ElectrifAi Offers New Machine Learning Models for Amazon SageMaker
Delivering fast and reliable machine learning business solutions
ElectrifAi, one of the global leading companies in practical artificial intelligence (AI) and pre-built machine learning (ML) models, today announced that it is releasing one of the world’s largest collections of pre-trained and pre-structured ML models for Amazon SageMaker to offer for sale. Amazon SageMaker is a fully managed service from Amazon Web Services (AWS) that provides every developer and data scientist with the ability to build, train, and deploy ML models quickly.
Delivering fast and reliable results, ElectrifAi is pleased to announce that 36 pre-trained ML models for Amazon SageMaker have been added to AWS Marketplace. ElectrifAi’s domain expertise across several verticals (including Banking, Financial Services and Insurance, Communications, Media, Entertainment, Healthcare and Consumer/Retail) coupled with 16 years of ML experience provide quick-to-deploy enterprise solutions. Clients can now see results in just days and weeks as opposed to taking 12 to 18 months since these solutions turn data into actionable insights.
AWS Marketplace users are now able to unlock insights on data using ElectrifAi’s ML models that are based on industry specific use cases. This allows for rapid deployment, scaling, and generation of complex insights that drive immediate cost, profit, and performance improvement. Since 2004, ElectrifAi has been driving quick time-to-value and successful business outcomes. Customers have received out-of-the-box solutions that save them time and money compared to building models in-house.
“We’ve been doing machine learning for 16+ years and have deep expertise across many verticals. Our machine learning models help our clients drive rapid cost, profit, and performance improvement,” said ElectrifAi Chief Executive Officer Ed Scott.
Through discovery conversations with clients, we help identify business problems that ElectrifAi clients are trying to solve and hope to achieve. After initial discussions and analyzing the data, ElectrifAi determines the appropriate use-case pre-trained model that most accurately fits the customer’s requirements to achieve an expected outcome. Once a common data model is provided, ElectrifAi trains the model with client’s data in their own environment to generate the signals related to the expected outcome. No model is 100% accurate; but with ElectrifAi’s experience and domain expertise, the model can be hyper-tuned to fit the client’s goals and risk tolerance. ElectrifAi calls such models “Pre-structured”, as they require minimal modification and tuning. These Pre-structured models navigate the nuances that vary from client to client within a given industry. For churn mitigation as an example, ElectrifAi applies several pre-structured models for segmentation and propensity modelling. ElectrifAi has a long history of building ML models, hyper-tuning the models to achieve accurate insights, and orchestrating multiple models to build use cases to deliver successful business outcomes.
Luming Wang, CTO, ElectrifAi says “We have a process we call the machine learning model factory with pre-trained, pre-structured, and brand-new models made to address client specific pain points. The model is explainable and tests for bias, resulting in easy-to-understand actionable insights that help solve real business problems.”
Jim McGowan, Head of Product, ElectrifAi says “When building a model for your company specific to an industry, we may already have some of the parts that we can provide very quickly with a lot of transparency so you can build on top of that and get where you’re going much faster. You would also have the support of an additional team of data scientists who can help with re-training or customizations of these models. We’re not just selling software, because we understand the domain and can build the models to solve for an end business use case. In a world of decreasing budgets and increasing demands, we can provide ready-to-go solutions while you work on other problems strategically important for your company.”