Machine Learning for Energy Trading, Automotive Sector, and Logistics, presented by BigML's Partners A1 Digital.
Main Conference: Introduction to Machine Learning.
DutchMLSchool: 1st edition of the Machine Learning Summer School in The Netherlands.
Enhancing and Automating Decision Making with Machine Learning - Main Conference: Introduction to Machine Learning.
DutchMLSchool: 1st edition of the Machine Learning Summer School in The Netherlands.
Machine learning is becoming widely used to automate decision making. While machine learning seems complex, it involves finding patterns in data that can be used to make useful predictions. The document discusses how factors like increased data availability, faster computation, and easier tools have led to the rise of machine learning applications. It also notes common pitfalls in early machine learning adoption like overhyping results and failing to develop a clear strategy. Overall machine learning is transforming industries by enabling cheaper and more data-driven decisions at scale.
Anatomy of an Application: Machine Learning End-to-End - Main Conference: Introduction to Machine Learning.
DutchMLSchool: 1st edition of the Machine Learning Summer School in The Netherlands.
Machine Learning: Business Perspective - Main Conference: Introduction to Machine Learning.
DutchMLSchool: 1st edition of the Machine Learning Summer School in The Netherlands.
DutchMLSchool. Introduction to Machine Learning with the BigML PlatformBigML, Inc
Introduction to Machine Learning with the BigML Platform - ML for Executives Course.
DutchMLSchool: 1st edition of the Machine Learning Summer School in The Netherlands.
DutchMLSchool. Logistic Regression, Deepnets, Time SeriesBigML, Inc
DutchMLSchool. Logistic Regression, Deepnets, and Time Series (Supervised Learning II) - Main Conference: Introduction to Machine Learning.
DutchMLSchool: 1st edition of the Machine Learning Summer School in The Netherlands.
Machine Learning for Logistics: Predicting Expedition Outcome - Main Conference: Introduction to Machine Learning.
DutchMLSchool: 1st edition of the Machine Learning Summer School in The Netherlands.
DutchMLSchool. Supervised vs Unsupervised LearningBigML, Inc
Supervised versus Unsupervised Learning Techniques - Main Conference: Introduction to Machine Learning.
DutchMLSchool: 1st edition of the Machine Learning Summer School in The Netherlands.
Enhancing and Automating Decision Making with Machine Learning - Main Conference: Introduction to Machine Learning.
DutchMLSchool: 1st edition of the Machine Learning Summer School in The Netherlands.
Machine learning is becoming widely used to automate decision making. While machine learning seems complex, it involves finding patterns in data that can be used to make useful predictions. The document discusses how factors like increased data availability, faster computation, and easier tools have led to the rise of machine learning applications. It also notes common pitfalls in early machine learning adoption like overhyping results and failing to develop a clear strategy. Overall machine learning is transforming industries by enabling cheaper and more data-driven decisions at scale.
Anatomy of an Application: Machine Learning End-to-End - Main Conference: Introduction to Machine Learning.
DutchMLSchool: 1st edition of the Machine Learning Summer School in The Netherlands.
Machine Learning: Business Perspective - Main Conference: Introduction to Machine Learning.
DutchMLSchool: 1st edition of the Machine Learning Summer School in The Netherlands.
DutchMLSchool. Introduction to Machine Learning with the BigML PlatformBigML, Inc
Introduction to Machine Learning with the BigML Platform - ML for Executives Course.
DutchMLSchool: 1st edition of the Machine Learning Summer School in The Netherlands.
DutchMLSchool. Logistic Regression, Deepnets, Time SeriesBigML, Inc
DutchMLSchool. Logistic Regression, Deepnets, and Time Series (Supervised Learning II) - Main Conference: Introduction to Machine Learning.
DutchMLSchool: 1st edition of the Machine Learning Summer School in The Netherlands.
Machine Learning for Logistics: Predicting Expedition Outcome - Main Conference: Introduction to Machine Learning.
DutchMLSchool: 1st edition of the Machine Learning Summer School in The Netherlands.
DutchMLSchool. Supervised vs Unsupervised LearningBigML, Inc
Supervised versus Unsupervised Learning Techniques - Main Conference: Introduction to Machine Learning.
DutchMLSchool: 1st edition of the Machine Learning Summer School in The Netherlands.
DutchMLSchool. ML: A Technical PerspectiveBigML, Inc
DutchMLSchool. Machine Learning: A Technical Perspective
TITLE AS IN SCHEDULE - Main Conference: Introduction to Machine Learning.
DutchMLSchool: 1st edition of the Machine Learning Summer School in The Netherlands.
Elena Grewal, Data Science Manager, Airbnb at MLconf SF 2016MLconf
Before the Model: How Machine Learning Products Start, with Examples from Airbnb: Often the most important part of building a machine learning product is the formulation of the problem; the most elegant model is rendered useless without the right application and model architecture. Airbnb is an online marketplace for accommodations which has found many interesting applications for machine learning products by taking a data driven approach to investment in Machine learning products. Come hear about how the Airbnb team generates and vets ideas for machine learning products and tailors the product to business problems, with some examples of success and lessons learned along the way.
Yuri is a Member of Technical Staff / Data Scientist at eBay in New York City. He is currently focused on developing scalable machine learning algorithms to produce high quality item recommendations. Yuri holds a Ph.D. degree from the Applied Physics and Applied Mathematics department from Columbia University and an undergraduate degree in Physics from UC Berkeley.
Abstract Summary:
Innovations in Recommender Systems for a Semi-structured Marketplace:
eBay has over 1 billion live items on the site at any given time. The lack of structured information about listings as well as variable inventory makes traditional collaborative filtering algorithms difficult to use in eBay’s large semi-structured marketplace. We will discuss approaches to overcome these challenges using machine learning and deep learning (both text and image based models). The details of the sampling strategy, feature engineering, and machine learned ranking model are all important for delivering improved operational metrics in A/B tests. We will cover both system architecture engineering as well as data science and machine learning methods that were developed to generate high quality recommendations.
