Join CCG and Microsoft for a hands-on demonstration of Azure’s machine learning capabilities. During the workshop, we will:
- Hold a Machine Learning 101 session to explain what machine learning is and how it fits in the analytics landscape
- Demonstrate Azure Databricks’ capabilities for building custom machine learning models
- Take a tour of the Azure Machine Learning’s capabilities for MLOps, Automated Machine Learning, and code-free Machine Learning
By the end of the workshop, you’ll have the tools you need to begin your own journey to AI.
Learn to Use Databricks for Data ScienceDatabricks
Data scientists face numerous challenges throughout the data science workflow that hinder productivity. As organizations continue to become more data-driven, a collaborative environment is more critical than ever — one that provides easier access and visibility into the data, reports and dashboards built against the data, reproducibility, and insights uncovered within the data.. Join us to hear how Databricks’ open and collaborative platform simplifies data science by enabling you to run all types of analytics workloads, from data preparation to exploratory analysis and predictive analytics, at scale — all on one unified platform.
The first step towards understanding data assets’ impact on your organization is understanding what those assets mean for each other. Metadata – literally, data about data – is a practice area required by good systems development, and yet is also perhaps the most mislabeled and misunderstood Data Management practice. Understanding metadata and its associated technologies as more than just straightforward technological tools can provide powerful insight into the efficiency of organizational practices and enable you to combine practices into sophisticated techniques supporting larger and more complex business initiatives. Program learning objectives include:
- Understanding how to leverage metadata practices in support of business strategy
- Discuss foundational metadata concepts
- Guiding principles for and lessons previously learned from metadata and its practical uses applied strategy
Metadata strategies include:
- Metadata is a gerund so don’t try to treat it as a noun
- Metadata is the language of Data Governance
- Treat glossaries/repositories as capabilities, not technology
Uncover how your business can save money and find new revenue streams.
Driving profitability is a top priority for companies globally, especially in uncertain economic times. It's imperative that companies reimagine growth strategies and improve process efficiencies to help cut costs and drive revenue – but how?
By leveraging data-driven strategies layered with artificial intelligence, companies can achieve untapped potential and help their businesses save money and drive profitability.
In this webinar, you'll learn:
- How your company can leverage data and AI to reduce spending and costs
- Ways you can monetize data and AI and uncover new growth strategies
- How different companies have implemented these strategies to achieve cost optimization benefits
Modern Data Challenges require Modern Graph TechnologyNeo4j
This session focuses on key data trends and challenges impacting enterprises. And, how graph technology is evolving to future-proof data strategy and architectures.
When it comes to creating an enterprise AI strategy: if your company isn’t good at analytics, it’s not ready for AI. Succeeding in AI requires being good at data engineering AND analytics. Unfortunately, management teams often assume they can leapfrog best practices for basic data analytics by directly adopting advanced technologies such as ML/AI – setting themselves up for failure from the get-go. This presentation explains how to get basic data engineering and the right technology in place to create and maintain data pipelines so that you can solve problems with AI successfully.
Solution architecture for big data projects
solution architecture,big data,hadoop,hive,hbase,impala,spark,apache,cassandra,SAP HANA,Cognos big insights
Five Things to Consider About Data Mesh and Data GovernanceDATAVERSITY
Data mesh was among the most discussed and controversial enterprise data management topics of 2021. One of the reasons people struggle with data mesh concepts is we still have a lot of open questions that we are not thinking about:
Are you thinking beyond analytics? Are you thinking about all possible stakeholders? Are you thinking about how to be agile? Are you thinking about standardization and policies? Are you thinking about organizational structures and roles?
Join data.world VP of Product Tim Gasper and Principal Scientist Juan Sequeda for an honest, no-bs discussion about data mesh and its role in data governance.
Building a Data Strategy – Practical Steps for Aligning with Business GoalsDATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task – but it’s worth the effort. Getting your Data Strategy right can provide significant value, as data drives many of the key initiatives in today’s marketplace – from digital transformation, to marketing, to customer centricity, to population health, and more. This webinar will help demystify Data Strategy and its relationship to Data Architecture and will provide concrete, practical ways to get started.
Learn to Use Databricks for Data ScienceDatabricks
Data scientists face numerous challenges throughout the data science workflow that hinder productivity. As organizations continue to become more data-driven, a collaborative environment is more critical than ever — one that provides easier access and visibility into the data, reports and dashboards built against the data, reproducibility, and insights uncovered within the data.. Join us to hear how Databricks’ open and collaborative platform simplifies data science by enabling you to run all types of analytics workloads, from data preparation to exploratory analysis and predictive analytics, at scale — all on one unified platform.
The first step towards understanding data assets’ impact on your organization is understanding what those assets mean for each other. Metadata – literally, data about data – is a practice area required by good systems development, and yet is also perhaps the most mislabeled and misunderstood Data Management practice. Understanding metadata and its associated technologies as more than just straightforward technological tools can provide powerful insight into the efficiency of organizational practices and enable you to combine practices into sophisticated techniques supporting larger and more complex business initiatives. Program learning objectives include:
- Understanding how to leverage metadata practices in support of business strategy
- Discuss foundational metadata concepts
- Guiding principles for and lessons previously learned from metadata and its practical uses applied strategy
Metadata strategies include:
- Metadata is a gerund so don’t try to treat it as a noun
- Metadata is the language of Data Governance
- Treat glossaries/repositories as capabilities, not technology
Uncover how your business can save money and find new revenue streams.
