Amazon SageMaker Studio can help you build, train, debug, deploy, and monitor your models and manage your machine learning (ML) workflows. Amazon SageMaker Pipelines enables you to build a secure, scalable, and flexible MLOps platform within Studio. In this post, we explain how to run PySpark processing jobs within a pipeline. This enables anyone that wants to train a model using Pipelines to also preprocess training data, postprocess inference data, or evaluate models using PySpark. This capability is especially relevant when you need to process large-scale data.

Tens of thousands of AWS customers use AWS machine learning (ML) services to accelerate their ML development with fully managed infrastructure and tools. For customers who have been developing ML models on premises, such as their local desktop, they want to migrate their legacy ML models to the AWS Cloud to fully take advantage of the most comprehensive set of ML services, infrastructure, and implementation resources available on AWS. The term legacy code refers to code that was developed to be manually run on a local desktop, and is not built with cloud-ready SDKs such as the AWS SDK for Python (Boto3) or Amazon SageMaker Python SDK. The best practice for migration is to refactor these legacy codes using the Amazon SageMaker API or the SageMaker Python SDK. However, in some cases, organizations with a large number of legacy models may not have the time or resources to rewrite all those models.

Amazon SageMaker Data Wrangler helps you understand, aggregate, transform, and prepare data for machine learning (ML) from a single visual interface. It contains over 300 built-in data transformations so you can quickly normalize, transform, and combine features without having to write any code. Data science practitioners generate, observe, and process data to solve business problems where they need to transform and extract features from datasets. Transforms such as ordinal encoding or one-hot encoding learn encodings on your dataset. These encoded outputs are referred as trained parameters.

I am back with a very interesting topic i.e MLOPS. What normally people think client need a 99% accurate model, when we do prediction with help of api, it will just give you prediction result. Let me tell you that.. story doesn't end here. We have one more phase about making all that we have worked to make into a workflow. That helps organizations to ship results quickly and efficiently.

SageMaker Processing allows us to provision a GPU machine on demand, and only for the time needed to evaluate the model. To do so, we use a slightly modified evaluation script that can interact with the Processing job. And this time we will run the evaluation on the entire test dataset, i.e. 15K records. Once the run is complete, we can find the evaluation results in a JSON file on the specified output folder in S3 (in our case the file will be called evaluation.json): In fact, the evaluation results tell us that the Processing job managed to run 177 samples per second.

Stop me if you've heard this before: Companies are racing to a new technological paradigm but are using yesterday's tech to do it. A survey of more than 300 storage professionals by storage vendor NGD Systems found only 11% of the companies they talked to would give themselves an "A" grade for their compute and storage capabilities. The chief reason given is that while enterprises are rapidly deploying technologies for edge networks, real-time analytics, machine learning, and internet of things (IoT) projects, they are still using legacy storage solutions that are not designed for such data-intensive workloads. More than half -- 54% -- said their processing of edge applications is a bottleneck, and they want faster and more intelligent storage solutions. The study, entitled "The State of Storage and Edge Computing" and conducted by Dimensional Research, found 60% of storage professionals are using NVMe SSDs to speed up the processing of large data sets being generated at the edge.