Feature engineering is the process of using domain knowledge to create new features that allow machine learning algorithms to work better or work at all. It involves applying transformations to existing features, like splitting date-time fields or normalizing numeric values, as well as computing new features from existing ones. Flatline is a domain-specific language for programmatic feature engineering and filtering that allows creating new features using expressions over existing fields. Care must be taken to avoid leakage when creating new features.
Machine Learning automation. Advanced WhizzML workflows: feature selection, boosting, gradient descent, and stacking.
VSSML18: 4th edition of the Valencian Summer School in Machine Learning.
Building Custom Machine Learning Algorithms with Apache SystemMLsparktc
This document discusses Apache SystemML, which is a machine learning framework for building custom machine learning algorithms on Apache Spark. It originated from research projects at IBM involving machine learning on Hadoop. SystemML aims to allow data scientists to build ML solutions using languages like R and Python, while executing algorithms on big data platforms like Spark. It provides a high-level language for expressing algorithms and performs automatic parallelization and optimization. The document demonstrates SystemML through a matrix factorization example for a targeted advertising problem. It shows how to use SystemML, Spark and Zeppelin together to build a custom algorithm and optimize part of the machine learning pipeline.
Companies that understand how to apply AI will scale and win their respective markets over the next decade. That said, delivering on this promise and managing machine learning projects is much harder than most people anticpate. Many organizations hire teams of PhDs and data scientists, then fail to ship products that move business metrics. The root cause is often a lack of product strategy for AI, or the failure to adapt their product development processes to the needs of machine learning systems. This talk will cover some of the common ways machine learning fails in practice, the tactical responsibilities of AI product managers, and how to approach product strategy for AI.
Peter Skomoroch, former Head of Data Products at Workday and LinkedIn, will describe how you can navigate these challenges to ship metric moving AI products that matter to your business.
Peter will provide practical advice on:
* The role of an AI Product Manager
* How to evaluate and prioritize your AI projects
* The ways AI product management differs from traditional product management
* Bridging the worlds of design and machine learning
* Making trade offs between data quality and other business metrics
This document discusses data transformations for machine learning. It begins by noting that perfectly formatted data is ideal but rarely exists in reality. Common obstacles to machine learning-ready data are discussed, including data structure, missing values, and unwanted features. The process of transforming data involves understanding the goal, identifying relevant machine learning tasks, accessing and structuring the data, and performing feature engineering. Common transformations include data cleaning, labeling, denormalizing, aggregating, pivoting, and handling time windows. An example of transforming loan data from Prosper is provided to demonstrate handling streaming XML data updates.
This document discusses deepnets, which are a type of supervised learning algorithm for classification and regression. Deepnets build upon logistic regression by adding hidden layers between the input and output layers. This allows deepnets to model more complex nonlinear relationships than logistic regression. While deepnets have powerful representational abilities, their success depends on finding the optimal network structure for a given problem. The document outlines how BigML uses metalearning and network search techniques to automate this process and make deepnets more accessible for users. Deepnets work best for problems where computational resources allow exploring many network structures to find the best performing one.
This document summarizes a presentation on feature engineering for machine learning. It discusses how feature engineering is important for allowing machine learning algorithms to work better or at all by creating new features that provide better representations of the data. Various techniques for feature engineering are presented, including transforming date/time fields, handling categorical variables, text analysis, and discretizing continuous variables. The use of feature engineering tools like Flatline for programmatically creating new features is also demonstrated. Feature selection techniques are briefly discussed to help identify the most important and non-leaky features.
Practical Explainable AI: How to build trustworthy, transparent and unbiased ...Raheel Ahmad
This presentation is from the Federated & Distributed Machine Learning Conference. This talk focuses on why we need explainable AI and how can we build models that are trustworthy, transparency and unbiased.
A lack of trust is inhibiting the adoption of #AI. This presentation discusses approaches to delivering trusted data pipelines for AI and machine learning
Explainable AI (XAI) is becoming Must-Have NFR for most AI enabled product or solution deployments. Keen to know viewpoints and collaboration opportunities.
How to Use Artificial Intelligence by Microsoft Product ManagerProduct School
The talk focused on the Fundamentals of Product Management, leveraging the speaker's personal experiences in the AI field. It covered core Product Manager topics such as managing customer needs, business goals & technology feasibility, the holy trinity of the Product Manager discipline, delve into data analyses, rapid experimentation, and execution, and finally, explored the challenges of customer privacy, bias, and inclusivity in AI products.
Artificial Intelligence Impact - What AI is (and isn't) Helping Startups Scal...Daniel Faggella
(This presentation was created as a short talk for a French Tech Hub event in San Francisco)
Contents:
- What investors see as "drivers of value" in terms of the use of AI in specific industries and business applications
- Examples of AI in industry (exploring business models and use cases)
- Where AI fits into (or doesn't) the business model of your startup, and how to determine whether or not AI has any short-term value in a specific business model
Feature engineering is the process of using domain knowledge to create new features that allow machine learning algorithms to work better or work at all. It involves applying transformations and encoding schemes to raw data to construct informative features for modeling. Feature engineering is important because ML algorithms only learn from the data and features provided, so carefully engineered features are crucial. Effective feature engineering requires domain expertise, experimentation, and evaluation to identify representations of the data that best support predictive tasks.
Intro to machine learning for web folks @ BlendWebMixLouis Dorard
This document provides an introduction and overview of machine learning. It discusses use cases for machine learning like real estate pricing and spam filtering. It covers the two phases of machine learning as training a model and then predicting with the model. It also discusses limitations of machine learning like needing enough high quality training data. The document recommends using an ML canvas to plan machine learning projects by defining the problem, data, metrics, and model development process. It provides an example case study of using machine learning for churn prediction and analysis.