Driving profitability is a top priority for companies globally, especially in uncertain economic times. It's imperative that companies reimagine growth strategies and improve process efficiencies to help cut costs and drive revenue – but how?
By leveraging data-driven strategies layered with artificial intelligence, companies can achieve untapped potential and help their businesses save money and drive profitability.
In this webinar, you'll learn:
- How your company can leverage data and AI to reduce spending and costs
- Ways you can monetize data and AI and uncover new growth strategies
- How different companies have implemented these strategies to achieve cost optimization benefits
Modern Data Challenges require Modern Graph TechnologyNeo4j
This session focuses on key data trends and challenges impacting enterprises. And, how graph technology is evolving to future-proof data strategy and architectures.
When it comes to creating an enterprise AI strategy: if your company isn’t good at analytics, it’s not ready for AI. Succeeding in AI requires being good at data engineering AND analytics. Unfortunately, management teams often assume they can leapfrog best practices for basic data analytics by directly adopting advanced technologies such as ML/AI – setting themselves up for failure from the get-go. This presentation explains how to get basic data engineering and the right technology in place to create and maintain data pipelines so that you can solve problems with AI successfully.
Solution architecture for big data projects
solution architecture,big data,hadoop,hive,hbase,impala,spark,apache,cassandra,SAP HANA,Cognos big insights
Five Things to Consider About Data Mesh and Data GovernanceDATAVERSITY
Data mesh was among the most discussed and controversial enterprise data management topics of 2021. One of the reasons people struggle with data mesh concepts is we still have a lot of open questions that we are not thinking about:
Are you thinking beyond analytics? Are you thinking about all possible stakeholders? Are you thinking about how to be agile? Are you thinking about standardization and policies? Are you thinking about organizational structures and roles?
Join data.world VP of Product Tim Gasper and Principal Scientist Juan Sequeda for an honest, no-bs discussion about data mesh and its role in data governance.
Building a Data Strategy – Practical Steps for Aligning with Business GoalsDATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task – but it’s worth the effort. Getting your Data Strategy right can provide significant value, as data drives many of the key initiatives in today’s marketplace – from digital transformation, to marketing, to customer centricity, to population health, and more. This webinar will help demystify Data Strategy and its relationship to Data Architecture and will provide concrete, practical ways to get started.
[Note: This is a partial preview. To download this presentation, visit:
https://www.oeconsulting.com.sg/training-presentations]
This presentation is a collection of PowerPoint diagrams and templates used to convey 20 different digital transformation frameworks and models.
INCLUDED FRAMEWORKS/MODELS:
1. Ten Guiding Principles of Digital Transformation
2. The BCG Strategy Palette
3. Digital Value Chain Model
4. Four Levels of Digital Maturity
5. Customer Experience Matrix
6. Design Thinking Framework
7. Business Model Canvas
8. Customer Journey Map
9. OECD Digital Government Transformation Framework
10. Accenture's Nonstop Customer Experience Model
11. MIT's Digital Transformation Framework
12. McKinsey's Digital Transformation Framework
13. Capgemini's Digital Transformation Framework
14. DXC Technology's Digital Transformation Framework
15. Gartner's Digital Transformation Framework
16. Cognizant's Digital Transformation Framework
17. PwC's Digital Transformation Framework
18. Ionolgy's Digital Transformation Framework
19. Accenture's Digital Business Strategy Framework
20. Deloitte's Digital Industrial Transformation Framework
Data Modeling, Data Governance, & Data QualityDATAVERSITY
Data Governance is often referred to as the people, processes, and policies around data and information, and these aspects are critical to the success of any data governance implementation. But just as critical is the technical infrastructure that supports the diverse data environments that run the business. Data models can be the critical link between business definitions and rules and the technical data systems that support them. Without the valuable metadata these models provide, data governance often lacks the “teeth” to be applied in operational and reporting systems.
Join Donna Burbank and her guest, Nigel Turner, as they discuss how data models & metadata-driven data governance can be applied in your organization in order to achieve improved data quality.
Best Practices for Streaming IoT Data with MQTT and Apache Kafka®confluent
Watch this talk here: https://www.confluent.io/online-talks/best-practices-for-streaming-iot-data-with-MQTT-and-apache-kafka-on-demand
Organizations today are looking to stream IoT data to Apache Kafka. However, connecting tens of thousands or even millions of devices over unreliable networks can create some architecture challenges.
In this session, we will identify and demo some best practices for implementing a large scale IoT system that can stream MQTT messages to Apache Kafka.
The Top 12 Virtual Networking Tips To Boost Your CareerBernard Marr
Crafting a digital reputation and building an online network takes time, but anyone can do it – and these days, it’s more important than ever. Here are some tips for virtual networking in a post-pandemic world.
