Why is Everybody Talking About the Cloud?

Your business runs on data. Your life probably runs on data.

The cloud makes that possible.

If you’re serious about building scalable, flexible and powerful machine learning models (or making sure your team can build them), then you probably want to start getting comfortable with the cloud.

The cloud is already a part of your life

A huge chunk of our lives is dominated and driven by machine learning algorithms. These algorithms are now used in nearly every field to build models that can predict future events with an astonishing level of reliability. Most of those algorithms and models are cloud-based.

Artificial intelligence and machine learning algorithms are the forces driving social media, marketing, customer support, fraud detection, business intelligence, and pretty much every movie and music recommendation we see every day.

We’re talking to machine learning algorithms and asking them questions. We’re letting them help us make our most basic decisions like what to watch, read, listen to, and wear. Machine learning and artificial intelligence are becoming standard across business as we know it. It’s hard to imagine that most of this work won’t be happening in the cloud.

Start loving it!

Why do we need cloud-based machine learning?

Right now, we’re collecting and accumulating data at massive and unmanageable rates. Businesses collect website clicks, social media interactions, credit card transactions, GPS trails, and on and on and on. But it’s almost impossible for most companies to process all of this information and use it in any kind of meaningful way.

Enter machine learning! A good algorithm will use collected data to learn patterns and predict insights and results that can help us make better decisions that are backed by actual analysis.

You might be more familiar with this than you realize. Do you ever use social media? Every time you see a recommendation for a friend you might know or an article you might like, that’s a machine learning algorithm. Do you watch videos online? Have you noticed the other videos that are being recommended to you? Machine learning! Ever apply for a credit card or a loan? Banks primarily let an algorithm decide whether or not to let you have a credit card (and how much interest to charge) based on your credit score. Your credit score is based on your credit history, the loans you have (and have applied for), and other kinds of data. All of this data has to be collected, analyzed, and interpreted in massive quantities at lightning-fast speeds.

That’s where the Cloud comes in.

Who’s using AI, ML, and the cloud?

According to a recent survey by Evans Data Corp, 6.5 million developers are currently using some form of artificial intelligence (AI) or machine learning (ML), and another 5.8 million plan to start using artificial intelligence or machine learning within six months. Given that there are more than 22 million developers worldwide, that means a majority (around 56%) are either using these technologies now or will start using them soon.

In its Technology, Media and Telecommunications Predictions, Deloitte Global predicts that in 2019, companies will accelerate their usage of cloud-based artificial intelligence software and services. In companies that adopt AI technology, 70% will obtain AI capabilities through cloud-based enterprise software and 65% will create AI applications using cloud-based development services. By 2020, penetration rates of enterprise software with integrated AI and cloud-based AI platforms will reach an estimated 87% and 83% among companies that use AI software. The cloud will drive more full-scale AI implementations, better ROI (return on investment) from AI, and higher AI spending. Because of this, we’re going to be able to see and build AI capabilities and benefits that used to belong only to early adopters.

On top of that, the International Data Corporation predicted that worldwide spending on artificial intelligence systems is forecast to reach $35.8 billion in 2019. That’s an increase of 44.0% over the amount spent in 2018. With industries investing aggressively in projects that use AI software capabilities, it expects spending on AI systems will more than double to $79.2 billion in 2022.

Much of that development is taking place in the cloud.

If you’re running a data science team, you want to think about how this affects your business.

What changed?

For a long time, machine learning models were simply out of reach for most businesses. The costs alone made them prohibitive. Even if a business could afford to implement one, it probably didn’t have anyone on hand who could design a model and interpret the results. Cloud-based machine learning solutions have changed that. Relatively speaking, they’re cheap to operate and often come with pre-built solutions to complex problems.

One of the biggest advantages of cloud-based machine learning is that it gives organizations access to high-performance infrastructure that they couldn’t afford (or properly use) on their own. ML applications require a ton of processing power. That’s traditionally been very expensive! Now, many organizations use systems that rely on GPUs to handle ML workloads. It’s much more affordable to rent access to these systems in the cloud than to purchase them outright.

While this sometimes be a complicated process, Saturn Cloud has a one-click ability to run on GPUs. Also, for the enterprise tier, it’s a great alternative to building your own cloud-hosted data science environment. With those, you usually have to deal with maintenance requirements, high costs (like needing full-time employees to manage all the details) and frequent reengineering to keep up with updates.Thousands of people use Saturn Cloud because it makes life simple.

Cloud-based machine learning also usually includes access to affordable data storage. As data volumes continue to grow, businesses find that moving data to public cloud systems is less expensive than continuing to house it in their own data centers. If the data is already stored in a cloud, it often makes sense to use a cloud-based ML service. Transferring large quantities of data takes time and adds expense.

The cloud makes it easy for businesses to experiment with machine learning capabilities and scale up as projects go into production. It gives you access to intelligence without requiring much in the way of advanced skills in data science and artificial intelligence and you also often have access to machine learning options that don’t require deep knowledge of machine learning theory and AI.

The really exciting thing about the cloud is the way that it allows for collaboration. While it’s different on every platform, Saturn Cloud is my favorite way to collaborate because it’s so simple. You can set it up with almost no effort, and then anyone you choose can collaborate with the click of a button. It’s the only easy way to collaborate on Jupyter Notebooks.

(I’m a big fan of Jupyter Notebooks…)

Where can I get access?

While I’m a big fan of Saturn Cloud and their cloud-based Jupyter Notebooks,

(Full disclosure: I’ve absolutely done work for Saturn Cloud, been paid by them, and know how good Saturn Cloud is and what it can do.)

…there are a number of cloud machine learning tools and services out there. These include Google Cloud Platform, Amazon Web Services, Microsoft Azure, IBM Cloud, Oracle Cloud, and more. One of the cool things about Saturn Cloud is that (for the enterprise tier), they are multi-cloud so people can use Saturn on AWS, Google, and Azure clouds without being locked into any one vendor.

If you want to work with large amounts of data, machine learning in the cloud is the best option you have right now, both in terms of speed and the scale at which they operate. And the smarter the cloud becomes, the more appealing it will be for the kinds of work that are going to define next-generation data services and the future of business as a whole.

Thanks for reading! As always, if you do anything cool with this information, let everyone know about it in the comments below, or reach out on LinkedIn @annebonner!