Financial analysis is an important tool for evaluating company performance. Practitioners work to answer financial questions to make profitable investment decisions, and use advanced quantitative analyses to do so. As a result, Financial Question Answering (QA) is a question answering task that requires deep reasoning about numbers. Furthermore, it is unknown how well pre-trained language models can reason in the financial domain. The current state-of-the-art requires a retriever to collect relevant facts about the financial question from the text and a generator to produce a valid financial program and a final answer. However, recently large language models like GPT-3 have achieved state-of-the-art performance on wide variety of tasks with just a few shot examples. We run several experiments with GPT-3 and find that a separate retrieval model and logic engine continue to be essential components to achieving SOTA performance in this task, particularly due to the precise nature of financial questions and the complex information stored in financial documents. With this understanding, our refined prompt-engineering approach on GPT-3 achieves near SOTA accuracy without any fine-tuning.

Following our previous posts on how to improve results when using Machine Learning on imbalanced data, in this post, we'll talk about oversampling and undersampling. We'll discuss a particular example that will help us see how these approaches can be combined with an automated search to create a well-tuned model from our data. The data we're using to illustrate this post was published as one of Kaggle's contests: Give Me Some Credit. You can download the CSV from Kaggle's site or better yet you can quickly import it into BigML cloning it from our gallery. The data describes examples of loans that ended up becoming seriously delinquent (or not) in two years.

As shown by the example in this post, we collected enough images, uploaded them, and annotated them with regions and labels. Then we created datasets and trained a Deepnet to perform Object Detection. We also evaluated the model and used it to predict new images that detected objects accurately. All of these tasks were done on the Dashboard with a few clicks. This is as accessible as it gets in Machine Learning. And just as our motto suggests, BigML has made Object Detection beautifully simple for everyone. Be sure to visit the release page of BigML Object Detection, where you can find more information and documentation.

We are about to wrap up 2022, a year that brought plenty of Machine Learning projects, events, education opportunities, and many groundbreaking Machine Learning applications developed by ML practitioners around the world. The challenges and business needs of our customers continue to fuel our passion to bring to life the robust and innovative Machine Learning solutions they deserve. In this blog post, we put together the highlights of 2022 covering Machine Learning's lasting impact on a vast number of industries and businesses, BigML's new additions and enhancements to our pioneering Machine Learning software platform, our live and virtual events, education initiative updates, and much more! None of the numbers listed above and the activities described on this blog post would be possible without our customers, partners, followers, and certified practitioners. That's why this blog post is dedicated to all of you.

As Machine Learning use grows, the need for engineering solutions to cover all the diversity of real end-to-end scenarios that arise becomes more obvious. Originally, people mainly focused on creating and tuning the best model that your data could produce. Nowadays, that task can be handled nicely by automated procedures like OptiML and AutoML, which will smartly find the best combination of model types and parameters for the business problem at hand. But still, once we find the right model the challenge of building the right framework to use it as a piece of software ready for production remains. In short, we need actionable models.

Machine Learning as a Service (MLaaS) is a big deal in the cloud market. Forbes predicted that the global machine learning market would go from $7.3B to $30.6B by 2024. To fuel this growth, data scientists and ML engineers are tasked with building more models to keep up with the ever dynamic business needs of customers and shareholders. One need not have a deep knowledge of ML techniques to get the most out of it which makes BigML stand out in the market. Founded in 2011 by serial entrepreneur Francisco J.

As Machine Learning solutions to real-world problems spread, people are beginning to acknowledge the glaring need for solutions that go beyond training a single model and deploying it. The simplest process should at least cover feature extraction, feature generation, modeling, and monitoring in a traceable and reproducible way. In BigML, it's been a while since we realized that, and the platform has constantly added features designed to help our users easily build both basic and complex solutions. Those solutions often need to be deployed in particular environments. Our white-box approach is totally compatible with that, as users can download the models created in BigML and predict with them wherever needed by using bindings to Python or other popular programming languages.

The BigML platform keeps making an impact by enabling many predictive use cases and solutions in a multitude of industries. In case you were unable to attend our recent virtual events showcasing these real-life examples, we have compiled the following videos explaining how Machine Learning is currently being put to use in areas like Mobility and GRC (Governance, Business Risk Management, and Compliance), as well as on low-power devices (EdgeML).

BigML's no/low-code approach to ML provides entry for a much larger audience than just PhDs and data scientists that most other tools target. Machine Learning as a Service (MLaaS) is a big deal in the cloud market. Forbes predicted that the global machine learning market would go from $7.3B to $30.6B by 2024. To fuel this growth, data scientists and ML engineers are tasked with building more models to keep up with the ever dynamic business needs of customers and shareholders. One need not have a deep knowledge of ML techniques to get the most out of it which makes BigML stand out in the market.

Continuing with our Object Detection release blog posts series, today, we'll showcase how to automate the training of the object detection models (and their predictions) that anyone will be able to create in BigML in short order. As discussed in previous posts, BigML already offers classification, regression, and unsupervised learning models (e.g., clustering, anomaly detection). They all accept images as just another input data type usable for model training. In fact, when images are uploaded a new Source is created for each and their corresponding IDs are added to a new Composite Source object with a new image field type. In summary, images can be combined with any other data type and can be assigned one or more labels by using the new label fields.