“Houston, we have a model...” Introduction to MLOps

•

1 like•244 views

The document introduces MLOps (Machine Learning Operations) and the need to operationalize machine learning models beyond just model deployment. It discusses challenges like data and model drift, retraining models, software dependencies, monitoring models in production, and the need for automation, testing, and reproducibility across the full machine learning lifecycle from data to deployment. An example MLOps workflow is shown using GitHub and Azure ML to enable experiment tracking, automation, and continuous integration and delivery of models.

Rui Quintino
AI & Analytics Strategy Advisor
DevScope
20210219
“Houston,
we have a model...”
Introduction to MLOps

We have a model... It’s great!
YOU
ARE
HERE

Easy right?
https://cloud.google.com/solutions/machine-learning/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning

Or Data Drift -> causing Model Performance degradation?
https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/work-with-data/datadrift-tutorial/datadrift-tutorial.ipynb

Plus...
• How/who can we retrain if needed (ex: data drift)?
• What are the software requirements?
• How can we later update lib/os versions? (ex: security)
• How can we monitor model performance in production?
• How do we ensure data checks in production?
• How was the model trained? What was the performance?
• How to scale? And Reproduce?
• ...

Model & ML code just tip of the iceberg...

Model & ML code just tip of the iceberg...
https://cloud.google.com/solutions/machine-learning/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning

Introducing MLOps – Machine Learning & Operations

DevOps MLOps
Code testing
Code reproducibility
App deployment
Model retraining
Model validation
Model reproducibility
Model deployment

Build app
Collaborate Test app Deploy app Monitor app
App developer

Model reproducibility Model retraining
Model deployment
Model validation
Train model Validate
model
Deploy
model
Monitor
model
Retrain model
Build app
Collaborate Test app Release app Monitor app
Audit /interpretability
App developer
Data scientist

Lets try a new hyper param configuration...

Compute, Experiment, Model, Deployment in Azure ML

Tracking critical for MLOps, but also for Experimenting

With GitHub & CML
• Automation for
Experimentation
• Ex: Smoke Test Locally (Fast)
• Queue Reproducible in Github
• Using notebooks
Experimentation

GitHub Workflows to support Experiment Queues
Check Continuous ML (CML) framework in Github

Branch for new experiment, change code, queue in GitHub

Commit & Push branch to queue workflow in GitHub

Notebook execution using papermill on Github agent

Train performance results on commits (also PRs)

MLOps Recap
•From Models to Pipelines
•Execution of Pipelines produce models
•Train for Deployment is fully automated/reproducible
•Pipelines enable auto model retraining
•Automation, Automation, Automation...
•Monitoring for Data Drift, Concept Drift, Model Drift
•Tracking, Metadata & Artifact Stores

Mature MLOps Example (Level 1)
https://cloud.google.com/solutions/machine-learning/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning

Not best fit for interative/model development

Final notes
•MLOps doesn’t guarantee ML success
•MLOps should improve feedback and learning
(not block DataScientists from business/data understanding)
•Dont reinvent the wheel...
•Still a young field
Dont believe everything you read in docs (and ppt...)
•When/If to introduce MLOps
(it depends...)

Also
• https://azure.microsoft.com/en-us/services/machine-
learning/mlops/
• https://cloud.google.com/solutions/machine-learning/mlops-
continuous-delivery-and-automation-pipelines-in-machine-
learning
• https://github.com/microsoft/MLOps
• https://databricks.com/blog/2020/10/13/using-mlops-with-
mlflow-and-azure.html
• https://github.com/Azure/MachineLearningNotebooks

© 2016 DevScope. All rights reserved.
Rua Passos Manuel Nº 223 – 4º Andar
4000-385 Porto
T. +351 223 751 350/51
F. +351 223 751 352
Av. Sidónio Pais, Nº 2 – 3º Andar
1050-214 5 Lisboa
info@devscope.net
www.devscope.net

