Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Deploying software to support the work of an enterprise is an increasingly complex job that's often referred to as'devops.' When enterprise teams started using artificial intelligence (AI) algorithms to more efficiently and collaboratively run these operations, end users coined the term AIops for these tasks. AI can help large software installations by watching the software run and flag any anomalies or instances of poor performance.

Artificial intelligence and machine learning (AI and ML) are key technologies that help organizations develop new ways to increase sales, reduce costs, streamline business processes, and understand their customers better. AWS helps customers accelerate their AI/ML adoption by delivering powerful compute, high-speed networking, and scalable high-performance storage options on demand for any machine learning project. This lowers the barrier to entry for organizations looking to adopt the cloud to scale their ML applications. Developers and data scientists are pushing the boundaries of technology and increasingly adopting deep learning, which is a type of machine learning based on neural network algorithms. These deep learning models are larger and more sophisticated resulting in rising costs to run underlying infrastructure to train and deploy these models.

Amazon Advertising helps companies build their brand and connect with shoppers, through ads shown both within and beyond Amazon's store, including websites, apps, and streaming TV content in more than 15 countries. Businesses or brands of all sizes including registered sellers, vendors, book vendors, Kindle Direct Publishing (KDP) authors, app developers, and agencies on Amazon marketplaces can upload their own ad creatives, which can include images, video, audio, and of course products sold on Amazon. To promote an accurate, safe, and pleasant shopping experience, these ads must comply with content guidelines. Can you figure out why two of the following ads would not be compliant? It also shows the same product multiple times.

"WE'RE ALWAYS 30 days away from going out of business," is a mantra of Jen-Hsun Huang, co-founder of Nvidia, a semiconductor company. That may be a little hyperbolic coming from the boss of a company whose market value has increased from $31bn to $486bn in five years and which has eclipsed Intel, once the world's mightiest chipmaker, by selling high-performance chips for gaming and artificial intelligence (AI). As Mr Huang observes, Nvidia is surrounded by "giant companies pursuing the same giant opportunity". To borrow a phrase from Intel's co-founder, Andy Grove, in this fast-moving market "only the paranoid survive". Constant vigilance has served Nvidia well.

Like every other event this year, one of the most awaited cloud computing events, AWS re:Invent 2020 is also being held virtually. The three-week virtual event took place from November 30th with live keynotes from Andy Jassy, CEO of AWS and Werner Vogels, VP and CTO of Amazon.com. From the very first day, the event has started to announce the major launch and preview announcements including machine learning tools, containers and more. Below here, we have listed the latest announcements on AI and machine learning, in no particular order, made at AWS re:Invent 2020. After Inferentia, AWS launched its second custom machine learning (ML) chip known as Trainium.

Last year, we compiled a list of top chips for accelerating ML tasks. We talked about the rising demand of AI-based systems on Chips and the year 2020 is no different -- the trend continued. While few chipmakers capitalised on this trend, chip giants like Intel had to undergo a tough period. They even had to sell their NAND division to South Korean chipmaker SK Hynix. Even Apple announced their separation from Intel processors and opened a new chapter of Apple Silicon.

Amazon today debuted AWS Trainium, a chip custom-designed to deliver what the company describes as cost-effective machine learning model training in the cloud. It comes ahead of the availability of new Habana Gaudi-based Amazon Elastic Compute Cloud (EC2) instances built specifically for machine learning training, powered by Intel's new Habana Gaudi processors. "We know that we want to keep pushing the price performance on machine learning training, so we're going to have to invest in our own chips," AWS CEO Andy Jassy said during a keynote address at Amazon's re:Invent conference this morning. "You have an unmatched array of instances in AWS, coupled with innovation in chips." Amazon claims that Trainium will offer the most teraflops of any machine learning instance in the cloud, where a teraflop translates to a chip being able to process 1 trillion calculations a second.

On Thursday, an Amazon AWS blogpost announced that the company has moved most of the cloud processing for its Alexa personal assistant off of Nvidia GPUs and onto its own Inferentia Application Specific Integrated Circuit (ASIC). AWS Inferentia is a custom chip, built by AWS, to accelerate machine learning inference workloads and optimize their cost. Each NeuronCore implements a high-performance systolic array matrix multiply engine, which massively speeds up typical deep learning operations such as convolution and transformers. NeuronCores are also equipped with a large on-chip cache, which helps cut down on external memory accesses, dramatically reducing latency and increasing throughput. When an Amazon customer--usually someone who owns an Echo or Echo dot--makes use of the Alexa personal assistant, very little of the processing is done on the device itself.

Over at AWS re:Invent 2019, Amazon has officially launched its new Inferentia chip which is designed for machine learning. Specifically, AWS Inferentia is a custom-built chip designed to facilitate faster and more cost-effective machine learning inferencing, meaning using models you've already trained to perform tasks and make predictions. AWS says that Inferentia will deliver high throughput inference performance, and it will do this at an "extremely low-cost" with a pay-as-you-go usage model. Low latency is also promised courtesy of a hefty amount of on-chip memory. In terms of that inference throughput, Inferentia is capable of achieving up to 128 TOPS (trillions of operations per second), and multiple chips can be combined together if you really want to push the performance boundaries.

AMAZON'S SIX-PAGE memos are famous. Executives must write one every year, laying out their business plan. Less well known is that these missives must always answer one question in particular: how are you planning to use machine learning? Responses like "not much" are, according to Amazon managers, discouraged. Machine learning is a form of artificial intelligence (AI) which mines data for patterns that can be used to make predictions.