AN INTRODUCTION TO AUTO-ML EDGE-ML (VIDEO 1/4)
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This document introduces Auto ML and compares traditional machine learning approaches to Edge ML. Traditional ML has limitations including not scaling well with large data as the number of parameters increases. It also does not provide a way to choose the best family of models without extensive grid search. Regular Auto ML approaches try to address these problems but still require large hardware resources. Edge ML takes a Bayesian approach called MODL that avoids grid search and scales to tiny hardware resources while producing accurate and robust models.
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In the last decade, we have witnessed an exponential growth in both the complexity and the number of Machine Learning (ML) techniques. As a consequence, leveraging such methods to solve real-case problems has become difficult for a Data Scientist (DS). Automated Machine Learning (AutoML) tools were devised to alleviate that task, but easily became as complex as the ML techniques themselves. The DS has started to rely on this kind of tools without understanding their functioning, thus loosing the control over the process.
In this vision paper, we propose HAMLET (Human-centric AutoMl via Logic and Argumentation), a framework that would help the DS to redeem her centrality. HAMLET is inspired to the well-known standard process model CRISP-DM. Iteration after iteration, the knowledge is augmented by acquiring more constraints about the problem until a suitable solution is found. HAMLET leverages Logic and Argumentation to merge both constraints and solutions in an uniformed human-and machine-readable medium. Not only it allows an easy exploration of the new knowledge at each iteration, but it also enforces a continuous revision via the AutoML tool and the confrontation between the DS and Domain Experts.
Machine Learning and AI: Core Methods and Applications
This session was presented at the CFA Institute on May 6th 2020
This deep-dive session discusses core methods and applications to provide an understanding of supervised and unsupervised machine learning. Participants will be introduced to advanced topics that include time series analysis, reinforcement learning, anomaly detection, and natural language processing. Case studies will also examine how to predict interest rates and credit risk with alternative data sets and how to analyze earning calls from EDGAR using Natural Language Processing Techniques.
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Machine learning (ML) is a branch of artificial intelligence that uses computing systems to analyze data, recognize patterns, and learn from that data. ML systems can learn and improve from experience over time as they are exposed to more data. There are two main types of ML: supervised learning, where the model learns from labeled examples with known outputs, and unsupervised learning, where the model finds hidden patterns in unlabeled data. The ML process involves defining objectives, preparing data, building models, evaluating models, and deploying the best performing model. Data preparation is an important step that can impact model performance, such as transforming, normalizing, and engineering features from the data.
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2) The MLOps pipeline includes stages for data engineering, modelling, deployment, and monitoring. Key aspects are ensuring reproducibility, managing data processing pipelines, and defining deployment and monitoring strategies.
3) Successful MLOps requires automating model deployment, monitoring model and data metrics over time, and retraining models when performance degrades to keep models performing well as data evolves in production.
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Netflix uses a variety of techniques to provide personalized recommendations to users. Some key aspects include:
1. Netflix recommendations are generated using both offline and online techniques. Offline techniques allow for more complex computations but results may become stale, while online techniques can respond quickly but have stricter time constraints.
2. Recommendations are generated using a variety of data sources and machine learning models, including SVD, RBMs, gradient boosted trees, and other techniques. Both the data and models are important for generating high quality recommendations.
3. Netflix tests recommendations using both offline and online A/B testing techniques. Offline testing is used to evaluate new models and ideas before launching online tests involving real users
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This document provides an overview of real world machine learning using Azure. It discusses the machine learning workflow including data understanding, preprocessing, feature engineering, model selection, evaluation and tuning. It then describes various Azure machine learning tools for building, testing and deploying machine learning models including Azure ML Workbench, Studio, Experimentation Service and Model Management Service. It concludes with an upcoming demo of predictive maintenance using Azure ML Studio.
Intro to Machine Learning by Microsoft Ventures
Machine learning lets you make better business decisions by uncovering patterns in your consumer behavior data that is hard for the human eye to spot. You can also use it to automate routine, expensive human tasks that were previously not doable by computers. In the business to business space (B2B), if your competitors can make wiser business decisions based on data and automate more business operations but you still base your decisions on guesswork and lack automation, you will lose out on business productivity. In this introduction to machine learning tech talk, you will learn how to use machine learning even if you do not have deep technical expertise on this technology.
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1.What is machine learning
2.What is a typical ML application architecture
3.How to start ML development with free resource links
4.Key decision factors in ML technology selection depending on use case scenarios
The Data Science Process - Do we need it and how to apply?
Machine learning is not black magic but a discipline that involves statistics, data science, analysis and hard work. From searching patterns and data preparation through applying and optimizing algorithms to obtaining usable predictions, one would need background and appropriate tools.
But do we need it, when there is already available AI as a service solution out there? Do we need to try hard with artificial neural networks? And if we decide to do so, what tools would be a safe bet?