MLOps – Applying DevOps to Competitive AdvantageDATAVERSITY
MLOps is a practice for collaboration between Data Science and operations to manage the production machine learning (ML) lifecycles. As an amalgamation of “machine learning” and “operations,” MLOps applies DevOps principles to ML delivery, enabling the delivery of ML-based innovation at scale to result in:
Faster time to market of ML-based solutions
More rapid rate of experimentation, driving innovation
Assurance of quality, trustworthiness, and ethical AI
MLOps is essential for scaling ML. Without it, enterprises risk struggling with costly overhead and stalled progress. Several vendors have emerged with offerings to support MLOps: the major offerings are Microsoft Azure ML and Google Vertex AI. We looked at these offerings from the perspective of enterprise features and time-to-value.
Cloud Machine Learning can help make sense of unstructured data, which accounts for 90% of enterprise data. It provides a fully managed machine learning service to train models using TensorFlow and automatically maximize predictive accuracy with hyperparameter tuning. Key benefits include scalable training and prediction infrastructure, integrated tools like Cloud Datalab for exploring data and developing models, and pay-as-you-go pricing.
DutchMLSchool. ML: A Technical PerspectiveBigML, Inc
DutchMLSchool. Machine Learning: A Technical Perspective
TITLE AS IN SCHEDULE - Main Conference: Introduction to Machine Learning.
DutchMLSchool: 1st edition of the Machine Learning Summer School in The Netherlands.
Elena Grewal, Data Science Manager, Airbnb at MLconf SF 2016MLconf
Before the Model: How Machine Learning Products Start, with Examples from Airbnb: Often the most important part of building a machine learning product is the formulation of the problem; the most elegant model is rendered useless without the right application and model architecture. Airbnb is an online marketplace for accommodations which has found many interesting applications for machine learning products by taking a data driven approach to investment in Machine learning products. Come hear about how the Airbnb team generates and vets ideas for machine learning products and tailors the product to business problems, with some examples of success and lessons learned along the way.
Yuri is a Member of Technical Staff / Data Scientist at eBay in New York City. He is currently focused on developing scalable machine learning algorithms to produce high quality item recommendations. Yuri holds a Ph.D. degree from the Applied Physics and Applied Mathematics department from Columbia University and an undergraduate degree in Physics from UC Berkeley.
Abstract Summary:
Innovations in Recommender Systems for a Semi-structured Marketplace:
eBay has over 1 billion live items on the site at any given time. The lack of structured information about listings as well as variable inventory makes traditional collaborative filtering algorithms difficult to use in eBay’s large semi-structured marketplace. We will discuss approaches to overcome these challenges using machine learning and deep learning (both text and image based models). The details of the sampling strategy, feature engineering, and machine learned ranking model are all important for delivering improved operational metrics in A/B tests. We will cover both system architecture engineering as well as data science and machine learning methods that were developed to generate high quality recommendations.
Feature engineering is the process of using domain knowledge to create new features that allow machine learning algorithms to work better or work at all. It involves applying transformations to existing features, like splitting date-time fields or normalizing numeric values, as well as computing new features from existing ones. Flatline is a domain-specific language for programmatic feature engineering and filtering that allows creating new features using expressions over existing fields. Care must be taken to avoid leakage when creating new features.
Machine Learning automation. Advanced WhizzML workflows: feature selection, boosting, gradient descent, and stacking.
VSSML18: 4th edition of the Valencian Summer School in Machine Learning.
Building Custom Machine Learning Algorithms with Apache SystemMLsparktc
This document discusses Apache SystemML, which is a machine learning framework for building custom machine learning algorithms on Apache Spark. It originated from research projects at IBM involving machine learning on Hadoop. SystemML aims to allow data scientists to build ML solutions using languages like R and Python, while executing algorithms on big data platforms like Spark. It provides a high-level language for expressing algorithms and performs automatic parallelization and optimization. The document demonstrates SystemML through a matrix factorization example for a targeted advertising problem. It shows how to use SystemML, Spark and Zeppelin together to build a custom algorithm and optimize part of the machine learning pipeline.
Companies that understand how to apply AI will scale and win their respective markets over the next decade. That said, delivering on this promise and managing machine learning projects is much harder than most people anticpate. Many organizations hire teams of PhDs and data scientists, then fail to ship products that move business metrics. The root cause is often a lack of product strategy for AI, or the failure to adapt their product development processes to the needs of machine learning systems. This talk will cover some of the common ways machine learning fails in practice, the tactical responsibilities of AI product managers, and how to approach product strategy for AI.
Peter Skomoroch, former Head of Data Products at Workday and LinkedIn, will describe how you can navigate these challenges to ship metric moving AI products that matter to your business.
Peter will provide practical advice on:
* The role of an AI Product Manager
* How to evaluate and prioritize your AI projects
* The ways AI product management differs from traditional product management
* Bridging the worlds of design and machine learning
* Making trade offs between data quality and other business metrics
This document discusses data transformations for machine learning. It begins by noting that perfectly formatted data is ideal but rarely exists in reality. Common obstacles to machine learning-ready data are discussed, including data structure, missing values, and unwanted features. The process of transforming data involves understanding the goal, identifying relevant machine learning tasks, accessing and structuring the data, and performing feature engineering. Common transformations include data cleaning, labeling, denormalizing, aggregating, pivoting, and handling time windows. An example of transforming loan data from Prosper is provided to demonstrate handling streaming XML data updates.
This document discusses deepnets, which are a type of supervised learning algorithm for classification and regression. Deepnets build upon logistic regression by adding hidden layers between the input and output layers. This allows deepnets to model more complex nonlinear relationships than logistic regression. While deepnets have powerful representational abilities, their success depends on finding the optimal network structure for a given problem. The document outlines how BigML uses metalearning and network search techniques to automate this process and make deepnets more accessible for users. Deepnets work best for problems where computational resources allow exploring many network structures to find the best performing one.
This document summarizes a presentation on feature engineering for machine learning. It discusses how feature engineering is important for allowing machine learning algorithms to work better or at all by creating new features that provide better representations of the data. Various techniques for feature engineering are presented, including transforming date/time fields, handling categorical variables, text analysis, and discretizing continuous variables. The use of feature engineering tools like Flatline for programmatically creating new features is also demonstrated. Feature selection techniques are briefly discussed to help identify the most important and non-leaky features.
