Algorithmia Blog - Deploying AI at scale

Flexibility, Scale, and Algorithmia’s Edge in Machine Learning

At Algorithmia, we’ve always been maniacally focused on the deployment of machine learning models at scale. Our research shows that deploying algorithms is the main challenge for most organizations exploring how machine learning can optimize their business.

In a survey we conducted this year, more than 500 business decision makers said that their data science and machine learning teams spent less than 25% of their time on training and iterating models. Most organizations get stuck deploying and productionizing their machine learning models at scale.

Machine Learning in Enterprise research results

The challenge of productionizing models at scale comes late in the lifecycle of enterprise machine learning but is often critical to getting a return on investment on AI. Being able to support heterogeneous hardware, conduct versioning of models, and run model evaluations is underappreciated until problems crop up from not having taken these steps.

Recent Trends
At the AWS re:Invent conference in Las Vegas this week, Amazon announced several updates to SageMaker, its machine learning service. Notable were mentions of forthcoming forecast models, a tool for building datasets to train models, an inference service for cost savings, and a small algorithm marketplace to—as AWS describes—“put [machine learning] in the hands of every developer.”

“What AWS just did was cement the notion that discoverability and accessibility of AI models are key to success and adoption at the industry level, and offering more marketplaces and options to customers is what will ultimately drive the advancement
of AI.”

–Kenny Daniel, CTO, Algorithmia

Amazon and other cloud providers are increasing their focus on novel uses for machine learning and artificial intelligence, which is great for the industry writ large. Algorithmia will continue to provide users seamless deployment of enterprise machine learning models at scale in a flexible, multi-cloud environment.

Deploying at Scale
For machine learning to make a difference at the enterprise level, deployment at scale is critical and making post-production deployment of models easy is mandatory. Algorithmia has four years of experience putting customer needs first, and we focus our efforts on providing scalability, flexibility, standardization, and extensibility.

We are heading toward a world of standardization for machine learning and AI, and companies will pick and choose the tools that will make them the most successful. We may be biased, but we are confident that Algorithmia is the best enterprise platform for companies looking to get the most out of their machine learning models because of our dedication to post-production service.

Being Steadfastly Flexible
Users want to be able to select from the best tools in data labeling, training, deployment, and productionization. Standard, customizable frameworks like PyTorch and TensorFlow and common file formats like ONNX increase flexibility for users for their specific needs. Algorithmia has been preaching and executing on this for years.

Standard, customizable frameworks increase flexibility for users for their specific needs. Algorithmia has been preaching this for years.
–Kenny Daniel, CTO, Algorithmia

Algorithmia’s Commitment
For at-scale enterprise machine learning, companies need flexibility and modular applications that easily integrate with their existing infrastructure. Algorithmia hosts the largest machine learning model marketplace in the world, with more than 7,000 models, and more than 80,000 developers use our platform.

“I expect more AI marketplaces to pop up over time and each will have their strengths and weaknesses. We have been building these marketplaces inside the largest enterprises, and I see the advantages of doing this kind of build-out to accelerate widespread
AI adoption.”

–Diego Oppenheimer, CEO, Algorithmia

It is Algorithmia’s goal to remain focused on our customers’ success, pushing the machine learning industry forward. We encourage you to try out our platform, or better yet, book a demo with one of our engineers to see how Algorithmia’s AI layer is the best in class.

10 Things We’re Thankful for at Algorithmia

Algorithmia value chart, thankful

At Algorithmia, we have much to be thankful for—it’s even one of our core tenets. So in light of Thanksgiving, we have compiled a list of all that we’re particularly appreciative of this year. Some of our staff are thankful for the little things—snacks and a dog-friendly office—and some are glad of more practical things—the freedom to develop skills and experience for career development. Regardless, Algorithmia is eternally grateful for our customers and contributors.

“I’m thankful for the flexibility in where we live and when and how we get our work done!”
–Stephanie, Developer Advocate

“I am thankful that I work at a company that has a great set of values that we live by. One of them is actually, “We are thankful”! We are thankful for every single one of our users, customers, and contributors. We do not exist without them and always strive to make their experiences better.”
–Jonah-Kai, Head of Growth Marketing

“I’m thankful for working with some incredibly talented people.”
–Besir, Algorithm Engineer

“I’m thankful for how helpful and supportive my team is.”
–Adnaan, Back End Engineer

“I’m thankful for board game night.”
–James, Product Designer

“I’m thankful that I get to work on complex and creative projects!”
–Whitney, Content Marketing Manager

“I’m thankful for the growth mindset and intellectually curious culture!”
–Ken, Enterprise Sales Development Rep

“I am thankful for the team’s willingness to jump in and fix problems, always. I call it a winner attitude.”
–Diego, CEO

“I am thankful for the remote friendly culture.”
–Rowell, Senior Platform Engineer

“I’m thankful for interesting, challenging, and creative opportunities every day.”
–Jon, Developer Advocate

“I’m thankful for the awesome views from the devpit (even if the blinds are down more often than not).”
–Ryan, Front End Engineering Lead

As we look toward the end of the year, we are also thankful to have the opportunity to give back to others and help underserved communities.

Thankful for giving

Our office charitable giving campaign


Going to Print—the Cimpress Machine Learning Story

Read the Case Study

Machine learning can automate business processes, but maybe more importantly,
it can improve customer experience—just look at Cimpress.