“Houston, we have a model...” Introduction to MLOps

MLOps (a compound of “machine learning” and “operations”) is a practice for collaboration and communication between data scientists and operations professionals to help manage the production machine learning lifecycle. Similar to the DevOps term in the software development world, MLOps looks to increase automation and improve the quality of production ML while also focusing on business and regulatory requirements. MLOps applies to the entire ML lifecycle - from integrating with model generation (software development lifecycle, continuous integration/continuous delivery), orchestration, and deployment, to health, diagnostics, governance, and business metrics. To watch the full presentation click here: https://info.cnvrg.io/mlopsformachinelearning In this webinar, we’ll discuss core practices in MLOps that will help data science teams scale to the enterprise level. You’ll learn the primary functions of MLOps, and what tasks are suggested to accelerate your teams machine learning pipeline. Join us in a discussion with cnvrg.io Solutions Architect, Aaron Schneider, and learn how teams use MLOps for more productive machine learning workflows. - Reduce friction between science and engineering - Deploy your models to production faster - Health, diagnostics and governance of ML models - Kubernetes as a core platform for MLOps - Support advanced use-cases like continual learning with MLOps

MLOps in action

Pieter de Bruin

MLOps Bridging the gap between Data Scientists and Ops.

Knoldus Inc.

Ml ops intro session

Avinash Patil

Ml ops past_present_future

Nisha Talagala

MLOps Using MLflow

Databricks

MLflow is an MLOps tool that enables data scientist to quickly productionize their Machine Learning projects. To achieve this, MLFlow has four major components which are Tracking, Projects, Models, and Registry. MLflow lets you train, reuse, and deploy models with any library and package them into reproducible steps. MLflow is designed to work with any machine learning library and require minimal changes to integrate into an existing codebase. In this session, we will cover the common pain points of machine learning developers such as tracking experiments, reproducibility, deployment tool and model versioning. Ready to get your hands dirty by doing quick ML project using mlflow and release to production to understand the ML-Ops lifecycle.

MLOps and Data Quality: Deploying Reliable ML Models in Production

Provectus

Looking to build a robust machine learning infrastructure to streamline MLOps? Learn from Provectus experts how to ensure the success of your MLOps initiative by implementing Data QA components in your ML infrastructure. For most organizations, the development of multiple machine learning models, their deployment and maintenance in production are relatively new tasks. Join Provectus as we explain how to build an end-to-end infrastructure for machine learning, with a focus on data quality and metadata management, to standardize and streamline machine learning life cycle management (MLOps). Agenda - Data Quality and why it matters - Challenges and solutions of Data Testing - Challenges and solutions of Model Testing - MLOps pipelines and why they matter - How to expand validation pipelines for Data Quality

Команда Data Phoenix Events приглашает всех, 17 августа в 19:00, на первый вебинар из серии "The A-Z of Data", который будет посвящен MLOps. В рамках вводного вебинара, мы рассмотрим, что такое MLOps, основные принципы и практики, лучшие инструменты и возможные архитектуры. Мы начнем с простого жизненного цикла разработки ML решений и закончим сложным, максимально автоматизированным, циклом, который нам позволяет реализовать MLOps. https://dataphoenix.info/the-a-z-of-data/ https://dataphoenix.info/the-a-z-of-data-introduction-to-mlops/

MLOps - The Assembly Line of ML

Jordan Birdsell

The catalyst for the success of automobiles came not through the invention of the car but rather through the establishment of an innovative assembly line. History shows us that the ability to mass produce and distribute a product is the key to driving adoption of any innovation, and machine learning is no different. MLOps is the assembly line of Machine Learning and in this presentation we will discuss the core capabilities your organization should be focused on to implement a successful MLOps system.