Want to see a high-level overview of the products in the Microsoft data platform portfolio in Azure? I’ll cover products in the categories of OLTP, OLAP, data warehouse, storage, data transport, data prep, data lake, IaaS, PaaS, SMP/MPP, NoSQL, Hadoop, open source, reporting, machine learning, and AI. It’s a lot to digest but I’ll categorize the products and discuss their use cases to help you narrow down the best products for the solution you want to build.
DAS Slides: Building a Data Strategy - Practical Steps for Aligning with Busi...DATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task. The opportunity in getting it right can be significant, however, as data drives many of the key initiatives in today’s marketplace: digital transformation, marketing, customer centricity, and more. This webinar will help de-mystify Data Strategy and Data Architecture and will provide concrete, practical ways to get started.
Data Catalog for Better Data Discovery and GovernanceDenodo
Watch full webinar here: https://buff.ly/2Vq9FR0
Data catalogs are en vogue answering critical data governance questions like “Where all does my data reside?” “What other entities are associated with my data?” “What are the definitions of the data fields?” and “Who accesses the data?” Data catalogs maintain the necessary business metadata to answer these questions and many more. But that’s not enough. For it to be useful, data catalogs need to deliver these answers to the business users right within the applications they use.
In this session, you will learn:
*How data catalogs enable enterprise-wide data governance regimes
*What key capability requirements should you expect in data catalogs
*How data virtualization combines dynamic data catalogs with delivery
The Data Trifecta – Privacy, Security & Governance Race from Reactivity to Re...DATAVERSITY
Change is hard, especially in response to negative stimuli or what is perceived as negative stimuli. So organizations need to reframe how they think about data privacy, security and governance, treating them as value centers to 1) ensure enterprise data can flow where it needs to, 2) prevent – not just react – to internal and external threats, and 3) comply with data privacy and security regulations.
Working together, these roles can accelerate faster access to approved, relevant and higher quality data – and that means more successful use cases, faster speed to insights, and better business outcomes. However, both new information and tools are required to make the shift from defense to offense, reducing data drama while increasing its value.
Join us for this panel discussion with experts in these fields as they discuss:
- Recent research about where data privacy, security and governance stand
- The most valuable enterprise data use cases
- The common obstacles to data value creation
- New approaches to data privacy, security and governance
- Their advice on how to shift from a reactive to resilient mindset/culture/organization
You’ll be educated, entertained and inspired by this panel and their expertise in using the data trifecta to innovate more often, operate more efficiently, and differentiate more strategically.
Learn the current state of the NoSQL landscape and discover the different data models within it. From document stores and key value databases to graph and Wide Column. Then you’ll learn why wide column databases are the most appropriate for scalable high performance use cases, including capabilities for massive scale-out architecture, peer-to-peer clustering to avoid bottlenecking and built-in multi-datacenter replication.
Elevating customer analytics - how to gain a 720 degree view of your customerActian Corporation
big data creates significant opportunities for marketers. Using big data analytics tools, marketers can improve decision making, deliver better value for their marketing spend, create truly personalized customer experiences, and understand their audience at the level of each individual consumer.
Introduction to Machine Learning with Azure & DatabricksCCG
Join CCG and Microsoft for a hands-on demonstration of Azure’s machine learning capabilities. During the workshop, we will:
- Hold a Machine Learning 101 session to explain what machine learning is and how it fits in the analytics landscape
- Demonstrate Azure Databricks’ capabilities for building custom machine learning models
- Take a tour of the Azure Machine Learning’s capabilities for MLOps, Automated Machine Learning, and code-free Machine Learning
By the end of the workshop, you’ll have the tools you need to begin your own journey to AI.
[Note: This is a partial preview. To download this presentation, visit:
https://www.oeconsulting.com.sg/training-presentations]
This presentation is a collection of PowerPoint diagrams and templates used to convey 20 different digital transformation frameworks and models.
INCLUDED FRAMEWORKS/MODELS:
1. Ten Guiding Principles of Digital Transformation
2. The BCG Strategy Palette
3. Digital Value Chain Model
4. Four Levels of Digital Maturity
5. Customer Experience Matrix
6. Design Thinking Framework
7. Business Model Canvas
8. Customer Journey Map
9. OECD Digital Government Transformation Framework
10. Accenture's Nonstop Customer Experience Model
11. MIT's Digital Transformation Framework
12. McKinsey's Digital Transformation Framework
13. Capgemini's Digital Transformation Framework
14. DXC Technology's Digital Transformation Framework
15. Gartner's Digital Transformation Framework
16. Cognizant's Digital Transformation Framework
17. PwC's Digital Transformation Framework
18. Ionolgy's Digital Transformation Framework
19. Accenture's Digital Business Strategy Framework
20. Deloitte's Digital Industrial Transformation Framework
Data Modeling, Data Governance, & Data QualityDATAVERSITY
Data Governance is often referred to as the people, processes, and policies around data and information, and these aspects are critical to the success of any data governance implementation. But just as critical is the technical infrastructure that supports the diverse data environments that run the business. Data models can be the critical link between business definitions and rules and the technical data systems that support them. Without the valuable metadata these models provide, data governance often lacks the “teeth” to be applied in operational and reporting systems.