Practical Explainable AI: How to build trustworthy, transparent and unbiased ...Raheel Ahmad
This presentation is from the Federated & Distributed Machine Learning Conference. This talk focuses on why we need explainable AI and how can we build models that are trustworthy, transparency and unbiased.
A lack of trust is inhibiting the adoption of #AI. This presentation discusses approaches to delivering trusted data pipelines for AI and machine learning
Explainable AI (XAI) is becoming Must-Have NFR for most AI enabled product or solution deployments. Keen to know viewpoints and collaboration opportunities.
How to Use Artificial Intelligence by Microsoft Product ManagerProduct School
The talk focused on the Fundamentals of Product Management, leveraging the speaker's personal experiences in the AI field. It covered core Product Manager topics such as managing customer needs, business goals & technology feasibility, the holy trinity of the Product Manager discipline, delve into data analyses, rapid experimentation, and execution, and finally, explored the challenges of customer privacy, bias, and inclusivity in AI products.
Artificial Intelligence Impact - What AI is (and isn't) Helping Startups Scal...Daniel Faggella
(This presentation was created as a short talk for a French Tech Hub event in San Francisco)
Contents:
- What investors see as "drivers of value" in terms of the use of AI in specific industries and business applications
- Examples of AI in industry (exploring business models and use cases)
- Where AI fits into (or doesn't) the business model of your startup, and how to determine whether or not AI has any short-term value in a specific business model
Feature engineering is the process of using domain knowledge to create new features that allow machine learning algorithms to work better or work at all. It involves applying transformations and encoding schemes to raw data to construct informative features for modeling. Feature engineering is important because ML algorithms only learn from the data and features provided, so carefully engineered features are crucial. Effective feature engineering requires domain expertise, experimentation, and evaluation to identify representations of the data that best support predictive tasks.
Intro to machine learning for web folks @ BlendWebMixLouis Dorard
This document provides an introduction and overview of machine learning. It discusses use cases for machine learning like real estate pricing and spam filtering. It covers the two phases of machine learning as training a model and then predicting with the model. It also discusses limitations of machine learning like needing enough high quality training data. The document recommends using an ML canvas to plan machine learning projects by defining the problem, data, metrics, and model development process. It provides an example case study of using machine learning for churn prediction and analysis.
MLOps – Applying DevOps to Competitive AdvantageDATAVERSITY
MLOps is a practice for collaboration between Data Science and operations to manage the production machine learning (ML) lifecycles. As an amalgamation of “machine learning” and “operations,” MLOps applies DevOps principles to ML delivery, enabling the delivery of ML-based innovation at scale to result in:
Faster time to market of ML-based solutions
More rapid rate of experimentation, driving innovation
Assurance of quality, trustworthiness, and ethical AI
MLOps is essential for scaling ML. Without it, enterprises risk struggling with costly overhead and stalled progress. Several vendors have emerged with offerings to support MLOps: the major offerings are Microsoft Azure ML and Google Vertex AI. We looked at these offerings from the perspective of enterprise features and time-to-value.
Cloud Machine Learning can help make sense of unstructured data, which accounts for 90% of enterprise data. It provides a fully managed machine learning service to train models using TensorFlow and automatically maximize predictive accuracy with hyperparameter tuning. Key benefits include scalable training and prediction infrastructure, integrated tools like Cloud Datalab for exploring data and developing models, and pay-as-you-go pricing.
Intelligent data summit: Self-Service Big Data and AI/ML: Reality or Myth?SnapLogic
Companies collect more data but struggle with how to glean the best insights. Use of Machine Learning also needs power data integration.
In this presentation, Janet Jaiswal, SnapLogic's VP of product marketing, reviews key strategies and technologies to deliver intelligent data via self-service ML models.
To learn more, visit https://www.snaplogic.com
Experimentation to Industrialization: Implementing MLOpsDatabricks
In this presentation, drawing upon Thorogood’s experience with a customer’s global Data & Analytics division as their MLOps delivery partner, we share important learnings and takeaways from delivering productionized ML solutions and shaping MLOps best practices and organizational standards needed to be successful.
We open by providing high-level context & answering key questions such as “What is MLOps exactly?” & “What are the benefits of establishing MLOps Standards?”
The subsequent presentation focuses on our learnings & best practices. We start by discussing common challenges when refactoring experimentation use-cases & how to best get ahead of these issues in a global organization. We then outline an Engagement Model for MLOps addressing: People, Processes, and Tools. ‘Processes’ highlights how to manage the often siloed data science use case demand pipeline for MLOps & documentation to facilitate seamless integration with an MLOps framework. ‘People’ provides context around the appropriate team structures & roles to be involved in an MLOps initiative. ‘Tools’ addresses key requirements of tools used for MLOps, considering the match of services to use-cases.
[DSC Europe 22] Why you need to think about MLOps at the beginning of your pr...DataScienceConferenc1
Nowadays MLOps is one of the most popular topics in the DS/ML world, as it enables businesses to reduce the cost of delivering new ML products and be more confident in them. In this talk, we'll discuss why it's important not to forget about MLOps from the beginning of a project, and we'll break down the main challenges that DS teams face in the development of new products.
The last 18+ months have proven to be like no other time in modern history, and it has had a profound effect on the supply chain in the manufacturing industry. This disruption has meant many restless nights worrying about supply chains, workforce agility, capacity planning, resource allocation, and much more for manufacturers. Manufacturers have realized that better planning and preparedness are crucial to adapting to the rapid changes in demand seen in today's current climate.
In this webinar, you will learn how to address these challenges head-on as we discuss how your organization can become more agile and scale to your specific business requirements and how Cloud ERP systems can support better planning and preparedness for what's next.
________________________________________
About The Presenter
Steve Canter - Director of Global Service Delivery
Steve Canter has over 25 years of experience in the information technology industry. Steve has been responsible for delivering solutions to many medium-sized and large companies in a variety of industries as a consultant and project manager. Steve also brings a unique perspective to SmartERP, having spent over ten years as the CIO for a manufacturing and distribution company. During that period, he also helped shape product and customer service strategy at Microsoft and Oracle as a member of several customer advisory boards.