Apply MLOps at Scale

Databricks

Apply MLOps at Scale by H&M

Databricks

Using MLOps to Bring ML to Production/The Promise of MLOps

Weaveworks

In this final Weave Online User Group of 2019, David Aronchick asks: have you ever struggled with having different environments to build, train and serve ML models, and how to orchestrate between them? While DevOps and GitOps have made huge traction in recent years, many customers struggle to apply these practices to ML workloads. This talk will focus on the ways MLOps has helped to effectively infuse AI into production-grade applications through establishing practices around model reproducibility, validation, versioning/tracking, and safe/compliant deployment. We will also talk about the direction for MLOps as an industry, and how we can use it to move faster, with more stability, than ever before. The recording of this session is on our YouTube Channel here: https://youtu.be/twsxcwgB0ZQ Speaker: David Aronchick, Head of Open Source ML Strategy, Microsoft Bio: David leads Open Source Machine Learning Strategy at Azure. This means he spends most of his time helping humans to convince machines to be smarter. He is only moderately successful at this. Previously, David led product management for Kubernetes at Google, launched GKE, and co-founded the Kubeflow project. David has also worked at Microsoft, Amazon and Chef and co-founded three startups. Sign up for a free Machine Learning Ops Workshop: http://bit.ly/MLOps_Workshop_List Weaveworks will cover concepts such as GitOps (operations by pull request), Progressive Delivery (canary, A/B, blue-green), and how to apply those approaches to your machine learning operations to mitigate risk.

MLOps Virtual Event: Automating ML at Scale

Databricks

MLOps - Build pipelines with Tensor Flow Extended & Kubeflow

Jan Kirenz

From Data Science to MLOps

Carl W. Handlin

Seamless MLOps with Seldon and MLflow

Databricks

Deploying and managing machine learning models at scale introduces new complexities. Fortunately, there are tools that simplify this process. In this talk we walk you through an end-to-end hands on example showing how you can go from research to production without much complexity by leveraging the Seldon Core and MLflow frameworks. We will train a set of ML models, and we will showcase a simple way to deploy them to a Kubernetes cluster through sophisticated deployment methods, including canary deployments, shadow deployments and we’ll touch upon richer ML graphs such as explainer deployments.

MLOps with serverless architectures (October 2018)

Julien SIMON

MLOps with Kubeflow

Saurabh Kaushik

Pythonsevilla2019 - Introduction to MLFlow

Fernando Ortega Gallego

ML-Ops how to bring your data science to production

Herman Wu

MLflow: A Platform for Production Machine Learning

Matei Zaharia

Introducing MLOps.pdf

Dr. Anish Cheriyan (PhD)

MLOps.pptx

AllenPeter7

MLOps and Reproducible ML on AWS with Kubeflow and SageMaker

Provectus

Looking to implement MLOps using AWS services and Kubeflow? Come and learn about machine learning from the experts of Provectus and Amazon Web Services (AWS)! Businesses recognize that machine learning projects are important but go beyond just building and deploying models, which is mostly done by organizations. Successful ML projects entail a complete lifecycle involving ML, DevOps, and data engineering and are built on top of ML infrastructure. AWS and Amazon SageMaker provide a foundation for building infrastructure for machine learning while Kubeflow is a great open source project, which is not given enough credit in the AWS community. In this webinar, we show how to design and build an end-to-end ML infrastructure on AWS. Agenda - Introductions - Case Study: GoCheck Kids - Overview of AWS Infrastructure for Machine Learning - Provectus ML Infrastructure on AWS - Experimentation - MLOps - Feature Store Intended Audience Technology executives & decision makers, manager-level tech roles, data engineers & data scientists, ML practitioners & ML engineers, and developers Presenters - Stepan Pushkarev, Chief Technology Officer, Provectus - Qingwei Li, ML Specialist Solutions Architect, AWS Feel free to share this presentation with your colleagues and don't hesitate to reach out to us at info@provectus.com if you have any questions! REQUEST WEBINAR: https://provectus.com/webinar-mlops-and-reproducible-ml-on-aws-with-kubeflow-and-sagemaker-aug-2020/

ML-Ops:Philosophy, Best-Practices and Tools

Jorge Davila-Chacon

Slides-Артем Коваль-Cloud-Native MLOps Framework - DataFest 2021.pdf

vitm11

Trenowanie i wdrażanie modeli uczenia maszynowego z wykorzystaniem Google Clo...

