We have successfully done the Demand Forecasting for the chemical industries & we are presenting Demand Forecasting Case Study PPT in the form of a PDF
SAP Applications and the Modern Data Scientist - Predictive Analytics for the...Dickinson + Associates
Abstract: SAP Predictive Analytics 2.3 bridges the gaps between IT, Business and Analytics. Learn how SAP Predictive Analytics 2.3 leverages current SAP infrastructure and provides the data preparation, visualization and data modeling tools necessary to gain insights from your data.
Join us as we will take a look at SAP Predictive Analytics (PA) 2.3 and demonstrate the value by analyzing data that will help drive real-world decisions:
• Understand where SAP PA 2.3 resides in SAP’s analytics roadmap
• Outline system and hardware requirements for implementation
• Learn how SAP PA 2.3 is not just for Data Scientists
• Demonstrate capabilities within SAP PA 2.3
• Answer questions and discuss how PA 2.3 can improve your business
Symposium 2019 : Gestion de projet en Intelligence ArtificiellePMI-Montréal
L’objectif d’un projet impliquant l’intelligence artificielle est d’accélérer la prise de décision, voir même, d’automatiser les actions qui doivent être effectuées dans le cadre d’une tache. La principale difficulté est qu’il n’est pas possible de savoir à l’avance quelle méthode d’AI permettra d’atteindre l’objectif. La gestion du projet est souvent atypique et nécessite d’être flexible en respectant toutefois des contraintes de budget. Pour cette raison une approche waterfall est à éviter. Toutefois, nous allons voir qu’elle peut être exploitée dans certaines phases du projet.
Lors de cette présentation, nous allons voir les trois phases du projet : prototypage de la solution, mise en production, ainsi que les stratégies de maintien à plus long terme de la solution.
Dr. Nathanael Weill
Create Success with Analytics: Predictive Analytics 101: Your Roadmap to Driv...Aggregage
Predictive analytics is an increasingly common buzzword with many forms. It seems everyone has their own take on what it is and which best practices and business benefits apply.
What does predictive analytics really mean? We’ll explore real-world examples of predictive in action and outline steps to help you maximize its value.
Create Success with Analytics: Predictive Analytics 101: Your Roadmap to Driv...Hannah Flynn
Predictive analytics is an increasingly common buzzword with many forms. It seems everyone has their own take on what it is and which best practices and business benefits apply.
What does predictive analytics really mean? We’ll explore real-world examples of predictive in action and outline steps to help you maximize its value.
Sergio Juarez, Elemica – “From Big Data to Value: The Power of Master Data Ma...Elemica
The document discusses master data management (MDM). It defines MDM as combining data governance practices with software tools to achieve a single version of the truth across systems. It then lists several market trends driving increased adoption of MDM, including MDM in the cloud, growing MDM software sales, rising information volumes, increased recognition of data's importance, and costs of poor data quality. The document also outlines how MDM can generate value in areas like customer/supplier relationships, engineering productivity, inventory costs, and procurement costs. Finally, it discusses common data issues that MDM can help solve and provides examples of potential solutions.
What is Data analytics? How is data analytics a better career option?Aspire Techsoft Academy
Are you looking for the Best Data analytics Training Institute in Pune Aspire Techsoft offers you the best SAS Data Analytics Certification Training in Pune with Certified expert faculties.
Why are so many businesses still struggling to realize the full value of their AI projects? The answer appears to lie in the gap between applying AI in pockets versus applying it at scale. This talk reflects on what is necessary to bring AI in your company to the next level.
Ai design sprint - Finance - Wealth managementChinmay Patel
Chinmay Patel presented an AI design sprint methodology. The methodology involves identifying a business problem, gathering and preparing relevant data, training and deploying a model, and maintaining/improving the model over time. As an example, Chinmay discussed how this process was used to build an automated claim resolution bot that can resolve claims within 3 seconds with no paperwork. The methodology was also proposed for a wealth management use case to perform user segmentation using clustering algorithms.
