Vídeos de programación

Vídeos sobre programación y desarrollo de software.
As companies mature through their Machine Learning journey, a pattern of “many models” often emerges. In the real world, many problems can be too complex to be solved by a single machine learning model. Whether that be predicting sales for each individual store, building a predictive maintenance model for thousands of oil wells, or tailoring an experience to individual users, building a model for each instance can lead to improved results on many machine learning problems, as opposed to training a single model to make predictions for all instances. However, the infrastructure, procedures and level of automatization required to operate this kind of pattern poses a challenge at all levels. #BIGTH20 #MLOps #DevOps Session presented at Big Things Conference 2020 by María Medina, Senior Data Scientist at Microsoft and Hosein Alizadeh, Principal Data Scientist at Microsoft 18th November 2020 Home Edition Do you want to know more? https://www.bigthingsconference.com/
Intelligent systems must be able to handle the complexity and uncertainty of the real world. Markov logic enables this by unifying first-order logic and probabilistic graphical models into a single representation. Many deep architectures are instances of Markov logic. An extensive suite of learning and inference algorithms for Markov logic has been developed, along with open source implementations like Alchemy. Markov logic has been applied to natural language understanding, information extraction and integration, robotics, social network analysis, computational biology, and many other areas. #BIGTH20 #AI #Cloud #DataScience #BigData #MachineLearning Session presented at Big Things Conference 2020 by PEDRO DOMINGOS University of Washington, Professor 16th November 2020 Home Edition Do you want to know more? https://www.bigthingsconference.com/
In her talk, Nuria will describe the work that we have done within the Commissioner for AI Strategy and Data Science against COVID-19 for the President of the Valencian Region. As commissioner, I have led a multi-disciplinary team of 20+ scientists who have volunteered since March 2020. We have been working on 4 large areas: (1) human mobility modeling; (2) computational epidemiological models (both metapopulation and individual models); (3) predictive models; (4) citizen surveys, with the launch of the covid19impactsurvey, one of the largest citizen surveys about COVID-19 to date with over 300.000 answers (https://covid19impactsurvey.org) I will describe some of the work we have carried out in each of these areas and will share the lessons learned in this very special initiative of collaboration between the civil society at large (through the survey), the scientific community (through the Expert Group) and a public administration (through the Commissioner at the Presidency level) #BIGTH20 #AI #Cloud #DataScience #BigData #MachineLearning Session presented at Big Things Conference 2020 by NURIA OLIVER, PHD Co-founder and Vice President at ELLIS 16th November 2020 Home Edition Do you want to know more? https://www.bigthingsconference.com/
How technology can support business in times of crisis by José Ruiz, Paradigma Digital; Pablo Carlier, Google Cloud; Jaume Brunet, Denodo; Ignacio Cabrera, IBM; Jim Webber, Neo4j; Alicia Domarco, Fujitsu; Guy Korland, Redis Labs. #BIGTH20 #AI #Cloud #DataScience #BigData #MachineLearning Session presented at Big Things Conference 2020 16th November 2020 Home Edition Do you want to know more? https://www.bigthingsconference.com/
A typical machine learning pipeline begins as a series of preprocessing steps followed by experimentation, optimization and model-tuning, and, finally deployment. Jupyter notebooks have become a hugely popular tool for data scientists and other machine learning practitioners to explore and experiment as part of this workflow, due to the flexibility and interactivity they provide. However, with notebooks it is often a challenge to move from the experimentation phase to creating a robust, modular and production-grade end-to-end AI pipeline. Elyra is a set of open-source, AI centric extensions to JupyterLab. Elyra provides a visual editor for building notebook-based pipelines that simplifies the conversion of multiple notebooks into batch jobs or workflows. #BIGT20 #AI #Visualization #Analytics #MachineLearning #DataScience #DeepLearning, Session presented at Big Things Conference 2020 by Nick Pentreath, Principal Engineer at IBM 18th November 2020 Home Edition Do you want to know more? https://www.bigthingsconference.com/