Sotrender

Okej, mam już mój świetny model w Notebooku, co dalej? Większość kursów i źródeł dotyczących uczenia maszynowego dobrze przygotowuje nas do implementacji algorytmów uczenia maszynowego i budowy mniej lub bardziej skomplikowanych modeli. Jednak w większości przypadków model jest jedynie małym fragmentem większego systemu, a jego wdrożenie i utrzymywanie okazuje się w praktyce procesem czasochłonnym i generującym rozmaite błędy. Problem potęguje się kiedy mamy do sproduktyzowania nie jeden, a więcej modeli. Choć z roku na rok powstaje coraz więcej narzędzi i platform do usprawnienia tego procesu, jest to zagadnienie któremu wciąż poświęca się stosunkowo mało uwagi. W mojej prezentacji przedstawię jakich podejść, dobrych praktyk oraz narzędzi i usług Google Cloud Platform używamy w Sotrender do efektywnego trenowania i produktyzacji naszych modeli ML, służących do analizy danych z mediów społecznościowych. Omówię na które aspekty DevOps zwracamy uwagę w kontekście wytwarzania produktów opartych o modele ML (MLOps) i jak z wykorzystaniem Google Cloud Platform można je w łatwy sposób wdrożyć w swoim startupie lub firmie. Prezentacja Macieja Pieńkosza z Sotrendera poczas Data Science Summit 2020

What's hot

MLOps by Sasha Rosenbaum

Sasha Rosenbaum

What is MLOps

Henrik Skogström

The A-Z of Data: Introduction to MLOps

DataPhoenix

MLOps - The Assembly Line of ML

Jordan Birdsell

Apply MLOps at Scale

Databricks

Apply MLOps at Scale by H&M

Databricks

Using MLOps to Bring ML to Production/The Promise of MLOps

Weaveworks

MLOps Virtual Event: Automating ML at Scale

Databricks

MLOps - Build pipelines with Tensor Flow Extended & Kubeflow

Jan Kirenz

From Data Science to MLOps

Carl W. Handlin

Seamless MLOps with Seldon and MLflow

Databricks

MLOps with serverless architectures (October 2018)

Julien SIMON

MLOps with Kubeflow

Saurabh Kaushik

Pythonsevilla2019 - Introduction to MLFlow

Fernando Ortega Gallego

ML-Ops how to bring your data science to production

Herman Wu

MLflow: A Platform for Production Machine Learning

Matei Zaharia

Introducing MLOps.pdf

Dr. Anish Cheriyan (PhD)

MLOps.pptx

AllenPeter7

MLOps and Reproducible ML on AWS with Kubeflow and SageMaker

Provectus

ML-Ops:Philosophy, Best-Practices and Tools

Jorge Davila-Chacon

What's hot (20)

MLOps by Sasha Rosenbaum

What is MLOps

The A-Z of Data: Introduction to MLOps

MLOps - The Assembly Line of ML

Apply MLOps at Scale

Apply MLOps at Scale by H&M

Using MLOps to Bring ML to Production/The Promise of MLOps

MLOps Virtual Event: Automating ML at Scale

MLOps - Build pipelines with Tensor Flow Extended & Kubeflow

From Data Science to MLOps

Seamless MLOps with Seldon and MLflow

MLOps with serverless architectures (October 2018)

MLOps with Kubeflow

Pythonsevilla2019 - Introduction to MLFlow

ML-Ops how to bring your data science to production

MLflow: A Platform for Production Machine Learning

Introducing MLOps.pdf

MLOps.pptx

MLOps and Reproducible ML on AWS with Kubeflow and SageMaker

ML-Ops:Philosophy, Best-Practices and Tools

Similar to “Houston, we have a model...” Introduction to MLOps

Slides-Артем Коваль-Cloud-Native MLOps Framework - DataFest 2021.pdf

vitm11

Trenowanie i wdrażanie modeli uczenia maszynowego z wykorzystaniem Google Clo...