In this session we will go through real world examples, mention key tools from Microsoft and open source world to do data science and machine learning and most importantly - we will provide a workflow and some best practices.
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Every single security company is talking in some way or another about how they are applying machine learning. Companies go out of their way to make sure they mention machine learning and not statistics when they explain how they work. Recently, that's not enough anymore either. As a security company you have to claim artificial intelligence to be even part of the conversation.
Guess what. It's all baloney. We have entered a state in cyber security that is, in fact, dangerous. We are blindly relying on algorithms to do the right thing. We are letting deep learning algorithms detect anomalies in our data without having a clue what that algorithm just did. In academia, they call this the lack of explainability and verifiability. But rather than building systems with actual security knowledge, companies are using algorithms that nobody understands and in turn discover wrong insights.
In this talk I will show the limitations of machine learning, outline the issues of explainability, and show where deep learning should never be applied. I will show examples of how the blind application of algorithms (including deep learning) actually leads to wrong results. Algorithms are dangerous. We need to revert back to experts and invest in systems that learn from, and absorb the knowledge, of experts.
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AN INTRODUCTION TO AUTO-ML EDGE-ML (VIDEO 1/4)
- 1. Video ¼ – Introduction to Auto ML Alexis Bondu www.edge-ml.fr MODL : A Bayesian approach for model selection
- 2. Machine Learning A semi-manual process… Data ScientistData Hardware resources Data-preprocessing Model tuning
- 3. Data Hardware resources Robot • Non traditional methods/algorithm • Non structured data (ex: graph, images, texts … ) Tricky A fully automated process… Auto ML • Classical models (ex: classification, regression) • Structured data Easy
- 4. Outline A – 2 limits of « Machine Learning » B – Regular Auto ML approaches C – Edge ML : a disruptive Auto ML approach
- 5. Learning algorithms f(X) B O 12 0.5 …… Expected value : A A – Limits of Machine Learning
- 6. Grid-search 1 - Exploration of a grid of parameters (grid-search) Parameter 1 Parameter2 Couple of values 2 - Evaluation of parameters by using a cross-validation (for each point of the grid) Data • Learning by using K-1 folds • Spliting data into K disjointed folds • Evaluating the model on the remaining fold • The average evaluation is retained • Learning algorithm repeated K times A – Limits of Machine Learning
- 7. Grid-search To optimize 2 parameters, the learning algorithm is repeated 250 times! 3 1 250 x 4 6 250 x 5 31 250 x A concrete example: • 5 values are tested for each parameter • The data is divided into 10 folds • The entire grid is explored Problem # 1 : How to scale with big data when the number of parameters increases? A – Limits of Machine Learning
- 8. No Free Lunch Theorem In absolute terms, no approach is better than all others. In practice, this depends on each data set ...… NO Problem # 2 : How to chose the more suitable family of models? A – Limits of Machine Learning
- 9. The dark side of the brute force B – Regular Auto ML approaches Problem # 2 : Choice of the family of models models Problem # 1 : Scaling Skilled DS Best practice Transcription into algorithms Numerous ML algorithms Very accurate ModelAutomated best practice No theoretical warranties Use of huge hardware recourses
- 10. MODL : a specific and formalized approach C – Edge ML: a disruptive Auto ML approach Problem # 2 : Choice of the family of models models Problem # 1 : Scaling No grid-search ! MODL approach Accurate and very robust models Use of tiny hardware recourses
- 11. High-performance models, very robust and interpretable. • Robustness: The learned classifiers are very robust (there is no equivalent). There is very little difference in performance on deployment data. The production of the models is simplified and secured ... • Interpretability: Simple to implement, and easy to understand. • Performance: The learned classifiers are relatively accurate - generally comparable to a Random Forest. C – Edge ML: a disruptive Auto ML approach
- 12. The automated pipe of Machine Learning : MODL is everywhere! • Discretization (numerical) • Grouping levels (categorical) • Selective Naive Bayes (SNB) • Models averaging Univariate Multivariate • Bonus : Sequence-mining Main question : How MODL avoid over-fitting ? Contents of the next videos
Editor's Notes
- Valable pour tous les modèles de ML Exemple de la random forest … Appris : chaque arbre : nœud = selection d’une var + point de coupure Meta : nb arbre / profondeur / effectif min / choix du critère de pureté / échantillonnage ligne et colonne
- Param : profondeur / effectif min C’est une science expérimentale ….
- …. C’est pourtant l’usage dans la communauté ….
- Principe communément admis dans la communauté … Ex : On ne peut que les RN sont plus performant que les RF … Chaque algo optimise le modèle au sein d’une classe de modèle … comment choisir ?
- #1 : Choix du modèle : RN = image, données de type signal, XGB = données tabulaire #2 : bonne pratique (tester sur un echantillon d’abord)
- Simplicité de miste