a myriad of cloud-based applications, Amazon Web Services (AWS) unveiled a wide range of new applications and product enhancements this week at its annual re:Invent conference that are geared to optimize data analytics and governance, and bolster the computing infrastructure to do so. Over the last few days, the company launched new services and features across its storage, compute, analytics, machine learning, databases, and security services, and made its first foray into supply chain management. Here is a roundup of the major announcements, with links to articles containing more details about the updates. A major theme at re:Invent 2022 was Amazon's efforts to ease data management and analytics for enterprises, as the company announced a dozen updates to data services. The updates included the launch of two new capabilities--Amazon Aurora zero-ETL integration with Amazon Redshift and Amazon Redshift integration for Apache Spark--that it claims will make the extract, transform, load (ETL) process obsolete.