Sotrender

Building a MLOps Platform Around MLflow to Enable Model Productionalization i...

Databricks

Getting machine learning models to production is notoriously difficult: it involves multiple teams (data scientists, data and machine learning engineers, operations, …), who often does not speak to each other very well; the model can be trained in one environment but then productionalized in completely different environment; it is not just about the code, but also about the data (features) and the model itself… At DataSentics, as a machine learning and cloud engineering studio, we see this struggle firsthand – on our internal projects and client’s projects as well.

artificggggggggggggggialintelligence.pdf

tt4765690

Mohamed Sabri: Operationalize machine learning with Kubeflow

Lviv Startup Club

Mohamed Sabri: Operationalize machine learning with Kubeflow

Edunomica

Deployment Design Patterns - Deploying Machine Learning and Deep Learning Mod...

All Things Open

AllThingsOpen 2018 - Deployment Design Patterns (Dan Zaratsian)

dtz001

While the adoption of machine learning and deep learning techniques continue to grow, many organizations find it difficult to actually deploy these sophisticated models into production. It is common to see data scientists build powerful models, yet these models are not deployed because of the complexity of the technology used or lack of understanding related to the process of pushing these models into production. As part of this talk, I will review several deployment design patterns for both real-time and batch use cases. I’ll show how these models can be deployed as scalable, distributed deployments within the cloud, scaled across hadoop clusters, as APIs, and deployed within streaming analytics pipelines. I will also touch on topics related to security, end-to-end governance, pitfalls, challenges, and useful tools across a variety of platforms. This presentation will involve demos and sample code for the the deployment design patterns.

Apache Spark Model Deployment

Databricks

Tech-Talk at Bay Area Spark Meetup Apache Spark(tm) has rapidly become a key tool for data scientists to explore, understand and transform massive datasets and to build and train advanced machine learning models. The question then becomes, how do I deploy these model to a production environment. How do I embed what I have learned into customer facing data applications. Like all things in engineering, it depends. In this meetup, we will discuss best practices from Databricks on how our customers productionize machine learning models and do a deep dive with actual customer case studies and live demos of a few example architectures and code in Python and Scala. We will also briefly touch on what is coming in Apache Spark 2.X with model serialization and scoring options.

Apache ® Spark™ MLlib 2.x: How to Productionize your Machine Learning Models

Anyscale

Apache Spark has rapidly become a key tool for data scientists to explore, understand and transform massive datasets and to build and train advanced machine learning models. The question then becomes, how do I deploy these model to a production environment? How do I embed what I have learned into customer facing data applications? In this webinar, we will discuss best practices from Databricks on how our customers productionize machine learning models do a deep dive with actual customer case studies, show live tutorials of a few example architectures and code in Python, Scala, Java and SQL.

Making Netflix Machine Learning Algorithms Reliable

Justin Basilico

Consolidating MLOps at One of Europe’s Biggest Airports

Databricks

At Schiphol airport we run a lot of mission critical machine learning models in production, ranging from models that predict passenger flow to computer vision models that analyze what is happening around the aircraft. Especially now in times of Covid it is paramount for us to be able to quickly iterate on these models by implementing new features, retraining them to match the new dynamics and above all to monitor them actively to see if they still fit the current state of affairs. To achieve those needs we rely on MLFlow but have also integrated that with many of our other systems. So have we written Airflow operators for MLFlow to ease the retraining of our models, have we integrated MLFlow deeply with our CI pipelines and have we integrated it with our model monitoring tooling. In this talk we will take you through the way we rely on MLFlow and how that enables us to release (sometimes) multiple versions of a model per week in a controlled fashion. With this set-up we are achieving the same benefits and speed as you have with a traditional software CI pipeline.

Enabling Scalable Data Science Pipeline with Mlflow at Thermo Fisher Scientific

Databricks

Mykola Mykytenko: MLOps: your way from nonsense to valuable effect (approache...