SAP Applications and the Modern Data Scientist - Predictive Analytics for the...Dickinson + Associates
Abstract: SAP Predictive Analytics 2.3 bridges the gaps between IT, Business and Analytics. Learn how SAP Predictive Analytics 2.3 leverages current SAP infrastructure and provides the data preparation, visualization and data modeling tools necessary to gain insights from your data.
Join us as we will take a look at SAP Predictive Analytics (PA) 2.3 and demonstrate the value by analyzing data that will help drive real-world decisions:
• Understand where SAP PA 2.3 resides in SAP’s analytics roadmap
• Outline system and hardware requirements for implementation
• Learn how SAP PA 2.3 is not just for Data Scientists
• Demonstrate capabilities within SAP PA 2.3
• Answer questions and discuss how PA 2.3 can improve your business
Symposium 2019 : Gestion de projet en Intelligence ArtificiellePMI-Montréal
L’objectif d’un projet impliquant l’intelligence artificielle est d’accélérer la prise de décision, voir même, d’automatiser les actions qui doivent être effectuées dans le cadre d’une tache. La principale difficulté est qu’il n’est pas possible de savoir à l’avance quelle méthode d’AI permettra d’atteindre l’objectif. La gestion du projet est souvent atypique et nécessite d’être flexible en respectant toutefois des contraintes de budget. Pour cette raison une approche waterfall est à éviter. Toutefois, nous allons voir qu’elle peut être exploitée dans certaines phases du projet.
Lors de cette présentation, nous allons voir les trois phases du projet : prototypage de la solution, mise en production, ainsi que les stratégies de maintien à plus long terme de la solution.
Dr. Nathanael Weill
Create Success with Analytics: Predictive Analytics 101: Your Roadmap to Driv...Aggregage
Predictive analytics is an increasingly common buzzword with many forms. It seems everyone has their own take on what it is and which best practices and business benefits apply.
What does predictive analytics really mean? We’ll explore real-world examples of predictive in action and outline steps to help you maximize its value.
Create Success with Analytics: Predictive Analytics 101: Your Roadmap to Driv...Hannah Flynn
Predictive analytics is an increasingly common buzzword with many forms. It seems everyone has their own take on what it is and which best practices and business benefits apply.
What does predictive analytics really mean? We’ll explore real-world examples of predictive in action and outline steps to help you maximize its value.
Sergio Juarez, Elemica – “From Big Data to Value: The Power of Master Data Ma...Elemica
The document discusses master data management (MDM). It defines MDM as combining data governance practices with software tools to achieve a single version of the truth across systems. It then lists several market trends driving increased adoption of MDM, including MDM in the cloud, growing MDM software sales, rising information volumes, increased recognition of data's importance, and costs of poor data quality. The document also outlines how MDM can generate value in areas like customer/supplier relationships, engineering productivity, inventory costs, and procurement costs. Finally, it discusses common data issues that MDM can help solve and provides examples of potential solutions.
What is Data analytics? How is data analytics a better career option?Aspire Techsoft Academy
Are you looking for the Best Data analytics Training Institute in Pune Aspire Techsoft offers you the best SAS Data Analytics Certification Training in Pune with Certified expert faculties.
Why are so many businesses still struggling to realize the full value of their AI projects? The answer appears to lie in the gap between applying AI in pockets versus applying it at scale. This talk reflects on what is necessary to bring AI in your company to the next level.
Ai design sprint - Finance - Wealth managementChinmay Patel
Chinmay Patel presented an AI design sprint methodology. The methodology involves identifying a business problem, gathering and preparing relevant data, training and deploying a model, and maintaining/improving the model over time. As an example, Chinmay discussed how this process was used to build an automated claim resolution bot that can resolve claims within 3 seconds with no paperwork. The methodology was also proposed for a wealth management use case to perform user segmentation using clustering algorithms.
