Algorithmia is fortunate to work with companies across many industries with varied use cases as they develop machine learning programs. We are delighted to showcase the great work one of our customers is doing and how the AI Layer is able to power their machine learning lifecycle.
Tevec is a Brazil-based company that hosts Tevec.AI, a supply chain recommendation platform that uses machine learning to forecast demand and suggest optimized replenishment/fulfillment order for logistics chains. Put simply, Tevec ensures retailers and goods transport companies deliver their products to the right place at the right time.
In founder Bento Ribeiro’s own words, the “Tevec Platform is a pioneer in the application of machine learning for the recognition of demand behavior patterns, automating the whole process of forecasting and calculation of ideal product restocking lots at points of sale and distribution centers, allowing sales planning control, service level, and regulatory stocks.”
Tevec runs forecasting and inventory-optimization models and customizes user permissions so they can adjust the parameters of their inventory routine, such as lead times, delivery dates, minimum inventory, and service levels. Users can fine-tune the algorithms and adapt for specific uses or priorities.
The challenge: serving and managing at scale
Initially, Tevec was embedding ML models directly into its platform, causing several issues:
- Updating: models and applications were on drastically different update cycles, with models changing many times between application updates
- Versioning: model iterating and ensuring all apps were calling the most appropriate model was difficult to track and prone to error
- Data integrations: manual integrations and multi-team involvement made customization difficult
- Model management: models were interacting with myriad endpoints such as ERP, PoS systems, and internal platforms, which was cumbersome to manage
Algorithmia provides the ability to not worry about infrastructure and guarantees that models we put in production will be versioned and production-quality.”
Luiz Andrade, CTO, Tevec
The solution: model hosting made simple with serverless microservices
Tevec decoupled model development from app development using the AI Layer so it can seamlessly integrate API endpoints, and users can maintain a callable library of every model version. Tevec’s architecture and data science teams now avoid costly and time-consuming DevOps tasks; that extra time can be spent on building valuable new models in Python, “the language of data science,” Andrade reasons. That said, with the AI Layer, Tevec can run models from any framework, programming language, or data connector—future-proofing Tevec’s ML program.
With Algorithmia in place, Tevec’s data scientists can test and iterate models with dependable product continuity, and can customize apps for customers without touching models, calling only the version needed for testing.
Algorithmia’s serverless architecture ensures the scalability Tevec needs to meet its customers demands without the costs of other autoscaling systems, and Tevec only pays for compute resources it actually uses.
Tevec continues to enjoy 100-percent year-on-year growth, and as it scales so will its ML architecture deployed on Algorithmia’s AI Layer. Tevec is planning additional products beyond perfect order forecasts and it is evaluating new frameworks for specific ML use cases—perfect for the tool-agnostic AI Layer. Tevec will continue to respond to customer demands as it increases the scale and volume of its service so goods and products always arrive on time at their destinations.
Algorithmia is the whole production system, and we really grabbed onto the concept of serverless microservices so we don’t have to wait for a whole chain of calls to receive a response.”
Luiz Andrade, CTO, Tevec
Read the full Tevec case study.
The financial services industry has often been at the forefront of using new technology to solve business problems. It’s no surprise that many firms in this sector are embracing machine learning, especially now that increased compute power, network connectivity, and cloud infrastructure are cheaper and more accessible.
This post will detail five important machine learning use cases that are currently providing value within financial services organizations.
The cost of financial fraud for a financial services company jumped 9 percent between 2017 and 2018, resulting in a cost of $2.92 for every dollar of fraud. We have previously discussed machine learning applications in fraud detection in detail, but it’s worth mentioning some additional reasons why this is one of the most important applications for machine learning in this sector.
Most fraud prevention models are based on a set of human-created rules that result in a binary classification of “fraud” or “not fraud.” The problem with these models is that they can create a high number of false positives. It’s not good for business when customers receive an abnormally high number of unnecessary fraud notifications. Trust is lost, and actual fraud may continue to go on undetected.
Machine learning clustering and classification algorithms can help reduce the problem of false positives. They continually modify the profile of a customer whenever they take a new action. With these multiple points of data, the machine can take a nuanced approach to determine what is normal and abnormal behavior.
Creditworthiness is a natural and obvious use of machine learning. For decades, banks have used very rudimentary logistic regression models with inputs like income 30-60-90-day payment histories to determine likelihood of default, or the payment and interest terms of a loan.
The logistic model can be problematic as it can penalize individuals with shorter credit histories or those who work outside of traditional banking systems. Banks also miss out on additional sources of revenue from rejected borrowers who would likely be able to pay.
With the growing number of alternative data points about individuals related to their financial histories (e.g., rent and utility bill payments or social media actions), lenders are able to use more advanced models to make more personalized decisions about creditworthiness. For example, a 2018 study suggests that a neural network machine learning model may be more accurate at predicting likelihood of default as compared to logistic regression or decision-tree modeling.