The recent pandemic has already caused damage to economies around the world and requires a fast and accurate resolution. There are several ways of tackling the virus that range from blocking its entry into cells to inhibiting its replication. Either way, a treatment is urgently needed. Considering the length of time required for a new drug to be approved, repurposing approved drugs is a valuable option to accelerate the drug discovery process. Virtual screening plays an important role at the early stages of drug discovery. This process generally takes a long time to execute since it typically relies on measuring similarities among molecules. This is a computationally heavy and expensive exercise, and a major challenge for today’s computers. Most of the well-known methods for this type of evaluation use 2D molecular fingerprints to encode structural information. Although they are efficient in terms of execution times, these methods lack the consideration of relevant aspects of molecular structures. #BIGTH20 #VirtualScreening Session presented at Big Things Conference 2020 by Albert Mercadal,Global Head of Advanced Analytics CoE at Fujitsu and Borja Menéndez, Sr. Operation Research Engineer at Fujitsu 18th November 2020 Home Edition Do you want to know more? https://www.bigthingsconference.com/
Welcome Note at Big Things Conference 2020 The responsibility to drive the future. #BIGTH20 #AI #Cloud #DataScience #BigData #MachineLearning Session presented at Big Things Conference 2020 by Carmen Vidal, Ceo & Founder at Paradigma Digital and Óscar Méndez, CEO at Stratio. 16th November 2020 Home Edition Do you want to know more? https://www.bigthingsconference.com/
“Containers are the new ZIP format to distribute software” is a fitting description of today’s development world. However, it is not always that easy and this talk highlights the development of a container strategy for datastores over time: Docker images: A new distribution model. Docker Compose: Local demos and a little more. Helm Chart: Going from demo to production. Kubernetes Operator: Full control with upgrades, scaling,… Besides the strategy we are also discussing specific technical details and hurdles that appeared during the development. Or why the future will be a combination of Helm Chart and Operator (for now). Session presented at Big Things Conference 2020 by PHILIPP KRENN Developer at Elastic 18th November 2020 Home Edition Do you want to know more? https://www.bigthingsconference.com/
While “software is eating the world”, those who are able to best manage the huge mass of data will emerge out on the top. The batch processing model has been faithfully serving us for decades. However, it might have reached the end of its usefulness for all but some very specific use-cases. As the pace of businesses increases, most of the time, decision-makers prefer slightly wrong data sooner, than 100% accurate data later. Stream processing – or data streaming – exactly matches this usage: instead of managing the entire bulk of data, manage pieces of them as soon as they become available. In this talk, I’ll define the context in which the old batch processing model was born, the reasons that are behind the new stream processing one, how they compare, what are their pros and cons, and a list of existing technologies implementing the latter with their most prominent characteristics. I’ll conclude by describing in detail one possible use-case of data streaming that is not possible with batches: display in (near) real-time all trains in Switzerland and their position on a map. I’ll go through the all the requirements and the design. If time allows, I’ll try to impress attendees with a demo. Session presented at Big Things Conference 2020 by NICOLAS FRÄNKEL Developer Advocate at Hazelcast 18th November 2020 Home Edition Do you want to know more? https://www.bigthingsconference.com/
Machine Learning (ML) is separated into model training and model inference. ML frameworks typically use a data lake like HDFS or S3 to process historical data and train analytic models. Model inference and monitoring at production scale in real time is another common challenge using a data lake. But it’s possible to completely avoid such a data store, using an event streaming architecture. This talk compares the modern approach to traditional batch and big data alternatives and explains benefits like the simplified architecture, the ability of reprocessing events in the same order for training different models, and the possibility to build a scalable, mission-critical ML architecture for real time predictions with muss less headaches and problems. The talk explains how this can be achieved leveraging Apache Kafka, Tiered Storage and TensorFlow. Session presented at Big Things Conference 2020 by KAI WAEHNER Field CTO, Confluent 18th November 2020 Home Edition Do you want to know more? https://www.bigthingsconference.com/