Join Donna Burbank and her guest, Nigel Turner, as they discuss how data models & metadata-driven data governance can be applied in your organization in order to achieve improved data quality.
Best Practices for Streaming IoT Data with MQTT and Apache Kafka®confluent
Watch this talk here: https://www.confluent.io/online-talks/best-practices-for-streaming-iot-data-with-MQTT-and-apache-kafka-on-demand
Organizations today are looking to stream IoT data to Apache Kafka. However, connecting tens of thousands or even millions of devices over unreliable networks can create some architecture challenges.
In this session, we will identify and demo some best practices for implementing a large scale IoT system that can stream MQTT messages to Apache Kafka.
The Top 12 Virtual Networking Tips To Boost Your CareerBernard Marr
Crafting a digital reputation and building an online network takes time, but anyone can do it – and these days, it’s more important than ever. Here are some tips for virtual networking in a post-pandemic world.
Want to see a high-level overview of the products in the Microsoft data platform portfolio in Azure? I’ll cover products in the categories of OLTP, OLAP, data warehouse, storage, data transport, data prep, data lake, IaaS, PaaS, SMP/MPP, NoSQL, Hadoop, open source, reporting, machine learning, and AI. It’s a lot to digest but I’ll categorize the products and discuss their use cases to help you narrow down the best products for the solution you want to build.
DAS Slides: Building a Data Strategy - Practical Steps for Aligning with Busi...DATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task. The opportunity in getting it right can be significant, however, as data drives many of the key initiatives in today’s marketplace: digital transformation, marketing, customer centricity, and more. This webinar will help de-mystify Data Strategy and Data Architecture and will provide concrete, practical ways to get started.
Data Catalog for Better Data Discovery and GovernanceDenodo
Watch full webinar here: https://buff.ly/2Vq9FR0
Data catalogs are en vogue answering critical data governance questions like “Where all does my data reside?” “What other entities are associated with my data?” “What are the definitions of the data fields?” and “Who accesses the data?” Data catalogs maintain the necessary business metadata to answer these questions and many more. But that’s not enough. For it to be useful, data catalogs need to deliver these answers to the business users right within the applications they use.
In this session, you will learn:
*How data catalogs enable enterprise-wide data governance regimes
*What key capability requirements should you expect in data catalogs
*How data virtualization combines dynamic data catalogs with delivery
The Data Trifecta – Privacy, Security & Governance Race from Reactivity to Re...DATAVERSITY
Change is hard, especially in response to negative stimuli or what is perceived as negative stimuli. So organizations need to reframe how they think about data privacy, security and governance, treating them as value centers to 1) ensure enterprise data can flow where it needs to, 2) prevent – not just react – to internal and external threats, and 3) comply with data privacy and security regulations.
Working together, these roles can accelerate faster access to approved, relevant and higher quality data – and that means more successful use cases, faster speed to insights, and better business outcomes. However, both new information and tools are required to make the shift from defense to offense, reducing data drama while increasing its value.
Join us for this panel discussion with experts in these fields as they discuss:
- Recent research about where data privacy, security and governance stand
- The most valuable enterprise data use cases
- The common obstacles to data value creation
- New approaches to data privacy, security and governance
- Their advice on how to shift from a reactive to resilient mindset/culture/organization
You’ll be educated, entertained and inspired by this panel and their expertise in using the data trifecta to innovate more often, operate more efficiently, and differentiate more strategically.
Learn the current state of the NoSQL landscape and discover the different data models within it. From document stores and key value databases to graph and Wide Column. Then you’ll learn why wide column databases are the most appropriate for scalable high performance use cases, including capabilities for massive scale-out architecture, peer-to-peer clustering to avoid bottlenecking and built-in multi-datacenter replication.
Elevating customer analytics - how to gain a 720 degree view of your customerActian Corporation
big data creates significant opportunities for marketers. Using big data analytics tools, marketers can improve decision making, deliver better value for their marketing spend, create truly personalized customer experiences, and understand their audience at the level of each individual consumer.
Introduction to Machine Learning with Azure & DatabricksCCG
Join CCG and Microsoft for a hands-on demonstration of Azure’s machine learning capabilities. During the workshop, we will:
- Hold a Machine Learning 101 session to explain what machine learning is and how it fits in the analytics landscape
- Demonstrate Azure Databricks’ capabilities for building custom machine learning models
- Take a tour of the Azure Machine Learning’s capabilities for MLOps, Automated Machine Learning, and code-free Machine Learning
By the end of the workshop, you’ll have the tools you need to begin your own journey to AI.
BA is used to gain insights that inform business decisions and can be used to automate and optimize business processes. Data-driven companies treat their data as a corporate asset and leverage it for a competitive advantage. Successful business analytics depends on data quality, skilled analysts who understand the technologies and the business, and an organizational commitment to data-driven decision-making.