Turning Big Data Analytics To Knowledge PowerPoint Presentation SlidesSlideTeam
This complete deck covers various topics and highlights important concepts. It has PPT slides which cater to your business needs. This complete deck presentation emphasizes Turning Big Data Analytics To Knowledge PowerPoint Presentation Slides and has templates with professional background images and relevant content. This deck consists of total of twenty two slides. Our designers have created customizable templates, keeping your convenience in mind. You can edit the colour, text and font size with ease. Not just this, you can also add or delete the content if needed. Get access to this fully editable complete presentation by clicking the download button below. http://bit.ly/2HHUsqf
1) DevOps aims to improve collaboration between development and operations teams through practices like automation and continuous integration and delivery. Integrating cognitive services like machine learning into DevOps can help automate manual tasks like incident detection and root cause analysis.
2) Cognitive services use machine learning algorithms to simulate human thought processes. They acquire knowledge from data to identify patterns and model solutions. Integrating these services into DevOps can help automate support of applications in production.
3) IT analytics tools can analyze data using techniques like textual, statistical, and configuration pattern analysis to extract valuable insights. These tools can help address challenges in DevOps by monitoring changes across environments and validating pre-production testing.
Integrating cognitive services in to your devops strategyAspire Systems
Why do we need DevOps in our organization? Well we may have expert team in software development, Release management, QA and IT Operations. Is this really enough to deliver the product on time when we use traditional agile software development approaches alone?
This document contains the resume of Jisu Behera, who has over 15 years of experience in data science and analytics roles. She has extensive experience building machine learning models for credit risk assessment, fraud detection, and other domains. Her technical skills include Python, machine learning algorithms like random forest and neural networks, and tools like TensorFlow, Keras, and Spark. She is currently a Data Science Manager at HCL Technologies, where she builds credit risk models and provides analytics support.
This document is a curriculum vitae for Firdos A, who has 9 years of experience as a senior analyst working on projects involving sales, marketing, and customer relationship processes using technologies like Oracle, Siebel, and SAS. They have experience in areas like operations management, campaign operations, process design, requirements gathering, and gap analysis. Their experience includes projects for companies like Sprint, PayPal, GSK, Dell, and more, where they performed roles like data analysis, requirement gathering, testing, and more.
[DSC Europe 22] The Making of a Data Organization - Denys HolovatyiDataScienceConferenc1
Data teams often struggle to deliver value. KPIs, data pipelines, or ML driven predictions aren't inherently useful - unless the data team enables the business to use them. Having worked on 37 data projects over the past 5 years, with total client revenue clocking at about $350B, I started noticing simple success factors - and summarized those in the Operating Model Canvas & the Value Delivery Process. With those, I branched out into what I call data organization consulting and help clients build their data teams for success, the one you see not only on paper but also in your P&L. In this talk, I'll share some insight with you.
We are a team of technology leaders and engineers with passion and single-minded focus on providing efficient and robust Information Technology solutions for clients across the globe.
We are extremely passionate about our work that keeps us agile and makes us who we are. We help each other and work together to make Ejyle a great workplace.
GSTi India’s mission is to provide end-to-end IT solutions for clients across the globe by aligning, creating, developing and providing efficient and cost effective services.
RCG has developed a unique approach to helping its clients solve business problems using data. Whether you are interested in learning how to use technology to expose more value from your data through analytics solutions or understanding whether statistical analysis would surface new insights, RCG is ready to help with its Data & Analytics Practice.
Data summit connect fall 2020 - rise of data opsRyan Gross
Data governance teams attempt to apply manual control at various points for consistency and quality of the data. By thinking of our machine learning data pipelines as compilers that convert data into executable functions and leveraging data version control, data governance and engineering teams can engineer the data together, filing bugs against data versions, applying quality control checks to the data compilers, and other activities. This talk illustrates how innovations are poised to drive process and cultural changes to data governance, leading to order-of-magnitude improvements.
They serve customers across small, Mid-size and Enterprise segments-ranging from $50M to $50B in size-in multiple industries.
Services include: End-to-end implementations, managed services, project management, training, integrations with enterprise systems, and business process re-engineering.