The talk was given at OReilly Strata Data Conference September 2018 in NYC
All the conferences and thought leaders have been painting a vision of the businesses of the future being powered by data, but if we’re honest with ourselves, the vast majority of our massive data science investments are being deployed to PowerPoint or maybe a business dashboard. Productionizing your machine learning (ML) portfolio is the next big step on the path to ROI from AI.
You probably started out years ago on a “big data” initiative: You collected and cleaned your data and built data warehouses, and when those filled up you upgraded to data lakes. You hired data engineers and data scientists, and around the organization, everyone brushed up their SQL querying skills and got some licenses to Tableau and PowerBI.
Then you saw what Google, Uber, Facebook, and Amazon were doing with machine learning to automate business processes and customer interactions. To not get broadsided, you hired more data scientists and machine learning engineers. They were put on your teams and started using your big data investments to train models. But what you probably found is that your tech stack and DevOps processes don’t fit ML models. Unlike most of your systems, ML models require short spikes of massive compute; they are often written in different languages than your core code; they need different hardware to perform well; one model probably has applications across many teams; and the people making the models often don’t have the engineering experience to write production code but need to iterate faster than traditional engineers. Expecting your engineering and DevOps teams to deploy ML models well is like showing up to Seaworld with a giraffe since they are already handling large mammals.
There is a path forward. Almost five years ago Algorithmia launched a marketplace for models, functions, and algorithms. Today 65,000 developers are on the platform deploying 4,500 models—the result has been a layer of tools and best practices to make deploying ML models frictionless, scalable, and low maintenance. The company refers to it as the “AI layer.”
Drawing on this experience, Diego Oppenheimer covers the strategic and technical hurdles each company must overcome and the best practices developed while deploying over 4,000 ML models for 70,000 engineers.
Topics include:
Best practices for your organization
Continuous model deployment
Varying languages (Your code base probably isn’t in Python or R, but your ML models probably are.)
Managing your portfolio of ML models
Standardize versioning
Enabling models across your organization
Analytics on how and where models are being used
Maintaining auditability
Machine learning projects may seem similar to any software engineering endeavor, the reality is machine learning projects are onerous, demand high quality work from every person involved, and are sensitive to any tiny mistake.
It seems that we cannot go five years without having some massive technology shift that becomes an essential part of our day-to-day lives. So, we will start with a proper definition of machine learning and how it is changing the way businesses analyze information. We will then continue by discussing proper ways to begin machine learning projects, including weighing the feasibility of a project, planning timelines, and the stages of the machine learning workflow once you start your project.
After exploring the stages of the machine learning workflow, we will end the webinar with an example of a completed machine learning project. We will demonstrate how to create a similar project and give you the tools to create your own.
What you'll learn:
A deeper understanding of the end-to-end machine learning workflow.
The tools needed to effectively create, design, and manage machine learning projects.
The skills to define your goal, foresee issues, release models, and measure outcomes during the ML project lifecycle.
Demo: Skyl Platform for End-End machine learning workflow.
This is the slide deck for this webinar:
https://skyl.ai/webinars/guide-end-to-end-machine-learning-projects
This presentation provides an objective approach to make your legacy and custom-built applications agile and infused with intelligence. This allows your apps to utilize new and more substantial data sets as well as apply artificial intelligence and machine learning to take in-the-moment actions.
Travis Cox, Kathy Applebaum, and Kevin McClusky from Inductive Automation will discuss key concepts and best practices, show demos, and answer questions from the audience, to help you start integrating ML into your day-to-day processes.
Learn more about:
• Practical ways to use ML in your factory or facility
• What you'll need to get started
• Existing ML tools and platforms
• And more
Mohamed Sabri: Operationalize machine learning with KubeflowLviv Startup Club
This document summarizes a hands-on workshop on Kubeflow Pipeline. The workshop will cover requirements, an introduction to the presenter Mohamed Sabri, and their approach of strategizing, shaping, and spreading knowledge. It then discusses operationalizing machine learning (MLOps) and provides an analysis, design, coaching, and implementation framework. Deliverables include an implemented MLOps environment, training sessions, design documents, and a recommendations roadmap. The rest of the document discusses MLOps architectures, challenges, example technologies and tools, a use case, and deployment workflows from notebooks to production.
Practical model management in the age of Data science and MLQuantUniversity
Sri Krishnamurthy presents on practical model risk management in the age of data science and machine learning. He discusses how machine learning and AI are driving paradigm shifts in finance. However, he cautions that claims about machine learning capabilities need to be balanced with realities about data and model quality. Key challenges include ensuring interpretability, transparency, and proper evaluation of models in production. He promotes his company's solutions for addressing these challenges through end-to-end workflow management and model governance tools.
R+Hadoop - Ask Bigger (and New) Questions and Get Better, Faster AnswersRevolution Analytics
The business cases for Hadoop can be made on the tremendous operational cost savings that it affords. But why stop there? The integration of R-powered analytics in Hadoop presents a totally new value proposition. Organizations can write R code and deploy it natively in Hadoop without data movement or the need to write their own MapReduce. Bringing R-powered predictive analytics into Hadoop will accelerate Hadoop’s value to organizations by allowing them to break through performance and scalability challenges and solve new analytic problems. Use all the data in Hadoop to discover more, grow more quickly, and operate more efficiently. Ask bigger questions. Ask new questions. Get better, faster results and share them.
DBCS Office Hours - Modernization through MigrationTammy Bednar
Speakers:
Kiran Tailor - Cloud Migration Director, Oracle
Kevin Lief – Partnership and Alliances Manager - (EMEA), Advanced
Modernisation of mainframe and other legacy systems allows organizations to capitalise on existing assets as they move toward more agile, cost-effective and open technology environments. Do you have legacy applications and databases that you could modernise with Oracle, allowing you to apply cutting edge technologies, like machine learning, or BI for deeper insights about customers or products? Come to this webcast to learn about all this and how Advanced can help to get you on the path to modernisation.
AskTOM Office Hours offers free, open Q&A sessions with Oracle Database experts. Join us to get answers to all your questions about Oracle Database Cloud Service.