Business analytics examples
Business analytics techniques break down into two main areas. The first is basic business intelligence. This involves examining historical data to get a sense of how a business department, team or staff member performed over a particular time. This is a mature practice that most enterprises are fairly accomplished at using.
Better Living Through Analytics - Strategies for Data DecisionsProduct School
Data is king! Get ready to understand how a successful analytics team can empower managers from product, marketing, and other areas to make effective, data-driven decisions.
Louis Cialdella, a data scientist at ZipRecruiter, shared some case studies and successful strategies that he has used at ZipRecruiter as well as previous experiences. The purpose of this data talk was to enlighten people on how to make sure that analysts can successfully partner with other departments and get them the information they need to do great things.
Data Science has become one of the most demanded jobs of the 21st century. It has become a buzzword that almost everyone talks about these days. But what is Data Science? In this article, we will demystify Data Science, the role of a Data Scientist and have a look at the tools required to master Data Science.
Fantastic Problems and Where to Find Them: Daryl WeirFuturice
Machine learning and big data have been buzz words for years now, but how do you know you have a machine learning problem on your hands? These slides, taken from a Futurice Beer & Tech talk, describe the types of problems ML methods are well suited to solve, with examples from a wide variety of industries. The deck also tells you where to get started if you want to try solving one of these problems yourself.
Machine Learning for Business - Eight Best Practices for Getting StartedBhupesh Chaurasia
Though the term machine learning has become very visible in
the popular press over the past few years—making it appear to be the newest shiny object—the technology has actually been
in use for decades. In fact, machine learning algorithms such as decision trees are already in use by many organizations for predictive analytics.
Analytics in a Day Ft. Synapse Virtual WorkshopCCG
Say goodbye to data silos! Analytics in a Day will simplify and accelerate your journey towards the modern data warehouse. Join CCG and Microsoft for a half-day virtual workshop, hosted by James McAuliffe.
Join CCG for our Data Governance (DG) Workshop where CCG will introduce their Data Governance methodology and framework that enables organizations to assess DG faster, deriving actionable insights that can be quickly implemented with minimal disruption. CCG will also discuss how Microsoft Azure Solutions can be leveraged to build a strong foundation for governed data insights.
How to Monetize Your Data Assets and Gain a Competitive AdvantageCCG
Join us for this session where Doug Laney will share insights from his best-selling book, Infonomics, about how organizations can actually treat information as an enterprise asset.
Analytics in a Day Ft. Synapse Virtual WorkshopCCG
Say goodbye to data silos! Analytics in a Day will simplify and accelerate your journey towards the modern data warehouse. Join CCG and Microsoft for a half-day virtual workshop, hosted by James McAuliffe.
Analytics in a Day Ft. Synapse Virtual WorkshopCCG
Say goodbye to data silos! Analytics in a Day will simplify and accelerate your journey towards the modern data warehouse. Join CCG and Microsoft for a half-day virtual workshop, hosted by James McAuliffe.
You had a strategy. You were executing it. You were then side-swiped by COVID, spending countless cycles blocking and tackling. It is now time to step back onto your path.
CCG is holding a workshop to help you update your roadmap and get your team back on track and review how Microsoft Azure Solutions can be leveraged to build a strong foundation for governed data insights.
Analytics in a Day Ft. Synapse Virtual WorkshopCCG
Say goodbye to data silos! Analytics in a Day will simplify and accelerate your journey towards the modern data warehouse. Join CCG and Microsoft for a half-day virtual workshop, hosted by James McAuliffe.
Power BI Advanced Data Modeling Virtual WorkshopCCG
Join CCG and Microsoft for a virtual workshop, hosted by Solution Architect, Doug McClurg, to learn how to create professional, frustration-free data models that engage your customers.
Join Brian Beesley, Director of Data Science, for an executive-level tour of AI capabilities. Get an inside peek at how others have used AI, and learn how you can harness the power of AI to transform your business.
Say goodbye to data silos! Analytics in a Day will simplify and accelerate your journey towards the modern data warehouse. Join CCG and Microsoft for a two-day virtual workshop, hosted by James McAuliffe.
Virtual Governance in a Time of Crisis WorkshopCCG
The CCGDG framework is focused on the following 5 key competencies. These 5 competencies were identified as areas within DG that have the biggest ROI for you, our customer. The pandemic has uncovered many challenges related to governance, therefore the backbone of this model is the emphasis on risk mitigation.
1. Program Management
2. Data Quality
3. Data Architecture
4. Metadata Management
5. Privacy
Advance Data Visualization and Storytelling Virtual WorkshopCCG
Join CCG and Microsoft for a virtual workshop, hosted by Senior BI Architect, Martin Rivera, taking you through a journey of advanced data visualization and storytelling.
In early 2019, Microsoft created the AZ-900 Microsoft Azure Fundamentals certification. This is a certification for all individuals, IT or non IT background, who want to further their careers and learn how to navigate the Azure cloud platform.