The document discusses several important considerations for companies looking to implement artificial intelligence, including developing an AI transformation playbook, assessing an organization's AI maturity, anticipating costs and timing, deciding whether to build or buy AI solutions, and addressing important legal and ethical issues around explainability, privacy, fairness, and safety. The document provides guidance on how companies can effectively lead their organization into the AI era by establishing the right strategies, processes, and safeguards.
Predicto is a predictive analytics system that uses machine learning and big data algorithms to analyze individual customer event histories and identify unsatisfied customers. This allows companies to target marketing campaigns specifically at stimulating unsatisfied customers before they drop off, in order to improve key performance indicators like retention, conversion, and virality. The implementation process involves an initial investigation of whether predictive analytics can be applied to a company's data, integrating the Predicto SDK to collect customer behavior data, and tuning predictive algorithms over 6-8 weeks.
For the next 40 minutes, I’d like to share with you our experience leveraging AI for businesses.
We’ll first do a tiny little quiz to check your AI knowledge - don’t worry it’s not technical at all.
Then we discuss the common challenges that startups face and give examples on how you can navigate them.
From here, you can do a self-assessment of where you are in the AI maturity journey.
Then we go to through 3 case studies in detail based on their AI maturity. At the end, we also discuss how you can spot opportunities to use AI in your company!
Finally, we close off with a summary and a list of recommendations of no-code AI tools that you can take a look at :)
It’s a loot of content, but the idea is that you will be able to walk away with a renewed understanding of what it takes to build an AI-enabled business but more importantly, how you can be in the driver seat and do it yourself.
We’ll take Q&As at the end and if you have any questions please add them onto Slido :)
This document provides a summary of Gururaj H. R.'s professional experience and qualifications. He has over 10 years of experience in data analysis, working for companies like Ariba Technologies, Symphony Marketing Solutions, and Bells Softech Limited. His experience includes tasks like data collection, analysis, report generation, and troubleshooting. He has an MBA in supply chain management and a diploma in mechanical engineering. His skills include MS Office, basic SQL, and he has experience working with cloud applications.
How to Create an Effective Enterprise AI Strategy.pdfAivada
In today’s fast-paced and technologically driven market, establishing a robust Enterprise AI Strategy is essential for any organization aiming to maintain competitiveness and foster innovation. This comprehensive guide explains the concept of an enterprise AI strategy, elucidates the myriad benefits it delivers, and provides a detailed framework on how to meticulously craft one for your organization.
Now more than ever, organizations must capture what’s happening in the business and transform their data into faster, smarter decisions. In this deck, you’ll learn how Deloitte uses Workday Prism Analytics to harness financial, workforce, and operational data by unlocking key analytics at the most critical times.
View related videos:
Welcome to the New World of Analytics.
https://www.youtube.com/watch?v=DLOekjChar0
Build Belonging and Diversity | Insights https://www.youtube.com/watch?v=slhpTY5z68c
Nadine Schöne, Dataiku. The Complete Data Value Chain in a NutshellIT Arena
Dr. Nadine Schöne is a Senior Solutions Architect at Dataiku in Berlin. In this role, she deals with all aspects of the data value chain for all users – including integration of data sources, ETL, cooperation, statistics, modelling, but also operationalization, monitoring, automatization and security during production. She regularly talks at conferences, holds webinars and writes articles.
Speech Overview:
How can you get the most out of your data – while staying flexible in your choice of infrastructure and without having to integrate a multitude of tools for the different personas involved? Maximizing the value you get out of your data is a necessity today. Looking at the whole picture as well as careful planning are the key for success. We will have a look at the complete data value chain from end to end: from the data stores, collaboration features, data preparation, visualization and automation capabilities, and external compute to scheduling, operationalization, monitoring and security.