Building a Scalable and reliable open source ML Platform with MLFlowGoDataDriven
This document discusses building a scalable and open source machine learning platform. It introduces MLOps and describes ING's ML batch platform use case. The machine learning lifecycle is presented, noting that operationalizing machine learning models is difficult due to infrastructure deployment challenges, lack of collaboration and standardization. An ideal MLOps approach is described with flexible, scalable, automated and standardized processes. Benefits of ING's MLOps approach include increased efficiency, speed, quality, security and auditability. Open source tools that could be leveraged are also presented.
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/
Mykola Mykytenko: MLOps: your way from nonsense to valuable effect (approache...Lviv Startup Club
Mykola Mykytenko: MLOps: your way from nonsense to valuable effect (approaches, cases, tools)
AI & BigData Online Day 2021
Website - https://aiconf.com.ua/
Youtube - https://www.youtube.com/startuplviv
FB - https://www.facebook.com/aiconf
Deep learning is a subset of machine learning that uses neural networks to enable computers to learn from large amounts of data. It can be used to solve problems involving data dependencies, huge data volumes, and highly accurate prediction and classification models. Deep learning has applications in computer vision, natural language processing, building chatbots, marketing, banking, and more. Common deep learning architectures include convolutional neural networks, recurrent neural networks, self-organizing maps, and autoencoders. A case study describes how a bank used deep learning to develop a predictive model to identify customers likely to close their accounts and the key factors driving this, in order to reduce business risk and retain customers.
Similar to DutchMLSchool. ML for Energy Trading and Automotive Sector (20)
Digital Transformation and Process Optimization in ManufacturingBigML, Inc
Keyanoush Razavidinani, Digital Services Consultant at A1 Digital, a BigML Partner, highlights why it is important to identify and reduce human bottlenecks that optimize processes and let you focus on important activities. Additionally, Guillem Vidal, Machine Learning Engineer at BigML completes the session by showcasing how Machine Learning is put to use in the manufacturing industry with a use case to detect factory failures.
The Road to Production: Automating your Anomaly Detectors - by jao (Jose A. Ortega), Co-Founder and Chief Technology Officer at BigML.
*Machine Learning School in The Netherlands 2022.
DutchMLSchool 2022 - ML for AML ComplianceBigML, Inc
Machine Learning for Anti Money Laundering Compliance, by Kevin Nagel, Consultant and Data Scientist at INFORM.
*Machine Learning School in The Netherlands 2022.
DutchMLSchool 2022 - Multi Perspective AnomaliesBigML, Inc
Multi Perspective Anomalies, by Jan W Veldsink, Master in the art of AI at Nyenrode, Rabobank, and Grio.
*Machine Learning School in The Netherlands 2022.
DutchMLSchool 2022 - My First Anomaly Detector BigML, Inc
The document discusses building an anomaly detector model to identify unusual transactions in a dataset. It describes loading transaction data with 31 features into the BigML platform and creating an anomaly detector model. The model scores new data and identifies the most anomalous fields to help detect fraud. Creating the anomaly detector involves interpreting the data, exploring the dataset distribution, and setting a threshold score to define what is considered anomalous.
DutchMLSchool 2022 - History and Developments in MLBigML, Inc
History and Present Developments in Machine Learning, by Tom Dietterich, Emeritus Professor of computer science at Oregon State University and Chief Scientist at BigML.
*Machine Learning School in The Netherlands 2022.
Introduction to End-to-End Machine Learning: Classification and Regression - Mercè Martín, VP of Bindings and Applications at BigML.
*Machine Learning School in The Netherlands 2022.
DutchMLSchool 2022 - A Data-Driven CompanyBigML, Inc
A Data-Driven Company: 21 Lessons for Large Organizations to Create Value from AI, by Richard Benjamins, Chief AI and Data Strategist at Telefónica.
*Machine Learning School in The Netherlands 2022.
DutchMLSchool 2022 - ML in the Legal SectorBigML, Inc
How Machine Learning Transforms and Automates Legal Services, by Arnoud Engelfriet, Co-Founder at Lynn Legal.
*Machine Learning School in The Netherlands 2022.
This document describes a proposed solution using machine learning and artificial intelligence to help create a safer stadium experience. The solution involves two parts: 1) linking access to stadiums to a verified identity through a fan app for preregistration, and 2) using AI/ML to help detect unwanted behaviors or events early. The rest of the document provides more details on the proposed smart video review framework, including using computer vision and audio analysis techniques to help identify issues like flares, flags, banners, chants including monkey chants. The goal is to help reviewers more efficiently identify potential problems but with privacy, ethics and human oversight.
DutchMLSchool 2022 - Process Optimization in Manufacturing PlantsBigML, Inc
Process Optimization in Manufacturing Plants, by Keyanoush Razavidinani, Digital Business Consultant at A1 Digital.
*Machine Learning School in The Netherlands 2022.
DutchMLSchool 2022 - Anomaly Detection at ScaleBigML, Inc
Lessons Learned Applying Anomaly Detection at Scale, by Álvaro Clemente, Machine Learning Engineer at BigML.
*Machine Learning School in The Netherlands 2022.
DutchMLSchool 2022 - Citizen Development in AIBigML, Inc
The document discusses the need for citizen developers and humans in the AI/ML process. It notes that while technology and talent are important, company culture must also support broad data analytics and AI/ML adoption. It then provides examples of how involving domain experts can help attribute meaning to correlations and build better causal models to improve AI systems. The document advocates for a systems thinking approach and having humans in the loop to help AI/ML systems consider the wider context and avoid issues like bias.
This new feature is a continuation of and improvement on our previous Image Processing release. Now, Object Detection lets you go a step further with your image data and allows you to locate objects and annotate regions in your images. Once your image regions are defined, you can train and evaluate Object Detection models, make predictions with them, and automate end-to-end Machine Learning workflows on a single platform. To make that possible, BigML enables Object Detection by introducing the regions optype.
