Ensemble

Deciding how to handle high dimensional datasets in ML can be tough, and every approach has their drawbacks. We've put together an overview of some of the most popular approaches out there (plus how Dark Matter compares!)

DeepSeek just trained its R1 model in 55 days and for just $5.58M. While the exact cost of the Nvidia chips used wasn’t released. Markets panicked at the news, and Nvidia stock dropped precipitously. The visceral fear? Computing hardware has suddenly lost its value. That’s the wrong takeaway. If DeepSeek really hit OpenAI-level performance at that fractional cost (95% cheaper than US comparables), it means Nvidia’s chips are delivering more to training per dollar—not less. DeepSeek cleverly extracted more efficiency from existing resources and predictably saw more value. This counterposition of thinking is the same principle behind the ML research at Ensemble. Our product, Dark Matter, transforms the training of ML, drastically cutting compute requirements and data preparation required for a great model. Today, Dark Matter saves 70-90% for ML models training on time series and tabular data - but that’s just the start. Soon we will be taking Dark Matter multimodal. Efficiency isn’t just about saving money. It’s about getting more from what you already have. The winners in AI won’t just cut costs—they’ll extract every ounce of value from their infrastructure, and we intend to democratize this power. Stay tuned. #DeepSeek #CostEffectiveAI #Nvidia

DeepSeek just trained its R1 model in 55 days and for just $5.58M. While the exact cost of the Nvidia chips used wasn’t released. Markets panicked at the news, and Nvidia stock dropped precipitously. The visceral fear? Computing hardware has suddenly lost its value. That’s the wrong takeaway. If DeepSeek really hit OpenAI-level performance at that fractional cost (95% cheaper than US comparables), it means Nvidia’s chips are delivering more to training per dollar—not less. DeepSeek cleverly extracted more efficiency from existing resources and predictably saw more value. This counterposition of thinking is the same principle behind the ML research at Ensemble. Our product, Dark Matter, transforms the training of ML, drastically cutting compute requirements and data preparation required for a great model. Today, Dark Matter saves 70-90% for ML models training on time series and tabular data - but that’s just the start. Soon we will be taking Dark Matter multimodal. Efficiency isn’t just about saving money. It’s about getting more from what you already have. The winners in AI won’t just cut costs—they’ll extract every ounce of value from their infrastructure, and we intend to democratize this power. Stay tuned. #DeepSeek #CostEffectiveAI #Nvidia

The banking industry is at a critical juncture. As an article in The Financial Brand suggests, only 10% of banks may survive the AI revolution due to its transformative impact on efficiency and customer engagement. Whether this comes to pass or not, in banking the focus remains on survival of the fittest —cost-cutting, risk mitigation, and operational efficiency. These are all compelling points, but what about the growth factor? At Ensemble , we’ve shown how ML can be leveraged to drive customer growth. Our case study on banking customer churn prediction demonstrates how advanced ML pipelines, powered by our proprietary Dark Matter algorithm, can enable banks to predict churn with unparalleled accuracy. Ensemble enables banks to turn data into opportunities—proactively meeting customer needs and fostering growth, not just preventing losses. AI & ML's future in banking isn’t just about survival; it’s about delivering value. Check out our case study and get in touch for a demo (https://buff.ly/3DEWW96).

In astrophysics, dark matter refers to the invisible substance that determines the organization of galaxies on grand scales. We can't observe it directly, but know it is essential for our calculations to make sense. At Ensemble, we borrow this idea to describe what our product does for machine learning. When building ML models, we work with data that tends to be sparse, statistically complex, or low sample size (or all three!). Traditional feature engineering captures the obvious signals, but there’s always hidden, unobserved information — like "dark matter"— that your model overlooks. This is where Ensemble’s breakthrough comes in. Using novel statistical theory, we identify and extract new, unlabeled variables that are custom-derived to enhance the performance of each of your specific models. What makes these features special is that they are ???????????? ???????????? ????????????????????, meaning they add unique and meaningful signals without redundancy — all derived from the data you already have. The result? Better predictions from the same models and datasets, whether you’re using deep learning or a simple linear regression. With just three lines of code, you unlock a new level of data richness and predictive power. Let’s start thinking beyond what’s visible in our datasets and start leveraging the "dark matter" of machine learning. ??Could your models benefit from discovering their hidden dimensions? #MachineLearning #DataScience #FeatureEngineering #PredictiveAnalytics

In astrophysics, dark matter refers to the invisible substance that determines the organization of galaxies on grand scales. We can't observe it directly, but know it is essential for our calculations to make sense. At Ensemble, we borrow this idea to describe what our product does for machine learning. When building ML models, we work with data that tends to be sparse, statistically complex, or low sample size (or all three!). Traditional feature engineering captures the obvious signals, but there’s always hidden, unobserved information — like "dark matter"— that your model overlooks. This is where Ensemble’s breakthrough comes in. Using novel statistical theory, we identify and extract new, unlabeled variables that are custom-derived to enhance the performance of each of your specific models. What makes these features special is that they are ???????????? ???????????? ????????????????????, meaning they add unique and meaningful signals without redundancy — all derived from the data you already have. The result? Better predictions from the same models and datasets, whether you’re using deep learning or a simple linear regression. With just three lines of code, you unlock a new level of data richness and predictive power. Let’s start thinking beyond what’s visible in our datasets and start leveraging the "dark matter" of machine learning. ??Could your models benefit from discovering their hidden dimensions? #MachineLearning #DataScience #FeatureEngineering #PredictiveAnalytics

