Recommendation Systems

Recommendation systems govern the behavior of nearly every popular webservice today, such as YouTube, Reddit, Google, Netflix, Amazon, and the New York Times. In order to keep users engaged, your service must keep them interested. Using modern recommendation algorithms such as probabilistic matrix factorization and deep learning, we capitalize on the treasure trove of data collected by today's software platforms to deliver the optimal user experience.

One small part of displaying the optimal items within your product or service is A/B testing. A/B tests are a minefield for bad statistics, and can lead to misleading or even false conclusions. Problems such as "p-hacking" and "peeking" are now hot topics in major scientific publications. We will ensure that your business follows best practices to run statistically correct A/B tests.

Whether your business displays products, news articles, or user-generated content, you'll want to make use of recommendation systems so that you show the right thing to the right user at the right time. Even selecting the best color for a button or the best headline for an article can be a data-driven statistical choice.

Social Recommendations

Social recommendations differ from product recommendations in that a product is an inanimate entity, whereas a social recommendation is a recommendation for another user in the network. A user recommendation affects 2 users: the user receiving the recommendation and the user being recommended.

Good social recommendations are a necessity for friend networks and dating applications. Along with filling out a user profile and answering surveys, we can also make use of implicit user behavior by how they make use of and interact with your service. This data can be combined into a single model that can optimize any metric that is important to your business, for example: increasing the likelihood that 2 users will "follow" each other, or increasing users' "likes" and "shares".

Deep Learning, Natural Language Processing, and Computer Vision

Deep Learning is the state-of-the art when it comes to natural language and computer vision problems. One might assume that natural language processing and computer vision are for academics and research scientists, but in reality their applications are quite practical.

Some examples include:

• Sentiment analysis for stock predictions
• Spam detection for email
• Topic modeling for automatic document sorting (e.g. sorting through resumes)
• Image classification and object recognition for industrial agriculture
• Image classification for medical diagnosis
• Object detection for self-driving vehicles
• Neural machine translation (for translating languages)
• Speech recognition and language understanding for virtual assistants (Siri, Cortana, Alexa, Google Assistant)
• Facial recognition for building security and password-less phone unlocking

Predictive Modeling with Machine Learning

Although predictive modeling has been around for many decades as part of the statistics literature, predictive modeling today is far more powerful. Predictive modeling involves finding a set of good attributes that can reliably predict an outcome. Some examples include capacity planning, fraud detection, and churn prediction. By accurately predicting future outcomes, businesses can make more intelligent choices. For example, having more staff on hand when customer volume is high.

Data Processing Pipelines

The recent explosion in data collection is both a blessing and a burden. While helping us improve our modeling capabilities, it demands far greater resources than ever before, to the point that we have even done studies measuring the environmental impact of processing our data! Joe Engineer's workstation is no longer capable of processing today's data workload.

We apply horizontal scaling (also known as parallelization or distributed computing) in order to process your data across thousands of machines. Businesses are now becoming aware that reporting will suffer if data can't be processed on time and without failures. With data growing everyday, data processing pipelines must have the ability to scale. Aside from being useful for generating reports, the end results of a data processing pipeline are also required for downstream data analysis and machine learning.

Full Backend Development, from Prototype to Production

Software engineering and machine learning expertise means that your feature or product will be seen through its entire life cycle - from proof of concept to deployment to a production environment. Most often, data science and software engineering are seen as separate disciplines. Data scientists and machine learning engineers do the research and create prototypes. These are passed on to the software engineering team in order to create "production-ready" code. Synapton ensures that your feature or product is built to your specifications from start to finish, even if it means scaling to millions of users. Documentation and testing are also considered to be pivotal to good software engineering practice. If necessary, white papers and other documentation will be written for investors and stakeholders with a level of technical depth appropriate for the audience.

Training in Data Science, Machine Learning, and Artificial Intelligence

Many businesses now have their own in-house data scientists. For these businesses, a temporary collaboration in conjunction with training in modern best-practices is the most cost-effective approach. As the saying goes, "give a man a fish and feed him for a day; teach a man to fish and feed him for life". By going through our world-class training programs, state-of-the-art algorithms become part of your own team's repertoire, allowing your business to use and apply them long after our initial collaboration has ended.

Training is also extremely useful for those wishing to upgrade their career trajectory or take on a different role, and entrepreneurs who wear all the hats and want to take a data-driven approach to their business. Startup founders today find themselves not just doing sales and marketing in addition to all the other responsibilities of a CEO, but also coding. Adding data-driven decision-making to your skillset only increases your chances of success.

Web Application and Full Stack Development

Synapton has the expertise to develop and deploy your entire web application from the ground up. From database design, to web frameworks, to frontend development. Our developers are experienced with a wide array of technologies, including Postgres, MySQL, Amazon RDS, Redis, MongoDB, DynamoDB, Ruby on Rails, Flask, Django, jQuery, React, Angular, and Bootstrap.

Scalability and minimization of downtime are a top priority at Synapton. We use the latest advancements in load balancing, auto scaling, and function-as-a-service in order to build resilient and scalable web applications. Whether you prefer to use AWS (ELB, Auto Scaler, Lambda) or any other cloud service, your scaling needs will be accounted for.

Get in Touch

Contact us at info [at] synaptonai.com to inquire about how we can help your business.