As with any other BigML feature, Object Detection is available from the BigML Dashboard, API, and WhizzML for automation. Object Detection is extremely helpful to tackle a wide range of computer vision use cases such as medical image analysis, quality control in manufacturing, license plate recognition in transportation, people detection in security surveillance, among many others.
This new release brings Image Processing to the BigML platform, a feature that enhances our offering to solve image data-driven business problems with remarkable ease of use. Because BigML treats images as any other data type, this unique implementation allows you to easily use image data alongside text, categorical, numeric, date-time, and items data types as input to create any Machine Learning model available in our platform, both supervised and unsupervised.
Now, it is easier than ever to solve a wide variety of computer vision and image classification use cases in a single platform: label your image data, train and evaluate your models, make predictions, and automate your end-to-end Machine Learning workflows. As with any other BigML feature, Image Processing is available from the BigML Dashboard, API, and WhizzML, and it can be applied to solve use cases such as medical image analysis, visual product search, security surveillance, and vehicle damage detection, among others.
Machine Learning in Retail: Know Your Customers' Customer. See Your FutureBigML, Inc
This session presents a quite common situation for those working in food and beverage retail (FnB) and highlights interesting insights to fight waste reduction.
Speaker: Stephen Kinns, CEO and Co-Founder at catsAi.
*ML in Retail 2021: Webinar.
Machine Learning in Retail: ML in the Retail SectorBigML, Inc
This is an introductory session about the role that Machine Learning is playing in the retail sector and how it is being deployed across the different areas of this industry.
Speaker: Atakan Cetinsoy, VP of Predictive Applications at BigML.
*ML in Retail 2021: Webinar.
ML in GRC: Machine Learning in Legal Automation, How to Trust a LawyerbotBigML, Inc
This presentation analyzes the role that Machine Learning plays in legal automation with a real-world Machine Learning application.
Speaker: Arnoud Engelfriet, Co-Founder at Lynn Legal.
*ML in GRC 2021: Virtual Conference.
ML in GRC: Supporting Human Decision Making for Regulatory Adherence with Mac...BigML, Inc
This is a real-life Machine Learning use case about integrated risk.
Speakers: Thomas Rengersen, Product Owner of the Governance Risk and Compliance Tool for Rabobank, and Thomas Alderse Baas, Co-Founder and Director of The Bowmen Group.
*ML in GRC 2021: Virtual Conference.
We are pleased to share with you the latest VCOSA statistical report on the cotton and yarn industry for the month of March 2024.
Starting from January 2024, the full weekly and monthly reports will only be available for free to VCOSA members. To access the complete weekly report with figures, charts, and detailed analysis of the cotton fiber market in the past week, interested parties are kindly requested to contact VCOSA to subscribe to the newsletter.
06-18-2024-Princeton Meetup-Introduction to MilvusTimothy Spann
06-18-2024-Princeton Meetup-Introduction to Milvus
tim.spann@zilliz.com
https://www.linkedin.com/in/timothyspann/
https://x.com/paasdev
https://github.com/tspannhw
https://github.com/milvus-io/milvus
Get Milvused!
https://milvus.io/
Read my Newsletter every week!
https://github.com/tspannhw/FLiPStackWeekly/blob/main/142-17June2024.md
For more cool Unstructured Data, AI and Vector Database videos check out the Milvus vector database videos here
https://www.youtube.com/@MilvusVectorDatabase/videos
Unstructured Data Meetups -
https://www.meetup.com/unstructured-data-meetup-new-york/
https://lu.ma/calendar/manage/cal-VNT79trvj0jS8S7
https://www.meetup.com/pro/unstructureddata/
https://zilliz.com/community/unstructured-data-meetup
https://zilliz.com/event
Twitter/X: https://x.com/milvusio https://x.com/paasdev
LinkedIn: https://www.linkedin.com/company/zilliz/ https://www.linkedin.com/in/timothyspann/
GitHub: https://github.com/milvus-io/milvus https://github.com/tspannhw
Invitation to join Discord: https://discord.com/invite/FjCMmaJng6
Blogs: https://milvusio.medium.com/ https://www.opensourcevectordb.cloud/ https://medium.com/@tspann
Expand LLMs' knowledge by incorporating external data sources into LLMs and your AI applications.
Discovering Digital Process Twins for What-if Analysis: a Process Mining Appr...Marlon Dumas
This webinar discusses the limitations of traditional approaches for business process simulation based on had-crafted model with restrictive assumptions. It shows how process mining techniques can be assembled together to discover high-fidelity digital twins of end-to-end processes from event data.
Discovering Digital Process Twins for What-if Analysis: a Process Mining Appr...
DutchMLSchool. ML for Energy Trading and Automotive Sector
1. Machine Learning for Energy Trading,
Automotive Sector and Logistics
BigML Summerschool, Breukelen, NL
Dr. Dieter Mayr, 8th of July, 2019
2. 2
i. A1 Digital Machine Learning Platform powered by BigML
ii. Our perception on Machine Learning in various industries
iii. Use cases and solutions
iv. Our approach and recommendations
Agenda
3. 3
Who we are
A1 Digital – IoT, ML, Cloud and Security Focus
3 Headquarters
Vienna, Munich and
Lausanne - present
in 10 countries
180
Employees
More than 500
international
customer projects
AffiliatedGroup
A1 Telekom Austria
Group & America Móvil
Your Partner
for Cloud, ML, IoT
and Security
4. 4
A1 Digital‘s ML Team
Our Mission: A1 Digital enables its customers to perform
Machine Learning and Advanced Analytics at Scale
Core ML Team: Data Scientists as
… ML Consultants
… Product Manager
… Data Engineer & DevOps
Our technology stack: one large BigML deployment running
in our own cloud (Exoscale)
Our offer: Ml
7. 7
Market starts demanding ML
How usually discover B2B customers dealing with ML:
SMEs
Little strategy on ML, mainly individual persons
Little low-hanging fruits: they know how to solve their problems
Depending on industry: IT mostly drives efforts for ML
Input often from the higher management „to try ML“
Many obstacles: little resources (time), fear of learning curve,
underestimation of potential of own data, bad first experiences
Large
Enterprises
Centralized vs decentralized ML Teams
Often: strategy + „center of excellence“
Large investments in complex infrastructure
Communication: Business vs. Data Science team
Lack of agile and simple ways to explore ML
10. 10
Data related project remain complex….