Lviv Startup Club

How To Build Auto-Adaptive Machine Learning Models with Kubernetes

cnvrg.io AI OS - Hands-on ML Workshops

This webinar will instruct data scientists and machine learning engineers how to build manage and deploy auto-adaptive machine learning models in production. Data is ever changing, leaving your models outdated and built on old data. This can lead to underperforming models and a lot of manual work to fix it. By allowing your models to continually learn you’ll ensure that they run at peak performance. Using state of the art Kubernetes infrastructure, we’ll show you how to automatically track and manage your auto-adaptive machine learning models while in production. By building auto-adaptive machine learning models, data engineers can bridge the gap between research and production. After this webinar you’ll be able to build and deploy machine learning pipelines that automatically adapt and retrain based on any validation trigger you choose. Key webinar takeaways: How to build auto-adaptive machine learning pipelines How to use Kubernetes to manage and scale models in production How to automatically monitor for peak performance How to set up continuous deployment of ML pipeline Watch all our webinars at https://cnvrg.io/webinars-and-workshops/

Productionizing Machine Learning with a Microservices Architecture

Databricks

[DSC Europe 23] Petar Zecevic - ML in Production on Databricks

DataScienceConferenc1

Deploying ML models in production, with or without CI/CD, is significantly more complicated than deploying traditional applications. That is mainly because ML models do not just consist of the code used for their training, but they also depend on the data they are trained on and on the supporting code. Monitoring ML models also adds additional complexity beyond what is usually done for traditional applications. This talk will cover these problems and best practices for solving them, with special focus on how it's done on the Databricks platform.

MLOPS By Amazon offered and free download

pouyan533

Machine Learning In Production

Samir Bessalah

Making Data Science Scalable - 5 Lessons Learned

Laurenz Wuttke

Making Data Science Scalable - 5 Lessons Learned Making Data Science and Machine Learning scalable is not easy: #1 Data Science in silos is bad #2 ML-Feature stores should be at the heart of every ML-Platform #3 Auto ML works great if you have a Feature store #4 Treat Data Science Projekts more like Software Development #5 Cloude based Infrastructure makes it easy to get started Data Science MeetUp Cologne, Germany 16. May 2019 datasolut GmbH - https://datasolut.com

Similar to “Houston, we have a model...” Introduction to MLOps (20)

Slides-Артем Коваль-Cloud-Native MLOps Framework - DataFest 2021.pdf

Trenowanie i wdrażanie modeli uczenia maszynowego z wykorzystaniem Google Clo...

Building a MLOps Platform Around MLflow to Enable Model Productionalization i...

artificggggggggggggggialintelligence.pdf

Mohamed Sabri: Operationalize machine learning with Kubeflow

Deployment Design Patterns - Deploying Machine Learning and Deep Learning Mod...

AllThingsOpen 2018 - Deployment Design Patterns (Dan Zaratsian)

Apache Spark Model Deployment

Apache ® Spark™ MLlib 2.x: How to Productionize your Machine Learning Models

Making Netflix Machine Learning Algorithms Reliable

Consolidating MLOps at One of Europe’s Biggest Airports

Enabling Scalable Data Science Pipeline with Mlflow at Thermo Fisher Scientific

Mykola Mykytenko: MLOps: your way from nonsense to valuable effect (approache...

How To Build Auto-Adaptive Machine Learning Models with Kubernetes

Productionizing Machine Learning with a Microservices Architecture

[DSC Europe 23] Petar Zecevic - ML in Production on Databricks

MLOPS By Amazon offered and free download

Machine Learning In Production

Making Data Science Scalable - 5 Lessons Learned

More from Rui Quintino

Power BI for Data Science and Machine Learning - Data Science Portugal meetup

Rui Quintino

Session recording at https://youtu.be/gm5nohV30fE Abstract: Power BI is a widely successful tool for Business Intelligence projects, but there’s lots of value for Data Scientists too. This session will explore how to leverage Power BI for Data Science & Machine Learning workloads. From powerful & effortless EDA, built-in anomaly detection to R & Python integration, and even AutoML capabilities, we’ll see how these and other features can contribute to better AI project productivity and outcomes. Can Power BI be a superpower for Data Scientists? We’ll find out!