Learn about AZ-900 exam concepts and how to prepare and pass the exam
In early 2019, Microsoft created the AZ-900 Microsoft Azure Fundamentals certification. This is a certification for all individuals, IT or non IT background, who want to further their careers and learn how to navigate the Azure cloud platform.
Learn about AZ-900 exam concepts and how to prepare and pass the exam
In early 2019, Microsoft created the AZ-900 Microsoft Azure Fundamentals certification. This is a certification for all individuals, IT or non IT background, who want to further their careers and learn how to navigate the Azure cloud platform.
Learn about AZ-900 exam concepts and how to prepare and pass the exam
Business intelligence dashboards and data visualizations serve as a launching point for better business decision making. Learn how you can leverage Power BI to easily build reports and dashboards with interactive visualizations.
Data Governance and MDM | Profisse, Microsoft, and CCGCCG
CCG will introduce a methodology and framework for DG that allows organizations to assess DG faster, deriving actionable insights that can be quickly implemented with minimal disruption. CCG will also review how Microsoft Azure Solutions can be leveraged to build a strong foundation for governed data insights. In addition, Profisee will introduce a popular component of data governance, MDM.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
3. An introduction to Machine Learning and its uses in business
Machine Learning 101
4. Why should anyone care about Machine Learning?
What is Machine Learning?
How does Machine Learning work?
Ok, but how does it really work?
How can an organization use Machine Learning?
Agenda
5. The concepts in Machine Learning are not new.
https://www.quantinsti.com/blog/machine-learning-basics
another human.
four
6. Though the concepts have been around, Machine Learning has just started
getting buzz in recent years because the barriers to entry are much lower.
Flood of data and decreasing costs of storage
Increasing computational power
Increased attention from researchers
Growth of open source technologies
Support from industries
8. Sales/Marketing
• Price Optimization
• Inventory Forecasting
• Customer Segmentation
• Cross Sell / Upsell /
Recommendation Engines
• Customer Churn Predictions
• Customer Lifetime Value
Finance
• Asset Pricing
• Risk Analysis
• Fraud detection
• Market Forecasting
• Anti Money Laundering
Operations
• Inventory Forecasting
• Robotics
• Automated Workflows
• Predictive Maintenance
• Schedule Optimization
• IoT Production Line
Monitoring
Service
• Single View of Customer
• Customer Service analysis
• Chat Bots / Digital Assistants
• Social Media Analysis
• Lead Scoring
Machine Learning doesn’t just have to be the realm of high tech.
There are practical ways to incorporate it across the business.
10. Why should anyone care about Machine Learning?
What is Machine Learning?
How does Machine Learning work?
Ok, but how does it really work?
How can an organization use Machine Learning?
Agenda
11. Machine Learning is a discipline that supports the data science process.
It is a technique, and its value is in the outputs it drives.
Discipline Process Decision Actions
Data Science
A broad process for generating insights that may
involve data ingestion from one or many sources
(including external data, streaming data, or big
data), data processing and cleansing, model
generation using either statistical or machine
learning approaches, model selection, model
deployment and maintenance, and visualization
of data.
Advanced Analytics
Apply data science to predictive (what
will happen?) or prescriptive (what
should we do?) business use cases.
Artificial Intelligence /
Cognitive Computing
Apply data science to approximate
human intuition and decision making
(e.g. strategy, creativity, planning) or
human sensory function s (e.g.
computer vision, natural language
understanding, etc.)
Statistics
A branch of math for generating descriptions
or inferences about a population, often based
on samples of the population. Inferences may
take the form of “models,” which are
equations that approximate the data’s
inherent relationships.
Machine Learning
Combines computer science with math
concepts to generate models by rapidly
iterating on large datasets.
Other Analytics Disciplines
High Performance Computing, Data
Engineering, Visualization, etc.
Automation /
Robotics /
Intelligent Devices
Strategy / Operations
12. Advanced Analytics can enable predictive and prescriptive uses of data.
Traditional
analytics focus on
understanding and
explaining the data
that has been
collected.
Advanced Analytics
focus on generating
new data in the
form of predictions
or decisions, and
going the extra step
to automate
decision-making
when possible.
13. Simply put, machine learning is the science of making best guesses by
iterative trial and error.
101010101010101010101010101010101010
010101010101010101010101010101010101
14. Why should anyone care about Machine Learning?
What is Machine Learning?
How does Machine Learning work?
Ok, but how does it really work?
How can an organization use Machine Learning?
Agenda
15. Machine Learning works by using “algorithms” to generate “models”.
A model is a repeatable, data-driven approach to making a best guess.
It does this by formalizing mathematical relationships between data in the form of either:
– Rules (e.g. predict applicants will default on a loan if Credit Score < 700 and Debt to Income Ratio > 30%)
– Or an equation (e.g. predict Home Price = 100*Square Footage + 2*Average Income in the Area)
Note that this is different from other types of models, like operating models or data models
Statistical Model Data ModelOperating Model
People
Process Technology
Data
Guide
Support
Enable
16. What’s a model?
Data
Prior month sales: $4MM
2 months prior: $3MM
3 months prior: $2MM
Program / Model
This month sales =
(prior month +
2 months prior +
3 months prior)
/ 3
Answer
This month’s sales = $3MM?
