As 2018 comes to a close, we’d like to take a look back to see how our readers have interacted with our blog, which articles were the most read, and what that could tell us about the field of machine learning writ large.
We know 2019 will be a year of tremendous progress in tech, and we’re relentlessly curious and eager for it. We look forward to adding more algorithms for our marketplace, expanding our AI Layer to more industries, producing interesting articles about novel tech applications, and engaging with innovators in the AI and machine learning fields.
Let’s take a look back on our year:
In March, we published Introduction to Machine Learning to give readers an in-depth look at what machine learning is at the macro and micro level. We got great engagement from this piece and know it will have staying power even as the world of AI morphs and grows.
Machine learning applications in sentiment analysis are becoming more and more popular, and conducting sentiment analysis can provide a company with continuous focus group feedback to gauge customer satisfaction and contentment. The explanation of a specific data use case in How to Perform Sentiment Analysis with Twitter Data was our ninth most read article of 2018.
A post from April on how computer vision works was insanely popular this year. Introduction to Computer Vision was shared more than 4,000 times by our readers, and provides a big-picture overview of the field of machine learning concerned with training computers to identify elements in images. It’s a hot topic in AI because of the pervasiveness of this technology. As our CEO said last year,
Used to be if the product was free you were the product , now if a product is free you are the training set.
— Diego Oppenheimer (@doppenhe) October 6, 2017
Introduction to Emotion Recognition was another tech overview article that was of much interest to curious tech readers in 2018. Like computer vision, emotion recognition trains computers to read the facial expressions of people in images to decipher their moods. This technology has many possible applications, including criminal justice: polygraph analysis, juror psychology, security surveillance systems and interrogation tactics, or in industry for fatigue monitoring for pilots and drivers.
Haven’t you always wanted to know how deep learning works without ground truth? Introduction to Unsupervised Learning is for you (and for the more than 7,500 other avid AI news consumers who have read this post since April. And no, before you ask, unsupervised learning is not about classrooms without teachers present; actually it kind of is.
Our intro posts sure were popular this year! (Perhaps in 2019 we’ll move on to intermediate posts.) Introduction to Optimizers comes in at the number four most-read article. Optimizers shape and mold machine learning models into their most accurate possible forms, and they’re the cousin of loss functions (see below).
Much is still unfolding in the machine learning software field; but hardware is just as important when running multivariate algorithms at scale. Learning the different compute modes and which is best for building and deploying ML applications was a topic of supreme interest for nearly 12,000 savvy readers out there this year. Make some time today to read Hardware for Machine Learning.
Facial recognition software was in the news a lot in 2018 so it makes sense that our post, Facial Recognition Through OpenFace was so popular. This article gives a good technical run-down of how OpenFace, a facial recognition machine learning model works.
Remember optimizers from above? Loss Functions can also evaluate machine learning models by determining how well an algorithm is modeling a dataset. Learn more about this tool in Introduction to Loss Functions, which helped educate more than 17,000 people this year.
And finally! Our number one most-read post of 2018 is Convolutional Neural Networks in PyTorch! Convolutional neural networks are algorithms that work in tandem on large projects #convoluted (typically computer vision). Check out this deep dive into the Python-based framework, PyTorch, and how it easily enables development of machine learning work flows.
We hope you’ll join us in 2019 as we take a deeper look into the most cutting-edge technology.
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.
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.
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
–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
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
–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.
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.”
“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.
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.
“Big companies should avoid building their own machine learning infrastructure. Almost every tech company I talk to is building their own custom machine learning stack and has a team that’s way too excited about doing this.” – @l2k dropping ML knowledge https://t.co/P0mOX8s9r0
— Peter Skomoroch (@peteskomoroch) November 12, 2018
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.
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!