Despite the optimism around increased equitability for customers and a larger client base for banks, there is still some trepidation around using black box algorithms for making lending decisions. Regulations, including the Fair Credit Reporting Act, require creditors to give individuals specific reasons for an outcome. This has been a challenge for engineers working with neural networks.
Credit bureau Equifax suggests that it has found a solution to this problem, releasing a “regulatory-compliant machine learning credit scoring system” in 2018.
Simply defined, algorithmic trading is automated trading using a defined set of rules. A basic example would be a trader setting up automatic buy and sell rules when a stock falls below or rises above a particular price point. More sophisticated algorithms exploit arbitrage opportunities or predict stock price fluctuations based on real-world events like mergers or regulatory approvals.
The previously mentioned models require thousands of lines of human-written code and have become increasingly unwieldy. Relying on machine learning makes trading more efficient and less prone to mistakes. It is particularly beneficial in high frequency trading, when large volumes of orders need to be made as quickly as possible.
Automated trading has been around since the 1970s, but only recently have companies had access to the technological capabilities able to handle advanced algorithms. Many banks are investing heavily in machine learning-based trading. JPMorgan Chase recently launched a foreign exchange trading tool that bundles various algorithms including time-weighted average price and volume-weighted average price along with general market conditions to make predictions on currency values.
Robo-advisors have made investing and financial decision-making more accessible to the average person. Their investment strategies are derived from an algorithm based on a customer’s age, income, planned retirement date, financial goals, and risk tolerance. They typically follow traditional investment strategies and asset allocation based on that information. Because robo-advisors automate processes, they also eliminate the conflict of financial advisors not always working in a client’s best interest.
While robo-advisors are still a small portion of assets under management by financial services firms ($426 billion in 2018), this value is expected to more than triple by 2023. Customers are enticed by lower account minimums (sometimes $0), and wealth management companies save on the costs of employing human financial advisors.
Cybersecurity and threat detection
Although not unique to the financial services industry, robust cybersecurity protocols are absolutely necessary to demonstrate asset safety to customers. This is also a good use case to demonstrate how machine learning can play a role in assisting humans rather than attempting to replace them. Specific examples of how machine learning is used in cybersecurity include:
Malware detection: Algorithms can detect malicious files by flagging never-before-seen software attempting to run as unsafe.
Insider attacks: Monitoring network traffic throughout an organization looking for anomalies like repeated attempts to access unauthorized applications or unusual keystroke behavior.
In both cases, the tedious task of constant monitoring is taken out of the hands of an employee and given to the computer. Analysts can then devote their time to conducting thorough investigations and determining the legitimacy of the threats.
It will be important to watch the financial sector closely because its use of machine learning and other nascent applications will play a large role in determining those technologies’ use and regulation across countless other industries.
Algorithmia’s AI Layer Powers the UN Methods Service
Algorithmia is renewing its commitment to global humanitarian efforts by making powerful ML tools available to everyone.
Economic and population data are key elements of planning and decision-making in first-world countries, but access to sophisticated analytic and compute power is limited or non-existent in developing countries.
Meeting the Problem Head On
Working in conjunction with the United Nations Global Platform for Official Statistics, Algorithmia built a repository of algorithms that are readily available to any data scientist of any member state at any time. There are models for predicting economic, environmental, and social trends to enable smarter decision-making for strategies like agricultural planning, flooding probabilities, and curbing deforestation.
The United Nations Global Platform for Official Statistics sought to build the algorithm repository as part of the Sustainable Development Goals (SDGs). The SDGs aim to meet global challenges in healthcare, poverty, environmental degradation, and inequality by 2030. The algorithm repository will serve member states to “establish strategies to reuse and adapt algorithms across topics and to build implementations for large volumes of data.” UN Big Data
Building an Algorithm Marketplace for the Developing World
The UN wanted a way to share models with underdeveloped countries to curate economic, environmental, and social data to save lives and improve health and environmental conditions. Using the UN algorithm repository, for example, a developing country could model farmland satellite imagery to predict draughts, urbanization trends, or migration patterns.
Such statistical information can be used in myriad ways by both humanitarian organizations and policy-making, governmental bodies to make smarter resource-allocation decisions, better understand urban planning needs from population data, and even predict migration crop cycles using geospatial imagery.
The UN’s partnership with Algorithmia demonstrates our dedication to leveraging AI and machine learning to seek solutions to global problems. We are so looking forward to empowering the developing world, one algorithm at a time.
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
For every dollar of fraud that financial services companies suffer, they incur $2.67 in costs to their business. With more entry points in the digital age and increasingly sophisticated attackers, tackling fraud manually is quickly fading to irrelevance: but machine learning offers a promising way to automate the process, as well as surface more nuanced fraud patterns.
This post will walk through the challenges of applying ML models to fraud detection, popular applications, and tradeoffs to think about in model selection.