Artificial Intelligence, Predictive Analysis, Blockchain, and Machine Learning platforms can be easily
Ingested to our Analytics to provide you with the best solution according to your business needs
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
Turning Big Data Analytics To Knowledge PowerPoint Presentation SlidesSlideTeam
This complete deck covers various topics and highlights important concepts. It has PPT slides which cater to your business needs. This complete deck presentation emphasizes Turning Big Data Analytics To Knowledge PowerPoint Presentation Slides and has templates with professional background images and relevant content. This deck consists of total of twenty two slides. Our designers have created customizable templates, keeping your convenience in mind. You can edit the colour, text and font size with ease. Not just this, you can also add or delete the content if needed. Get access to this fully editable complete presentation by clicking the download button below. http://bit.ly/2HHUsqf
1) DevOps aims to improve collaboration between development and operations teams through practices like automation and continuous integration and delivery. Integrating cognitive services like machine learning into DevOps can help automate manual tasks like incident detection and root cause analysis.
2) Cognitive services use machine learning algorithms to simulate human thought processes. They acquire knowledge from data to identify patterns and model solutions. Integrating these services into DevOps can help automate support of applications in production.
3) IT analytics tools can analyze data using techniques like textual, statistical, and configuration pattern analysis to extract valuable insights. These tools can help address challenges in DevOps by monitoring changes across environments and validating pre-production testing.
Integrating cognitive services in to your devops strategyAspire Systems
Why do we need DevOps in our organization? Well we may have expert team in software development, Release management, QA and IT Operations. Is this really enough to deliver the product on time when we use traditional agile software development approaches alone?
This document contains the resume of Jisu Behera, who has over 15 years of experience in data science and analytics roles. She has extensive experience building machine learning models for credit risk assessment, fraud detection, and other domains. Her technical skills include Python, machine learning algorithms like random forest and neural networks, and tools like TensorFlow, Keras, and Spark. She is currently a Data Science Manager at HCL Technologies, where she builds credit risk models and provides analytics support.
This document is a curriculum vitae for Firdos A, who has 9 years of experience as a senior analyst working on projects involving sales, marketing, and customer relationship processes using technologies like Oracle, Siebel, and SAS. They have experience in areas like operations management, campaign operations, process design, requirements gathering, and gap analysis. Their experience includes projects for companies like Sprint, PayPal, GSK, Dell, and more, where they performed roles like data analysis, requirement gathering, testing, and more.
[DSC Europe 22] The Making of a Data Organization - Denys HolovatyiDataScienceConferenc1
Data teams often struggle to deliver value. KPIs, data pipelines, or ML driven predictions aren't inherently useful - unless the data team enables the business to use them. Having worked on 37 data projects over the past 5 years, with total client revenue clocking at about $350B, I started noticing simple success factors - and summarized those in the Operating Model Canvas & the Value Delivery Process. With those, I branched out into what I call data organization consulting and help clients build their data teams for success, the one you see not only on paper but also in your P&L. In this talk, I'll share some insight with you.
We are a team of technology leaders and engineers with passion and single-minded focus on providing efficient and robust Information Technology solutions for clients across the globe.
We are extremely passionate about our work that keeps us agile and makes us who we are. We help each other and work together to make Ejyle a great workplace.
GSTi India’s mission is to provide end-to-end IT solutions for clients across the globe by aligning, creating, developing and providing efficient and cost effective services.
RCG has developed a unique approach to helping its clients solve business problems using data. Whether you are interested in learning how to use technology to expose more value from your data through analytics solutions or understanding whether statistical analysis would surface new insights, RCG is ready to help with its Data & Analytics Practice.
Data summit connect fall 2020 - rise of data opsRyan Gross
Data governance teams attempt to apply manual control at various points for consistency and quality of the data. By thinking of our machine learning data pipelines as compilers that convert data into executable functions and leveraging data version control, data governance and engineering teams can engineer the data together, filing bugs against data versions, applying quality control checks to the data compilers, and other activities. This talk illustrates how innovations are poised to drive process and cultural changes to data governance, leading to order-of-magnitude improvements.
They serve customers across small, Mid-size and Enterprise segments-ranging from $50M to $50B in size-in multiple industries.