Empowering you - Power BI, Power Platform & AI Builder

Rui Quintino

Jupyter Notebooks: Introduction, Tips & Tools

Rui Quintino

Kaggle Days Porto 2019 - 1st place presentation by team DevScope

Rui Quintino

DataSciencePT #27 - Fifty Shades of Automated Machine Learning

Rui Quintino

Is "the sexiest job of 21st century", the Data Scientist, about to be automated? How & when can AutoML tools help on a typical machine learning lifecycle? What AutoML challenges are still open & what ML work will remain in the foreseeable future? Most importantly… will robots get all the fun & sex appeal? :) Some questions we'll try to tackle on this session. *-Robots are not allowed in this session

Docker & Containers for Big Data, Data Science, Machine Learning & Deep Learning

Rui Quintino

PortoData Meetup http://www.portodata.net/xxxi-evento-porto-data-25-maio-2017-uptec/ Docker e Containers são das tecnologias atuais com maior crescimento e aceitação. Depois de um breve refresh ao Docker e vantagens dos containers, vamos ver casos práticos de como o Docker pode ajudar em workloads de desenvolvimento, testes ou mesmo produção para Big Data, Data Science, Machine Learning ou mesmo Deep Learning. Seja no posto de trabalho, on-prem ou Azure. Por último a facilidade de criação de clusters com Docker Swarm.

Microsoft Cognitive Services & Bot Framework - Universidade Fernando Pessoa

Rui Quintino

Open Source Deep Learning & Machine Learning with Microsoft CNTK & LightGBM

Rui Quintino

Session at Microsoft Open Source Camp 2017 https://msoscamp.io/ Introducing Microsoft Cognitive Toolkit, a deep-learning framework for neural networks to model, train & use popular neural networks architectures for large range of machine learning scenarios.We’ll see how to start with custom image classification & computer vision scenarios using CNTK, running on CPU and GPUs, together with LightGBM (Microsoft open source gradient boosting framework).

Data Science Portugal Meetup 7 - Machine Learning & Data Science Safety Remi...

Rui Quintino

Fasten your seat-belt first & other safety reminders for Data Science & Machine Learning The age of Big Data, Internet of Things, Deep Learning, increasingly effective Cognitive & Artificial Intelligence, we seem to have technology & computational resources for unlimited data mining. Still, extracting valuable insights from data remains a real challenge. We'll tackle some of the typical issues & obstacles we face when trying to extract meaning & insights from observational data. The never ending data quality challenges, why correlation is not causation is just the start… Why it's so hard to extract real causes & factors from data, and... what is observational data anyway? Things you'll be glad to know before presenting your first data project insights.

Microsoft Data Platform Airlift 2017 Rui Quintino Machine Learning with SQL S...

Rui Quintino

Sql Saturday Lisbon 2017 Rui Quintino -R first steps for sql devs & dbas

Rui Quintino

First steps with R & RStudio for SQL Developers & DBAs Are you a BI Developer? DBA? BI Analyst or a data/analytics lover? Come along, join us in this session as we explore this powerful, sometimes even strange!... language , showing main differences from the typical SQL/BI development. This is a beginner level session to guide you on why & when to use R, and help with the very first steps & typical challenges. http://www.sqlsaturday.com/583/Sessions/Details.aspx?sid=58218

The Power of Now! Azure Stream Analytics - Microsoft ITPro AirLift

Rui Quintino

SQL Saturday #188 Portugal - "Faster than the speed of light"... with Microso...

Rui Quintino

"Faster than the speed of light"... with #MSBI What-if insights from your business data were just a few seconds/clicks away? What-if we could instantly explore our data with rich & fully interactive dashboards using the amazing new features of Excel 2013, PowerPivot and PowerView? Without needing to build auxiliary reference tables, complex dax or powerpivot modelling? What-if instead self-service we could be "instantly-served"?

"De histórias mal contadas..."

Rui Quintino

More from Rui Quintino (14)