AWS Aims to Make AI More Accessible for Both AI Specialists and Non-AI Experts

R. Bhattacharyya

Summary Bullets:

  • In mid-May, AWS highlighted its portfolio of AI tools and solutions during its AWS Summit Online for the Americas region and announced the general availability of Amazon Kendra for enterprises.
  • Tools that support AI model development and management and pre-built solutions that can be easily deployed by developers who aren’t AI experts help streamline AI adoption.

AWS understands the challenges enterprises face when building their own machine learning models. The company notes that when scaling AI adoption, enterprises face wide-ranging complexities that can start as early as the data collection stage and continue throughout the model management lifecycle. At the beginning of a project, organizations face challenges related to data identification, storage, and curation as they pull together disparate data sources. Later, while building and training models, they need to manage numerous other complexities, such as sharing notebooks and pre-trained models. They need to ensure effective collaboration among what can be a growing number of individuals or teams, each with their own specializations. And, since machine learning models aren’t usually perfect the first time, team members need to communicate during the process of model tuning and optimization. They need to manage multiple versions of models, run experimental models in real time, and compare results. Even after deployment, machine learning algorithms need to be managed and monitored for concerns such as data drift, with newer versions deployed as additional data is collected or the factors that impact model results change. Managing these tasks can be challenging, and as AWS rightly points out, tools that help manage the complexities do much to streamline and speed AI deployments. Continue reading “AWS Aims to Make AI More Accessible for Both AI Specialists and Non-AI Experts”

What Is the Telco Edge?

K. Weldon
K. Weldon

Summary Bullets:

• There are opportunities for operators to leverage edge technology as a key enabler to enhance the performance of applications that leverage their 4G LTE/5G networks. This is especially important for supporting new IoT use cases.

• Operators can provide Multi-access Edge Computing (MEC) gear that are essentially micro data centers, collocated with base stations at the perimeter of their networks to process and store data nearer to where it is created. But they can also orchestrate other edge solutions along with cloud services hyperscalers, equipment vendors, and IT service provider partners.

There are many locations where operators can locate or orchestrate edge resources:
– Within their own network(e.g., at telco offices)
– At the perimeter of their network(e.g., at RAN or base station locations)
– At third party locations between the edge of the CSP and operator networks, or at the edge of a customer’s network
– Within customers’ networks(e.g., for retail and factory locations)

By locating a mesh of local or regional micro data centers adjacent to telco infrastructure to store and process data, there are benefits both for the operator and the enterprise customer. The benefits to the operator include lowering the cost of sending traffic to a remote data center or public cloud. Continue reading “What Is the Telco Edge?”