Services include: End-to-end implementations, managed services, project management, training, integrations with enterprise systems, and business process re-engineering.
The document discusses several important considerations for companies looking to implement artificial intelligence, including developing an AI transformation playbook, assessing an organization's AI maturity, anticipating costs and timing, deciding whether to build or buy AI solutions, and addressing important legal and ethical issues around explainability, privacy, fairness, and safety. The document provides guidance on how companies can effectively lead their organization into the AI era by establishing the right strategies, processes, and safeguards.
Predicto is a predictive analytics system that uses machine learning and big data algorithms to analyze individual customer event histories and identify unsatisfied customers. This allows companies to target marketing campaigns specifically at stimulating unsatisfied customers before they drop off, in order to improve key performance indicators like retention, conversion, and virality. The implementation process involves an initial investigation of whether predictive analytics can be applied to a company's data, integrating the Predicto SDK to collect customer behavior data, and tuning predictive algorithms over 6-8 weeks.
For the next 40 minutes, I’d like to share with you our experience leveraging AI for businesses.
We’ll first do a tiny little quiz to check your AI knowledge - don’t worry it’s not technical at all.
Then we discuss the common challenges that startups face and give examples on how you can navigate them.
From here, you can do a self-assessment of where you are in the AI maturity journey.
Then we go to through 3 case studies in detail based on their AI maturity. At the end, we also discuss how you can spot opportunities to use AI in your company!
Finally, we close off with a summary and a list of recommendations of no-code AI tools that you can take a look at :)
It’s a loot of content, but the idea is that you will be able to walk away with a renewed understanding of what it takes to build an AI-enabled business but more importantly, how you can be in the driver seat and do it yourself.
We’ll take Q&As at the end and if you have any questions please add them onto Slido :)
This document provides a summary of Gururaj H. R.'s professional experience and qualifications. He has over 10 years of experience in data analysis, working for companies like Ariba Technologies, Symphony Marketing Solutions, and Bells Softech Limited. His experience includes tasks like data collection, analysis, report generation, and troubleshooting. He has an MBA in supply chain management and a diploma in mechanical engineering. His skills include MS Office, basic SQL, and he has experience working with cloud applications.
How to Create an Effective Enterprise AI Strategy.pdfAivada
In today’s fast-paced and technologically driven market, establishing a robust Enterprise AI Strategy is essential for any organization aiming to maintain competitiveness and foster innovation. This comprehensive guide explains the concept of an enterprise AI strategy, elucidates the myriad benefits it delivers, and provides a detailed framework on how to meticulously craft one for your organization.
Now more than ever, organizations must capture what’s happening in the business and transform their data into faster, smarter decisions. In this deck, you’ll learn how Deloitte uses Workday Prism Analytics to harness financial, workforce, and operational data by unlocking key analytics at the most critical times.
View related videos:
Welcome to the New World of Analytics.
https://www.youtube.com/watch?v=DLOekjChar0
Build Belonging and Diversity | Insights https://www.youtube.com/watch?v=slhpTY5z68c
Nadine Schöne, Dataiku. The Complete Data Value Chain in a NutshellIT Arena
Dr. Nadine Schöne is a Senior Solutions Architect at Dataiku in Berlin. In this role, she deals with all aspects of the data value chain for all users – including integration of data sources, ETL, cooperation, statistics, modelling, but also operationalization, monitoring, automatization and security during production. She regularly talks at conferences, holds webinars and writes articles.
Speech Overview:
How can you get the most out of your data – while staying flexible in your choice of infrastructure and without having to integrate a multitude of tools for the different personas involved? Maximizing the value you get out of your data is a necessity today. Looking at the whole picture as well as careful planning are the key for success. We will have a look at the complete data value chain from end to end: from the data stores, collaboration features, data preparation, visualization and automation capabilities, and external compute to scheduling, operationalization, monitoring and security.
