Company Posts
Making neural networks available for energy consumption prediction
We created LSTEnergy, a Long-Term-Short-Memory energy forecasting model built using Blossom Sky and real-world smart meter data acquired in 2020. It is a time-series forecasting model that predicts future events using time-based previous data and is typically performed once every week per smart meter. It works by training it on a collection of past data and then attempting to predict future values using the 'smartmeter' dataset. The model is trained to learn a function that transfers a series of prior observations as input to an output observation, and is used to optimize particular processes with AI. Long Short-Term Memory (LSTM) is a form of recurrent neural network (RNN) that can learn long-term dependencies.
Company Posts
Federated Learning is necessary to avoid bias in Generative AI
As artificial intelligence becomes more integrated into our daily lives, it's important to consider the impact it has on our society. To avoid discrimination and bias, the most promising solution is federated learning, which uses a decentralized network of devices to process data, rather than relying on a central database, data warehouse or data lake. This approach helps to reduce the risk of biased results and makes it possible for a diverse group of people to contribute and help address potential biases. Join us in exploring this exciting area and how it can help shape a better future for all of us.
Company Posts
Why is federated learning so crucial for AI?
Federated learning (FL) is critical in a company's digital transformation since it enables decentralized, privacy-preserving machine learning model training. FL enables businesses to train models on decentralized data from multiple sources, resulting in more robust, scalable, and accurate models. Furthermore, FL assists businesses in overcoming data privacy concerns by allowing models to be trained without sharing raw data, lowering the risk of sensitive information being exposed or misused. FL also reduces the need for centralized data storage, processing, and maintenance, which lowers costs and increases efficiency. Businesses can also use FL to stay ahead in the fast-paced digital world and meet the changing needs of their customers and stakeholders.
Company Posts
Are You Wasting Money in the Cloud? Public cloud repatriation starting off in 2023
Public cloud displacement is a subject we don't discuss as often as we ought to. Many see relocating data and apps back from a public cloud provider to an enterprise data center as an admission that the initial decision to move the workloads to the cloud was a grave error. In my opinion, this is less of a failure than a hosting platform change depending on the current state of the economy. People frequently return to more conventional platforms because of the high expense of cloud computing. You surly remember the article from Dropbox (1), explaining why they left their public infrastructure and went to a private cloud approach.
Company Posts
Regulation-Compliant Data Processing
Today regulation-compliant data processing is a major challenge, driven by regulatory bodies across the world. In this blog post, we analyzed data transfer regulations from the perspective of GDPR and discussed key research challenges for including compliance aspects in federated data processing. Compliance is at the core of our blossom data platform. In the next blog post, we will discuss how Databloom's blossom data platform addresses some of the aforementioned challenges and ensures regulation-compliant data processing across multiple clouds, Geo-locations, and data platforms.
Company Posts
Modern data strategy: Data Mesh - should you adapt?
The truth is that not every company will be a good fit for the installation of a Data Mesh. The main target market for Data Mesh is larger businesses that deal with unpredictability and change in their environments and operations. This article explains what a data mesh is, what its fundamental components are, how data-driven enterprises benefit from them, and whether a data mesh is a good fit for you. Remember – if your organization's data needs are low and stable over time, a Data Mesh is undoubtedly a waste of money.