This article breaks down the most significant recent additions to the AWS machine learning platform, organized by audience: infrastructure-focused developers and DevOps engineers who build and deploy custom models, and data scientists or non-specialists who need managed services to analyze large datasets without hand-rolling ML pipelines.

Who AWS Is Building For

AWS machine learning investments fall into two broad categories:

• Developer and DevOps tooling — Deep Learning AMIs and Deep Learning Containers target engineers building sophisticated, custom models and the teams responsible for deploying them on cloud infrastructure.
• Managed AI services — Services like Amazon Forecast and Amazon Personalize are designed for data scientists and non-specialists who want to apply advanced ML techniques to large datasets without managing the underlying infrastructure or writing training code from scratch.

AWS Deep Learning Containers

Setting up a reproducible environment for deep learning development has historically been a time-consuming task. Data scientists had to manually resolve dependencies across multiple source repositories, manage framework versions, and tune configuration for their target hardware — a largely DIY process prone to environment drift and hard-to-debug failures.

NVIDIA addressed this problem early with NVIDIA GPU Cloud (NGC), a registry of pre-built containers that bundle all the libraries and drivers needed to run deep learning workloads on NVIDIA hardware. AWS adopted a similar approach and introduced AWS Deep Learning Containers: a set of Docker images pre-configured with the most popular deep learning frameworks.

Supported Environments

AWS Deep Learning Containers can run across a range of AWS compute services and orchestration platforms:

• Amazon Elastic Kubernetes Service (EKS)
• Amazon Elastic Container Service (ECS)
• Amazon SageMaker
• Individual EC2 instances
• Self-managed Kubernetes clusters backed by EC2 instances

Frameworks and Runtime Support

At the time of this writing, the containers shipped with support for TensorFlow 1.14.0, MXNet 1.4.1, and the NVIDIA CUDA toolkit 10.0. Both frameworks expose Python APIs, which is the dominant scripting language in the data science community.

Key capabilities include:

• Model training and inference with both TensorFlow and MXNet.
• Distributed training with TensorFlow, supported via Horovod — an open-source distributed training framework originally developed by Uber.
• CPU and GPU instance compatibility on Ubuntu 16.04.
• SageMaker integration — useful for teams that want to run custom algorithms and containers without managing the training infrastructure directly.

Availability and Deployment

Deep Learning Containers are available at no additional charge from the AWS Marketplace or Amazon Elastic Container Registry (ECR). You pay only for the underlying compute resources you consume.

For Kubernetes-based deployments, the workflow is straightforward: create a pod specification file that references one of the Deep Learning Container images, then schedule it onto your cluster using kubectl. The same container images can be used consistently across local development, EKS, and SageMaker, reducing environment parity issues between development and production.

Deep Learning AMIs: Updates for Smaller-Scale Projects

For teams working on smaller projects — those involving a handful of VMs rather than a full container orchestration cluster — AWS maintains Deep Learning Amazon Machine Images (AMIs). These provide a similar pre-configured environment directly on EC2 instances, without the need to manage container infrastructure.

In 2019, AWS expanded framework support for Deep Learning AMIs with the following additions:

• PyTorch 1.1, Chainer 5.4, and CUDA 10 support for MXNet
• Amazon Linux 2 support, along with TensorFlow 1.13.1, MXNet 1.4.0, and Chainer 5.3.0

AWS also extended the Elastic Inference service to support TensorFlow 1.12, adding a new EIPredictor Python API. Elastic Inference allows you to attach a fractional GPU accelerator to any SageMaker or EC2 instance — rather than provisioning a full GPU instance — which can significantly reduce the cost of deep learning inference workloads while still improving throughput over CPU-only instances.

AWS AI Managed Services: Now Generally Available

Several AI services previewed at re:Invent 2018 reached general availability in the months that followed. These managed services abstract away model training, infrastructure provisioning, and hyperparameter tuning — users supply data, and AWS handles the ML pipeline.

Amazon Forecast

Amazon Forecast is a fully managed time series forecasting service built on technology Amazon developed for its own internal demand-planning use cases. Users supply historical time series data and any relevant metadata, and Forecast automatically selects and trains a predictive model. The service is designed to handle the kind of complex, multi-variable forecasting problems — such as product demand, resource utilization, or financial metrics — that are difficult to address with simple statistical methods.

Amazon Personalize

Amazon Personalize is a managed recommendation and personalization engine that draws on the same underlying models used in Amazon’s retail platform. Teams provide an interaction dataset — such as page views, purchases, or content engagement events — along with an item catalog. Personalize handles model training, tuning, and hosting, and returns ranked recommendations via an API. This makes it practical to add real-time personalization to applications without dedicated ML engineering resources.