Cimpress, the parent company of VistaPrint, is one of the foremost aggregators of customized merchandise in the world with more than 10,000 employees spanning multiple continents. It has a mind for ethically and environmentally sustainable product production and has grown rapidly since its inception in 1994, while maintaining its ethos of staying small even as it gets big.

Cimpress integrates ML into its online experience

By 2016, Cimpress was running up against the challenge of deploying its models at
scale—a huge undertaking for any company to integrate into its existing tech infrastructure. The Cimpress team realized the effort required to manually deploy
ML models was slowing them down and started looking for solutions.

Cimpress tested many potential solutions but found Algorithmia’s Serverless AI Layer to be the perfect fit for deploying and managing its models at scale. The AI Layer reduced the number of full-time developers it required to maintain and optimize its systems.

Algorithmia is able to ensure seamless future deployments of machine learning projects for Cimpress without costly or time-intensive rollouts.

The Algorithmia collaboration is accelerating Cimpress’ ability to offer wider customer focus without reducing its commitment to quality and efficiency.

Cimpress was ahead of the curve in understanding core principles of machine learning

Of course, companies should spend time distilling and identifying their core business needs and gaps, like Cimpress did, before looking to incorporate machine learning says Chief Decision Intelligence Engineer at Google and widely published writer about all things AI and machine learning, Cassie Koryzov (Towards Data Science, 2018). An outside firm with expertise in building customized ML infrastructure is often better suited to meet the automation needs than internal developers.

Entrepreneur and former principal data scientist at LinkedIn Peter Skomoroch also calls for using outside experts to build machine learning into business models.

Learn more about Cimpress’ journey into employing Algorithmia’s AI Layer:Read the Case Study

Model Evaluations Beyond Compare

At Algorithmia, we strive first and foremost to meet customer needs, and we’re
releasing a new feature within the AI Layer to help you conduct model comparison.
Model Evaluations is a machine learning tool that lets you create a process for running models concurrently to gauge performance. You can test similar models against one another or compare different versions of one model using criteria you define. Model Evaluations makes comparing machine learning models in a production environment
easy and repeatable.  

If you have ever wanted to know which risk score algorithm is the best for your dataset, Model Evaluations can help. It can test models for accuracy, quality, error rates, drift, or any other performance indicator you specify. Evaluations can be created for an individual user or be organization-owned to enable collaboration across teams. Simply load a new model into the platform and run tests against your own models or those in the marketplace. We plan on making this tool part of the standard UI experience in a future release of Algorithmia Enterprise, but it is available for early access right now

Model Evaluations logo

Comparing models is important because testing and comparing models is an integral part of any development and deployment cycle. Achieve a competitive advantage over other models, build your brand’s credibility, and be certain that new versions outperform previous versions. Other benefits of Model Evaluations:

  • Improve model accuracy and performance
  • Test models before deploying
  • Conduct faster comparisons
  • Get results quicker

Sign up to get early access to our model comparison tool.

To learn more about Model Evaluations, you can find additional documentation, examples, and a step-by-step walkthrough in the Developer Center. But start here with this video we’ve put together demoing the Model Evaluations tool: 

Algorithmia is a leader in the machine learning space, and we care about building
smarter models, so please tell us
 about your experience. We’re eager to hear
your suggestions or ideas!

Model Evaluations will help data scientists compare the quality of different models or even measure the effectiveness of new versions of the same model.

Most Common Use Cases for Enterprise Machine Learning

In part two of our blog series about machine learning in the enterprise, we talk briefly about some of the most common use cases for machine learning. Larger companies produced the widest variety of use cases, however, there was no one single area of focus. Despite such varied answers on where companies were centralizing their attention, we noticed some common trends that we’ll discuss below.

Get the Full Report “The State of Enterprise Machine Learning” here.

Big emphasis on the customer
Among all our respondents, there was clear attention to how machine learning capabilities would help them interact with and retain their customers. Some of the highest selected use cases identified were: generating customer insights and intelligence (#1), improving the customer experience (#2), interacting with customers (#5), increasing customer satisfaction (#6), and retaining customers (#7).

Among the largest companies, the most common use case reported was increasing customer loyalty (59%), followed by increasing customer satisfaction (51%), and interacting with customers (48%). Similarly, among the smallest of responding companies, increasing customer satisfaction (36%) was the second most identified use case behind reducing costs (43%).

Larger organizations are putting significant efforts into using data science to identify areas of cost savings
For larger organizations, cost savings seems to be an increasingly important area of focus. This is due to the fact that it is easy to tie ROI to cost savings programs and showcase success.  43% of companies with 1,001 to 2,500 employees put it as a use case, as well as 41% of companies between 2,501 and 10,000 employees, and 48% of companies with more than 10,000 employees.

The focus on reducing costs is higher among sophisticated adopters
Sophisticated adopters have put the time and effort into developing their machine learning capabilities, with larger companies more likely to do so with greater resources. These larger and more sophisticated companies are investing more across a broader range of use cases. They are also the most focused on how they can use machine learning to reduce costs; 44% mentioned it as one of their use cases.

Early stage adopters are mainly focused on improving their customer retention through the application of machine learning (60%), with the middle stage adopters split between increasing customer loyalty (38%) and a growing interest in reducing costs (39%).

In general, larger and more sophisticated companies filled in more use cases overall than smaller and less mature companies: as you put resources toward and get better at ML, you get smarter about where to apply it and gain clarity on how it can help your business.

With these in mind, how are you utilizing your company’s machine learning capabilities, and how can Algorithmia help?

Get the Full Report “The State of Enterprise Machine Learning” here.