In the competitive world of online retail, precision in predicting customer behavior isn't just nice to have — it's a game changer. In a recent case study, Ensemble's Dark Matter reduced wasteful spending and engaged customers more effectively. Let’s break this down. ???????????????????????? ????????????????: ? Monthly Website Traffic: 100,000 visitors ? Current Conversion Rate: 2% (2,000 conversions) ? Average Order Value (AOV): $100 ? Customer Acquisition Cost (CAC): $50 per customer With these figures, the monthly revenue stands at $200,000, and the total spent on acquiring these customers is $100,000 [2000 conversions * $50 CAC]. This could definitely be better! On a public domain customer conversion dataset (Kaggle), we demonstrated that when boosted with Dark Matter, popular ML models including LightGBM and XGBoost predicted customer conversions with average 5% greater accuracy vs. baseline. Let’s assume that increased accuracy in customer conversion identification yields a ?? conservative 1% increase in conversion rates, elevating it to 3%, ?? with a modest 10% reduction in CAC to $45/customer, thanks to the insights that Dark Matter uncovers, saving time and $. ??????????????: ? Monthly Website Traffic: 100,000 visitors (stays the same)?? ? Current Conversion Rate: 3% (3,000 conversions) ?? ? Average Order Value (AOV): $100 (stays the same) ?? ? Customer Acquisition Cost (CAC): $45 (Down 10%)?? ? Monthly revenue grows from $200,000 to $300,000 ?? ? CAC adjusts to $135,000 (for 3,000 conversions) ?? ? Net Gain:?? $65,000/month ?????? What does this mean in practical terms? ? Stop overspending on incentives for unlikely converters. ? Unlock actionable insights to craft engagement strategies that work. Want to discuss how to increase revenue and marketing ROI? Get in touch for a demo @ Ensemble. #ml #predictiveanalytics #marketing

Feature engineering doesn’t have to take weeks—what if you could accelerate your process and get results in hours or days? In industries like pharma and biotech, where the stakes are high and time is short, speeding up the machine learning pipeline can make all the difference, especially when downstream, scientists and business development teams are counting on you. Enter Dark Matter from Ensemble. Here's How: ?????????????????? ??????????????????????: Instead of spending weeks pulling data and experimenting with features, Dark Matter automates the creation of task-specific features, extracting meaningful signal from your existing datasets that you can count on — no external data required. ???????????? ????????????????????: By enriching your data and simplifying statistical preprocessing, you can quickly test and refine models — up to 90% faster training times on large datasets (hundreds of millions of datapoints!) without hurting performance. ???????????? ????????????, ???????? ????????????: Whether you're working with deep learning or traditional ML models, Dark Matter gives you instant access to rich, highly relevant features, improving model performance without the need for extensive re-engineering. With Ensemble, you can spend less time preparing data and more time generating insights—empowering your team to iterate faster and achieve breakthroughs in record time.? Ready to see how we can accelerate your workflow? Learn more about Dark Matter and let’s start the conversation. #PharmaTech #FeatureEngineering #MachineLearning #DataScience

Biopharma companies generate a lot of proprietary data. But as the Benchling State of Tech in Biopharma report indicates, large and small Biopharma companies struggle to successfully leverage ML solutions in their discovery pipelines. At Ensemble, we’re meeting this industry need with scalable technology that automates data mapping and integration. Our “Dark Matter” solution plugs into existing data pipelines to uncover meaningful patterns and insights across complex data sets. The result? A machine learning stack that measurably accelerates decision-making and innovation. The proof? Consider our case study focused on the open-source Kinase Cancer Inhibitor dataset. Dark Matter demonstrably boosted the performance of linear and non-linear classification algorithms in identifying top-performing inhibitors. (Link: https://buff.ly/3ZKvi3a ) Better data = Better predictions. Get in touch for a demo or to learn more. https://buff.ly/3BbNKbp #BioPharma #AIReady #DrugDiscovery #Innovation#DataScience

In statistical modeling, unobserved confounders are unknown factors or relationships in data that limit accuracy. In empirical contexts, researchers account for them by drawing on an array of complex, time-consuming methods and strategies. Deep learning models can identify much of their behavior, but only with costly retraining and tuning. Ensemble takes a different approach that assumes unknowns at the outset, and our solution has proven to be faster and highly effective for even high-dimensional feature sets. ?Our supervised, task-specific embedding model seamlessly plugs into the feature engineering step of your ML pipeline to enhance data quality, capture complex, non-linear relationships, and identify unobserved confounders. The result? An embedding that provides your end ML model usable information that saves engineering and compute costs. Learn more: https://buff.ly/4gblZ24 #DataScience #MachineLearning #DeepLearning #AI