Amazon ML Insights for QuickSight

ML Insights is an add-on module for Amazon QuickSight, AWS’s interactive business intelligence service. It introduces machine learning and natural language processing features directly into the BI workflow, including:

• Anomaly detection — automatically surfaces unexpected changes in metrics across large datasets.
• Forecasting — projects future values of time series data within QuickSight dashboards.
• Auto-narratives — generates plain-language textual descriptions of chart data, making it easier to communicate findings to non-technical stakeholders.

ML Insights is aimed at analysts who need more than standard statistical aggregation but do not want to build and maintain separate ML pipelines outside of their BI tooling.

Key Takeaways for DevOps and ML Teams

• If your team builds custom deep learning models, Deep Learning Containers remove the environment setup burden and work across EKS, ECS, SageMaker, and raw EC2.
• For smaller VM-based workloads, Deep Learning AMIs offer a faster path to a pre-configured training environment without container orchestration overhead.
• Elastic Inference is worth evaluating if inference cost on GPU instances is a concern — attaching fractional accelerators can reduce spend compared to full GPU instances.
• Managed services like Forecast and Personalize are production-ready options for common ML use cases where building and maintaining custom models is not justified by the available ML engineering capacity.

Source: Originally based on reporting from searchaws.techtarget.com (2019).

Software Developer Tops UK Tech Job Volume

According to data published by the UK government’s Digital Economy Council, software developer was the single most common tech job advertised in the UK, with more than 130,000 job listings in the year covered by the report. The median salary for the role stood at £39,430 nationally, rising to £55,000 in London.

Front end developer ranked second by volume, appearing in more than 30,000 job advertisements. Median pay came in at just over £35,000 across the UK and £50,000 in London.

Highest-Paid UK Tech Roles

While software developer dominated by sheer volume, it was not the best-compensated role on the list. The report identified the following as the top earners:

• IT Systems Architect — £63,000 median UK salary; £77,500 in London (highest on the list)
• DevOps Engineer — £67,500 in London (second-highest London salary reported)
• Data Scientist — featured prominently in cost-of-living-adjusted comparisons (see below)

The gap between London and national median salaries is significant across all roles, but the report also highlighted that London’s higher cost of living can erode the real-world advantage of a higher nominal salary.

Tech Jobs Are Spreading Beyond London

A notable finding in the report is that digital tech employment is no longer concentrated solely in the capital. Cities across the UK are developing their own tech clusters, with meaningful salary premiums over the national average:

• Edinburgh — 48,118 people employed in digital tech; salaries approximately 15% above the national average
• Reading — 100,507 digital tech workers; salaries around 8.11% above the national average
• Belfast — 60,000 people in the digital tech sector; salaries 17% above the national average

Other cities mentioned as growing tech hubs include Sheffield, Leeds, Manchester and Cardiff.

Best Cities by Role (Cost of Living Adjusted)

When cost of living is factored in, the picture changes considerably. The report made the following city-by-role recommendations:

Scale of the UK Digital Tech Sector

The report estimates that approximately 2.1 million people work in the UK’s digital tech sector. In five cities — Oxford, Cambridge, Reading, Belfast and Newcastle — more than 10% of the total population is now employed in the sector. Cambridge is an outlier even within that group: the report states that around half of Cambridge’s working population is employed in digital tech.

High Demand Doesn’t Always Mean Fast Hiring

Volume of job ads does not automatically translate into vacancies being filled quickly. Separate data cited alongside the report suggests that software engineers, senior programmers and back-end developers are among the hardest tech professionals to recruit in the UK — meaning strong candidate leverage for those with the right skills.

For DevOps engineers and IT systems architects, the combination of relatively high advertised salaries and known recruitment difficulty suggests a candidate-friendly market, particularly for professionals who can demonstrate hands-on experience with infrastructure automation, CI/CD pipelines and cloud platforms.

Key Takeaways for Tech Professionals

• DevOps engineer and IT systems architect roles command the highest London salaries in the report.
• London’s nominal salary advantage is partly or fully offset by cost of living for many roles.
• Belfast, Leeds, Edinburgh and Glasgow offer competitive real-world pay for specific specialisms.
• Recruitment difficulty in software engineering and back-end development gives experienced candidates strong negotiating power.
• The UK digital tech sector is geographically diversifying, creating opportunities outside traditional hubs.

Salary data originates from the UK government’s Digital Economy Council report as summarised by ZDNet in 2019. Figures should be treated as a historical baseline; consult current industry salary surveys for up-to-date benchmarks.