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With the continuing growth of artificial intelligence (AI), particularly the use of Generative AI, the need for upskilling becomes clearer.
In the past few years, many companies have shifted enterprise technology tools into the cloud as a cost-saving measure.
However, many also say that those investments have yet to pay off due to suboptimal and immature architecture, a lack of skilled staff to manage complexity, and a backlog of projects that must be managed to rein in cost overruns.
Helping your team gain skills is essential to maximising AWS's value. Without it, organisations risk lower total cost of ownership (TCO), seek repatriation of services, and engage in multi-cloud arbitrage. Success on AWS is dependent on customer capabilities.
The lack of skilled talent prevents organisations from developing internal capabilities to support solutions and services and improve their experience with AWS.
The rapid evolution of generative AI is creating new jobs across all industries. And it’s changing how we work and the skills we need, from entry-level roles to the executive suite.
A forecast by World Economic Forum indicates that more than one billion jobs will be radically transformed over the next decade.
In addition to the 5M unfilled cloud roles globally, AI and Machine Learning (ML) will drive 2.6 million new jobs by 2027, primarily across data science, analytics and digital transformation.
The AI adoption challenge
Feedback from AWS customers highlight a few critical points that would help their teams take on AI/ML solutions and incorporate them with their cloud computing initiatives. These requirements can be addressed with the right AWS certification training to maximise available options.
Flexibility - There needs to be a straightforward way to find and access high-performing Foundation Models (FMs), including large language models (LLMs) that give outstanding results and are best suited for their purposes. To address this, AWS offers a wide selection of FMs, including text-to-text and text-image, built by top AI startups, including AI21 Labs, Anthropic, Stability AI and Amazon.
Secure customisation - The risk of cyber-attacks and leaks is genuine. So, teams are looking for ways to take the base FM and build differentiated apps using some or all of their data with less complexity and little to no room for error. The data that organisations use for customisation is incredibly valuable intellectual property. So, this data needs to stay completely protected, secure and private during that process. AWS users also want control over how their data is shared and used.
The most cost-effective infrastructure - Whether running Foundation Models, building them, or customising them, specialist teams need the most performant, cost-effective infrastructure that is purpose-built for ML. To support this, over the last 5 years, AWS has invested in its own silicon to push the envelope on performance and price performance for demanding workloads like ML training and inference. AWS Trainium and AWS Inferentia chips offer the lowest cost for training models and running inference in the cloud. This ability to maximise performance and control costs by choosing the optimal ML infrastructure is why leading AI startups run on AWS.
Ease of use - Technical teams are pressured to integrate and deploy FMs into their applications quickly. Running workloads on AWS using familiar controls and integrations with AWS's depth and breadth of capabilities and services like Amazon SageMaker and Amazon S3 helps staff adapt faster. This makes for easier AI modeling, in which you train a program on data to recognise specific patterns or make certain decisions without further human intervention. With AWS WS generative AI capabilities, you bring the model to your data instead of sending your data to the model.
Generative AI-powered solutions - With built-in generative AI, services such as Amazon CodeWhisperer, an AI coding companion, can help teams improve productivity. Users benefit from more natural and seamless interactions with applications and systems without even knowing there is ML involved. In addition, you can deploy common generative AI use cases like call summarisation and question answering using AWS sample solutions that combine AWS AI services with leading FMs.
Where to start with AWS training?
Given these challenges with adapting AI to cloud solutions, teams can address the skills gap through AWS training and certification. The following courses are key in all AWS services, such as Data Analytics and AI/ML.
Architecting on AWS
Learn to identify services and features to build resilient, secure and highly available IT solutions on the AWS Cloud. Architectural solutions differ depending on industry, types of applications, and business size. AWS Authorised Instructors emphasise best practices using the AWS Well-Architected framework and guide you through designing optimal IT solutions based on real-life scenarios.
AWS Cloud Practitioner Essentials
This is ideal for decision-makers who feel they've been out in the trenches for a long time and want to understand terminologies around the tech and how they can better serve customers and improve operations. Learn the essentials of the AWS Cloud, including key services and terminology. This one-day instructor-led course is for individuals who seek an overall understanding of the Amazon Web Services (AWS) Cloud, independent of specific technical roles.
Cloud Operations on AWS
For those in Operations, scaling ML models to production built by data scientists in Amazon SageMaker can be challenging. This course teaches systems operators, and anyone performing cloud operations functions how to manage and operate automatable and repeatable deployments of networks and systems on AWS. Learn about cloud operations functions like installing, configuring, automating, monitoring, securing, maintaining and troubleshooting these services, networks and systems.
Developing on AWS
How can you build ML predictions and services into your applications to solve a business problem? This course teaches experienced developers how to programmatically interact with AWS services to create web solutions. You will build a sample application during this course, learning how to set up permissions for the development environment, how to deploy machine learning models and more.
Security Engineering on AWS
The Security Engineering on AWS course addresses concerns over cyber attacks and leaks by helping you better understand how to interact and build with Amazon Web Services (AWS) securely. Learn about managing identities and roles, managing and provisioning accounts, and monitoring API activity for anomalies.
AWS Jam Sessions
We also offer a one-day AWS Jam Session to complement some of these courses. This gamified event sees teams competing to score points by completing challenges based on concepts covered in the specific AWS training courses. This reinforcement learning format helps teams understand the real-world applications of the concepts.
Build practical skills with AWS Training and Lumify Work
Lumify Work is an Advanced Tier AWS Training Partner in Australia, New Zealand and the Philippines. Through our Authorised AWS Instructors, we can provide you with a learning path for AWS cloud computing courses and AWS AI training relevant to you and your organisation so you can get more out of the cloud. Contact us for a skills assessment based on your AWS training requirements.
We offer a variety of materials to help you prepare for your exam, including an exam readiness course and sample questions that reflect the covered topics, an exam guide and practice exams that help minimise test-day surprises.
We aim to make accessing the best learning solutions easy for you and your teams. Lumify Work offers flexible delivery modalities with Lumify Anywhere. These modalities include Traditional classroom delivery (Instructor-Led Training or ILT), Virtual Instructor-Led Training (VILT, Hybrid VILT delivery, Online/Self-paced learning, Seminars, Webinars and Blended learning.
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