• Even with best tools, it remains a
challenge to find a use case with
adequate data…
• ML is just one (important!)
element but cannot solve all
problems
• But: with ML project,
stakeholders learn, understand
the imporance of data and
become creative in finding new
use cases in thair fields.
11. 11
But where we believe BigML is essential creating added value
• Nothing to install or configure
• No programming skills
required
• Smart data input
• Automatic Modeling
• Smart model consumption
PROGRAMMABLE
• Offer basic constructors that enable
sophisticated ML strategies
• API-first
SCALABLE
• Fully-automated infrastructure
• Instant access, instant scale
• All the complexities related to
infrastructure are abstracted away
• Serverless
13. 13
What kind of ML Platform is needed ?
• Nothing to install or configure
• No programming skills
required
• Smart data input
• Automatic Modeling
• Smart model consumption
PROGRAMMABLE
• Offer basic constructors that enable
sophisticated ML strategies
• API-first
SCALABLE
• Fully-automated infrastructure
• Instant access, instant scale
• All the complexities related to
infrastructure are abstracted away
• Serverless
14. 14
Machine Learning for
Energy Trading
REQUIREMENTS
Automated workflow for predicting control energy
prices
Evaluation of historic bids and revenues
OUR SOLUTION
Analysis of historic trading strategy
Expert workshops for defining data sources (spot
market prices, weather, etc.) and feature
engineering
Identify best performing machine learning
algorithms
Dashboard as decision support tool for trading and
evaluating historic bids
RESULTS
10 % higher revenues from auctions
More transparent decisions and reduced workload
15. 15
Energy Trading Dashboard
ML Application for Energy Trading
Download data &
Feature Engineering
Auction
announcements
Filter
announcements
„ML-ready Data“
Price prediction for
the next auction
Auction results
Additional data
16. 16
What kind of ML Platform is needed ?
• Nothing to install or configure
• No programming skills
required
• Smart data input
• Automatic Modeling
• Smart model consumption
PROGRAMMABLE
• Offer basic constructors that enable
sophisticated ML strategies
• API-first
SCALABLE
• Fully-automated infrastructure
• Instant access, instant scale
• All the complexities related to
infrastructure are abstracted away
• Serverless
17. 17
Machine Learning for
Wagon Hire and Rail Logistics
REQUIREMENTS
Forecast model on maintenance time required for each
wagon
Creation of complex data model
Scalable Solution to rapidly analyze vast amounts of data
Open, exportable models, ready to use in production
OUR SOLUTION
Consulting on use case selection
Workshop on data selection (gain vs. efforts)
Review of BigML platform how it fits into their
requirements
Test OptiML on existing challenges
RESULTS
OptiML outperforms existing models
Data Science unit works faster, increases collaboration
Leveraging existing models & efforts (from Python)
18. 18
Bindings
How to use bindings for the ML platform? Python example
Add source file
Create a dataset from source
Split data set into training and
test datasets
Create a model (decision tree)
with a training dataset
Evaluate with a test dataset
Get desired evaluation
parameters
19. 19
Bindings
Use the dashboard to evaluate any steps
Look up the Script-ID
Create execution for new
source with Script-ID
“Scriptify” one step or the
whole workflow
20. 20
What kind of ML Platform is needed ?
• Nothing to install or configure
• No programming skills
required
• Smart data input
• Automatic Modeling
• Smart model consumption
PROGRAMMABLE
• Offer basic constructors that enable
sophisticated ML strategies
• API-first
SCALABLE
• Fully-automated infrastructure
• Instant access, instant scale
• All the complexities related to
infrastructure are abstracted away
• Serverless
21. 21
Machine Learning for
Automotive Supplier
REQUIREMENTS
Central ML Platform enabling potentially thousands
of engineers worldwide to get started with ML
Easy entry in ML with ability to fully scale fast
OUR SOLUTION
Discuss ML efforts and strategy
Develop a PoC (on injection molding machines)
Evaluate results and feedback from subject matter
experts
Consulting to create a concept about how to roll-
out ML to business units
RESULTS
10+ potential use cases
Roadmap, learning program and show cases
Cost-effective strategy for a global accessable ML
platform enabling engineers to optimize data
related routines
22. 22
Challenging market expectations demands development of
ML-led Sense-Predict-React capabilities
• Conformity to
specification
• Product performance
Quality
• Low Rework cost
• High percentage of
passed quality inspection
• Low cost of quality
control
• Delivery Lead Times
• On Time Delivery
• Stock availability
Delivery
• Short production and
delivery lead time
• High accuracy of
inventory status
• High dependability of
internal lead times
• Product Selling
• Competitive Pricing
• Disruption driver
Cost
• Low unit cost of
manufacturing
• Fast inventory turnover
• High capacity utilization
• Product range
• Product portfolio offered
• Volume / product mix changes
Flexibility
• Shortest MRP and set
up times
• Shortest length of fixed
production schedule
• Optimal amount of
operating capacity
24. DataExploration
• Use-Case workshop
• First, quick results
• Follow-up potential
Pilot Package
• Multiple workshops
• Multiple data
sources
• Reliable results
ProductiveSystem
• Improve pilot solution
• Integration in existing system.
• Deployment of Application
Expandon yourown
• Implement further
use cases on your
own
The way to Machine Learning based applications
We enable our customers step-by-step
Continuous Machine Learning trainings and support by A1 Digital experts
from data to results in about 6 weeks
25. 25
Prioritize impact & reuse
Develop ML strategy core analytical capabilities & easy access (MLaaS) platforms
Leverage partner stay focused on your business
Educate (repeatedly) Unleash the Citizen Data Scientist.
Domain knowledge respect expertise and bring ML closer to decision making
Freedom Allow freedom for creativity and potentially failing
Small steps focus on fast first projects and stay agile
Start now
Recommendations to our customers