Accelerate your digital transformation efforts with a straightforward path to AI. Adopt best practices that streamline FDA approvals, meet regulatory needs, drive down costs, and improve patient outcomes.Learn more
Drive productivity gains through enhanced automation. Gain deeper insights into quality, predictive maintenance, and supply chain operations. Improve collaboration with other partners and stakeholders. Accelerate your R+D cycle.Learn more
Improve profitability through more intelligent pricing, claims, and reserve models. Reduce loss ratios and deliver an enhanced customer experience. Leverage state-of-the-art computer vision models and IoT-enabled insights.Learn more
Leverage your wealth of data to produce better recommendation systems, customer personalization, lifetime value, churn, and fraud models, market segmentation, and supply chain systems — all while driving increased operational efficiency.Learn more
Build smarter products while automating research best practices and streamlining your model delivery process. From UAVs to remote sensing, AI is driving innovation in robotics, automotive, telecommunications, and other related industries.Learn more
AI and machine learning represent the next major shift in banking and financial services. Build and deploy innovative models to forecast risk, fight financial crime, inform investment strategies, and value assets without sacrificing security.Learn more
Build and deploy better models for product analytics, content & asset generation, sentiment analysis, personalization, recommendations, and more while enabling your team to focus on building better products and services instead of internal tooling.Learn more
Deploy machine learning on the front lines in the fight against cyber crime and other threats. Leverage state of the art machine learning systems to monitor suspicious activity, protect your IP, and gain an edge over your competition.Learn more
With AI and machine learning, organizations can make new discoveries, make smarter decisions, manage downside risks, and streamline operations. Learning how innovative companies are turning their wealth of data into valuable insights.Learn more
Democratize AI by increasing access to compute and improving collaboration among distributed research teams, students, and faculty.Learn more
Help companies accelerate their digital transformation initiatives with an easy path to AI adoption. Leverage a multi-cloud ML platform that can operate on any data or compute infrastructure. Create resource isolation with granular permissions.Learn more
Develop large scale models with great precision, ship models to production faster, and deliver more breakthroughs. Gradient offers everything a Data Scientist or Machine Learning Engineer needs in one simple platform. Remove infrastructure hurdles and create a centralized place to track your work, scale your compute on-demand, and collaborate with your team.Learn more
Transform AI from a technical skill to an an organizational capability and maximize your return on your machine learning investment. Gain unprecedented visibility into work happening across the organization. Accelerate the machine learning management lifecycle while reduce regulatory and operational risk. Iterate faster and reduce time to market.Learn more
Support your AI adoption initiatives without sacrificing governance or security. Future-proof your machine learning stack with a scalable technology stack that increases transparency, reproducibility, and speed. Optimize your compute resource utilization. Offer a centralized platform for collaboration. Reduce model ops friction for rapid model delivery and iteration.Learn more
Leverage state-of-the-art libraries like HuggingFace Transformers, BERT, and other open source libraries for machine translation, chatbots, market intelligence, auto-complete, data entry, sentiment analysis, and more.
Deep learning powered computer vision is disrupting industries from agriculture to medicine. Object detection in autonomous vehicles, AI-powered cancer diagnosis, predictive maintenance in manufacturing, and many other game-changing technologies are powered by the latest neural network architectures like R-CNNs and YOLO.
Recommender systems have been around for since the dawn of the internet but recent advancements in neural networks are helping to achieve unparalleled levels of accuracy while simultaneously simplifying complex rule-based systems with elegant code bases 1/10th of the size.
The occurrence of anomalies can be extremely rare. New deep learning based approaches like autoencoders can detect faint signals in an ocean of valid events. Use Gradient to train and deploy state of the art models that can identify unexpected items or events in realtime data feeds or static datasets.
Time series and other types of forecasting are some of the most prevalent and successful applications for machine learning. Refit pre-trained CNN and RNN models to be used across different domains like demand, churn, fraud, price, and other vital forecasting use-cases.
GANs are exploding in popularity across multiple industries, from Hollywood to healthcare. Leverage custom and pre-trained models for use cases like text-to-image translation and image enhancing.
Neural networks are exploding in popularity but are not always needed and can sometimes underperform classical ML techniques and ensemble methods (especially on smaller datasets). SVMs, decision trees, k-means clustering, random forests, gradient boosting, etc. are all supported on Gradient.
DeepMind and OpenAI are perhaps the most well known drivers of this novel technique on the research side but RL is making its way into production applications from trade execution in finance to robotics. RL is known to be extremely computationally intensive but recent advancements have heavily optimized the process to decrease the cost of leveraging this exciting new area within AI.
AutoML and Neural Architecture Search are becoming a standard tool in the toolbox for ML practitioners. The biggest hurdles to adopting these emerging techniques are access to infrastructure and having a unified hub to experiment with different trajectories, libraries and parameters. Gradient was built to simplify these two key areas.