There are long-term trends in technology we all know are happening. Computers will get more powerful. More devices will be connected. Finally, the cloud will continue to become a more integral part of everything we do. We know these things because we see the news reinforcing these ideas over days, months and years.
There are days however where a few news items bring into clear focus something we kind of know but don’t readily admit.
Today is such a day.
Long story short, IBM, Dell and HP have their hands full competing with Amazon.
Anyone who tracks Amazon’s stock already knows the company is one of the few which gets away with not making much if any profit – yet its share price continues to grow. The argument goes according to many, the company can reduce investment at any point and profits will swell. In reality the company hasn’t done this on a consistent basis for many years.
Instead, it continues to drive prices in the cloud computing and other markets downward.
It is so tough to compete with the company that today, HP did something unthinkable. They pulled out of the public cloud market. This is a huge deal because contrary to what hardware vendors want to have happen – there are many businesses using the public cloud to run their organizations.
Moreover, HP seems to be sending a mixed message. On the one hand they say they can’t compete against public cloud vendors but on the other, they say they aren’t leaving the public cloud. What this should tell us is they will eventually leave the market after they have racked up as-yet-to-be-determined losses.
More importantly – by definition, if companies are moving from servers to the cloud and HP doesn’t provide an offering in this space, they will continue to sell less servers.
To add insult to injury, just as HP tells us what many did consider obvious – they can’t compete with Amazon… The ecommerce leader has just launched a new service on its cloud which in some ways rivals what IBM touts as a differentiator with its Watson product.
Amazon Machine Learning can do many things such as detecting problematic transactions, preventing customer churn, and improving customer support. The related APIs and wizards guide developers through the process of creating and tuning machine learning models that the company says can be easily deployed and scale to support billions of predictions.
The goal of the solution is to make machine learning broadly accessible to all software developers by abstracting away this complexity and automating these steps which require expertise in statistics, data analysis, and machine learning. Moreover, developers can quickly create as many models as they need, and generate predictions from them with high throughput without worrying about provisioning hardware, distributing and scaling the computational load, managing dependencies, or monitoring and troubleshooting the infrastructure.
“Amazon has a long legacy in machine learning. It powers the product recommendations customers receive on Amazon.com, it is what makes Amazon Echo able to respond to your voice, and it is what allows us to unload an entire truck full of products and make them available for purchase in as little as 30 minutes,” said Jeff Bilger, Senior Manager, Amazon Machine Learning. “Early on, we recognized that the potential of machine learning could only be realized if we made it accessible to every developer across Amazon. Amazon Machine Learning is the result of everything we’ve learned in the process of enabling thousands of Amazon developers to quickly build models, experiment, and then scale to power planet-scale predictive applications.”
Another salient bit of information is the following provided by the company – the bolded area was added by me.
Amazon Machine Learning allows developers to visualize the statistical properties of the datasets that will be used to “train” the model to find patterns in the data. This saves time by allowing developers to understand data distributions and identify missing or invalid values prior to model training. Amazon Machine Learning then automatically transforms the training data and optimizes the machine learning algorithms so that developers don’t need a deep understanding of machine learning algorithms or tuning parameters to create the best possible model. Using the Amazon Machine Learning technology, a single Amazon developer was able in 20 minutes to solve a problem that had previously taken two developers 45 days to solve – none of these developers had prior experience in machine learning, and both models achieved the same accuracy of 92 percent. Once a model is created, developers can then easily generate batch or real time predictions directly from Amazon Machine Learning without having to develop and manage their own infrastructure to do so.
The challenge for server companies is greater than ever as the public cloud – which in many cases people believe is the same thing as AWS gets better. Innovations such as these are certainly not only happening at Amazon but what differentiates the company from others is they are willing to reduce prices to a point where there is no profit in order to get customers in the door. This is why Microsoft and Google often offer $100,000 credits to get promising startups on their cloud before it is too difficult for them to switch.
Amazon, continues to amaze with its bewildering array of products from tablets to cloud services. Obviously they aren’t infallible because their tablets and phones are having competitive challenges. In cloud however, the company’s early lead and innovation means the competition in public and private cloud as well as server companies are going to have to find ways to differentiate. And with Amazon adding new innovations to their cloud on a regular basis, another trend I predict is it will be ever-more difficult to point to advantages your cloud provides that theirs doesn’t.