Artificial Intelligence, Predictive Analysis, Blockchain, and Machine Learning platforms can be easily
Ingested to our Analytics to provide you with the best solution according to your business needs
Similar to Demand Forecasting Case Study ppt - pdf (20)
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
Build applications with generative AI on Google CloudMárton Kodok
We will explore Vertex AI - Model Garden powered experiences, we are going to learn more about the integration of these generative AI APIs. We are going to see in action what the Gemini family of generative models are for developers to build and deploy AI-driven applications. Vertex AI includes a suite of foundation models, these are referred to as the PaLM and Gemini family of generative ai models, and they come in different versions. We are going to cover how to use via API to: - execute prompts in text and chat - cover multimodal use cases with image prompts. - finetune and distill to improve knowledge domains - run function calls with foundation models to optimize them for specific tasks. At the end of the session, developers will understand how to innovate with generative AI and develop apps using the generative ai industry trends.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
Orchestrating the Future: Navigating Today's Data Workflow Challenges with Ai...Kaxil Naik
Navigating today's data landscape isn't just about managing workflows; it's about strategically propelling your business forward. Apache Airflow has stood out as the benchmark in this arena, driving data orchestration forward since its early days. As we dive into the complexities of our current data-rich environment, where the sheer volume of information and its timely, accurate processing are crucial for AI and ML applications, the role of Airflow has never been more critical.
In my journey as the Senior Engineering Director and a pivotal member of Apache Airflow's Project Management Committee (PMC), I've witnessed Airflow transform data handling, making agility and insight the norm in an ever-evolving digital space. At Astronomer, our collaboration with leading AI & ML teams worldwide has not only tested but also proven Airflow's mettle in delivering data reliably and efficiently—data that now powers not just insights but core business functions.
This session is a deep dive into the essence of Airflow's success. We'll trace its evolution from a budding project to the backbone of data orchestration it is today, constantly adapting to meet the next wave of data challenges, including those brought on by Generative AI. It's this forward-thinking adaptability that keeps Airflow at the forefront of innovation, ready for whatever comes next.
The ever-growing demands of AI and ML applications have ushered in an era where sophisticated data management isn't a luxury—it's a necessity. Airflow's innate flexibility and scalability are what makes it indispensable in managing the intricate workflows of today, especially those involving Large Language Models (LLMs).
This talk isn't just a rundown of Airflow's features; it's about harnessing these capabilities to turn your data workflows into a strategic asset. Together, we'll explore how Airflow remains at the cutting edge of data orchestration, ensuring your organization is not just keeping pace but setting the pace in a data-driven future.
Session in https://budapestdata.hu/2024/04/kaxil-naik-astronomer-io/ | https://dataml24.sessionize.com/session/667627
2. Demand Forecasting
Customer Detail
Problem Statement
Challenges
Industry - Chemicals (Fragrances and Flavours)
Location - India
The supply chain team was using traditional
methods of forecasting. They were generating a
huge error in their forecasting and production
planning and wanted a robust forecasting solution.
1) High number of SKUs.
2) Some external factors weren’t captured by the
team.
4. Solution
Business Impact
● Existing data from SAP and Tally of past 10 years of sales were
taken
● Data engineering, data cleaning, & missing data removal
● Identification of internal and external factors affecting sales
● Designing of Deep Learning model for forecasting using
combination of LSTM and other algorithms
● Creation and connection of dashboard with SAP to get
forecasting in real-time
The forecasting solution was able to forecast the peak and
downfalls for a duration of next 8 months accurately. The error rate
reduced by 10% from the traditional methods.
5. Our Methodology
Understanding
Requirements
Designing Solutions
Deployment
We understand your
business goals, and get
acquainted with all the
necessary details.
Our team of experts,
starts building solutions
to deliver your business goals.
Once complete
solutions is ready
then we deploy at
your core system.
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2
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6. Thank You
Chirag Tank
Co-Founder & CEO
Jitendra Purohit
Co-Founder & COO
+91 9820080751
+91 7021980537
chirag.tank@datenwissen.com
jitendra.purohit@datenwissen.com