In the past we’ve told computers how to use data to answer our questions.
17. Answer
Last month’s sales: $2MM
Data
Prior month sales: $4MM
2 months prior: $3MM
3 months prior: $1MM
Answer
Last month’s sales: $2MM
Data
Prior month sales: $4MM
2 months prior: $3MM
3 months prior: $1MM
Answer
Last month’s sales: $2MM
Data
Prior month sales: $4MM
2 months prior: $3MM
3 months prior: $1MM
Answer
Last month’s sales: $2MM
Data
Prior month sales: $4MM
2 months prior: $3MM
3 months prior: $1MM
But we’ve found that if we give the machine historic facts, we can let it
find the right program / model to plug in for future answers.
Answer
Last month’s sales: $2MM
Data
Prior month sales: $4MM
2 months prior: $3MM
3 months prior: $2MM
Program / Model
This month’s sales =
1/8 * Prior month +
1/3 * 2 months prior +
1/4 * 3 months prior
What’s a model?
18. Answer
Last month’s sales: $2MM
Data
Prior month sales: $4MM
2 months prior: $3MM
3 months prior: $1MM
Answer
Last month’s sales: $2MM
Data
Prior month sales: $4MM
2 months prior: $3MM
3 months prior: $1MM
Answer
Last month’s sales: $2MM
Data
Prior month sales: $4MM
2 months prior: $3MM
3 months prior: $1MM
Answer
Last month’s sales: $2MM
Data
Prior month sales: $4MM
2 months prior: $3MM
3 months prior: $1MM
Once we have our machine-defined program, we can use it
with new data to make better predictions.
Answer
Last month’s sales: $2MM
Data
Prior month sales: $4MM
2 months prior: $3MM
3 months prior: $2MM
Program / Model
This month’s sales =
1/8 * Prior month +
1/3 * 2 months prior +
1/4 * 3 months prior
New Data
Prior month sales: $8MM
2 months prior: $6MM
3 months prior: $8MM
Answer
This month’s sales = $5MM
What’s a model?
19. What is an algorithm?
The word algorithm gets used a lot, but it isn’t always defined.
A defined set of steps for solving a problem
Often involves repeating steps
In Machine Learning, it may or may not have an ending condition. Some common ending conditions are:
– The problem is solved to our satisfaction
• For example – stop when the last 4 iterations have been 95% accurate or better
– The problem hasn’t been solved but we don’t seem to be getting any closer to solving it
• For example – stop if the last 10 iterations have not seen any improvement in accuracy
– The process has run for a long time
• For example – stop after the program has run for 12 hours, regardless of whether progress is still being made
20. Collect the data and randomly create initial decision rules.
Design a method for measurably evaluating how good or bad your hypothesis is.
Update your hypothesis in a way that marginally improves the performance of your decision rules.
Continue this process until either you are satisfied with the results, or your hypothesis can’t improve
anymore with the data available.
What is an algorithm?
Create a
hypothesis
Evaluate the
hypothesis
Adjust the
hypothesis
Repeat until
convergence
Almost all machine learning algorithms follow the same general pattern.
21. Why should anyone care about Machine Learning?
What is Machine Learning?
How does Machine Learning work?
Ok, but how does it really work?
How can an organization use Machine Learning?
Agenda
22. Supervised Learning Unsupervised Learning
We know the “right answers” for some of the scenarios.
– We may have history we can look back on
– We may be hoping to replicate human decision making
There aren’t necessarily “right answers,” we just
want to get a better understanding of our data.
There are two main families of algorithms to choose from.
Image credit: Gowthamy Vaseekaran via Medium.com, available at https://medium.com/@gowthamy/machine-learning-supervised-learning-vs-unsupervised-learning-f1658e12a780
Predict our profits next quarter.
Identify the number written on a check.
Predict a user’s rating for a given product.
Group our customers into segments.
Find the most important variables in a dataset.
Identify credit card transactions that are out of the ordinary.
23. Now let’s walk through two of the most popular machine learning approaches
and discuss how the algorithms are applied.
Classification Clustering
24. Everyone will repay their loan.
Create a
hypothesis
20 outstanding loans
Use classification when you want to guess a non-numeric value, like a yes/no answer. We
will take a decision tree approach.
25. Calculate accuracy as the % of predictions that are correct based on your current set of rules.
Evaluate the
hypothesis
20 outstanding loans
12 repaid, 8 defaulted
Accuracy = 12/20 = 60%
Use classification when you want to guess a non-numeric value, like a yes/no answer. We
will take a decision tree approach.
26. Income > 60kIncome < 60k
Find the next branch by looking for the data split that would have the biggest impact on the purity of
each node. There are several ways to do this mathematically (Gini Index, Information Gain, Chi-
Square).
Adjust the
hypothesis
20 outstanding loans20 outstanding loans
Credit Score > 700Credit Score < 700
20 outstanding loans
DTI > 40%DTI < 40%
70%50%
60% weighted
71%53%
59% weighted
80%73%
75% weighted
Use classification when you want to guess a non-numeric value, like a yes/no answer. We
will take a decision tree approach.
27. Repeat the process for each of your new “leaf” nodes. Stop when you reach an acceptable level of
accuracy, or when your accuracy begins getting worse with independent data.
Repeat until
convergence
20 outstanding loans
DTI > 40%DTI < 40%
Credit Score > 700Credit Score < 700Income > $60kIncome < $60k
100%50% 100%100%
80% weighted
Use classification when you want to guess a non-numeric value, like a yes/no answer. We
will take a decision tree approach.
28. Classification is used for lots of problems that copy human intuition.
Think about how you classify information to identify these images!
But with more advanced
approaches like convolutional
neural networks these
pictures can definitely be
classified by a machine.
These use cases are obviously
more complex than our
simple decision tree.
29. Now let’s walk through two of the most popular machine learning approaches
and discuss how the algorithms are applied.
Classification Clustering
30. Imagine Marketing
has asked you to split
these customers into
3 groups.
How would you do it?
Use clustering when there’s no “correct” classification, but you still want to assign
individuals to groups. This algorithm is called k-means clustering.
31. I can segment my customers by assigning them to 3 groups. We’ll set down 3 random “anchors” and
assign each customer to its closest anchor.
Create a
hypothesis
Use clustering when there’s no “correct” classification, but you still want to assign
individuals to groups. This algorithm is called k-means clustering.
32. Move the anchors to the center of each cluster. Count how many anchors are actually closer to one of
the other anchors.
Evaluate the
hypothesis
Use clustering when there’s no “correct” classification, but you still want to assign
individuals to groups. This algorithm is called k-means clustering.
33. Re-assign each customer to the group corresponding to the center they’re closest to.
Adjust the
hypothesis
Use clustering when there’s no “correct” classification, but you still want to assign
individuals to groups. This algorithm is called k-means clustering.
34. Repeat until
convergence
Move the anchors again. Continue re-assigning customers and moving the anchors until the anchors
stop moving.
Use clustering when there’s no “correct” classification, but you still want to assign
individuals to groups. This algorithm is called k-means clustering.
35. This is just the tip of the iceberg.
There are several algorithms available for various types of problems.
36. Why should anyone care about Machine Learning?
What is Machine Learning?
How does Machine Learning work?
Ok, but how does it really work?
How can an organization use Machine Learning?
Agenda
37. Engaging with Machine Learning
Image inspired by Microsoft
Delivering analytics with Machine Learning requires
alignment across people, process, technology, and data.
38. The sources of data can for machine learning can be quite broad. People
Process Technology
Data
Data
Warehouses
•Curated & Governed data
•Big data
•Cloud or on-prem
Data Lakes
•Unstructured & Semi-
structured data
•Streaming data
•Partially curated
Externally
Procured
Data
•May be purchased from 3rd
party providers
•May be scraped from the
web
•May require designing
research experiments
Data science teams typically have the programming and data integration skills to use data from
anywhere it can be found.
39. Data scientists combine broad skills to integrate
data, build models, and drive business value.
People
Process Technology
Data
41. Traditional Analytics
Store and access data. Filter and aggregate it. Visualize it.
Show it to the business
so they can take action.
Machine Learning
Filter and aggregate it.
1
𝑁
𝑛=1
𝑁
𝑥
Create a model. Generate new data
(predictions, etc.).
The new data can be stored
with the rest of the data for
use in analytics.
Or it can be visualized
directly to gain insights.
Or it can automate
decisions or actions,
allowing better processes
to run faster and 24/7.
The outputs of the data science process can be used in traditional analytics,
analyzed directly, or fed into automated decision-making.
People
Process Technology
Data
42. We’ll spend the rest of the workshop talking about the tools that enable all this
to happen.
+
Develop models faster with automated machine learning
Use any Python environment and ML frameworks
Manage models across the cloud and the edge.
Prepare data clean data at massive scale
Enable collaboration between data scientists and data engineers
Access machine learning optimized clusters
Azure Machine Learning
Python-based machine learning service
Azure Databricks
Apache Spark-based big-data service
People
Process Technology
Data
45. What is Databricks, in a nutshell?
is a unified platform powered by Apache Spark,
capable of abstracting complex cluster management to
scale out your data processing and machine learning
workloads, with intelligent optimizations to dynamically
reallocate workers given computational demands.
46. Scaling Out with Distributed Processing vs. Scaling Up
Option A
A-G
H-N
O-T
U-Z
Imagine this… I need to find every entry in the phone book with my first name. I’d like to hire
someone to read through the entire phone book and pick them out.
Option B
47. Scaling Out with Distributed Processing vs. Scaling Up
Option A
A-D
E-I
J-M
N-R
Imagine this… I need to find every entry in the phone book with my first name. I’d like to hire
someone to read through the entire phone book and pick them out.
S-V
W-Z
Option B
48. Imagine this… I need to extract every numeric column in my dataset and normalize the values in each.
I need to perform a grid search of hyperparameters to improve the accuracy of my classification model.
I need to train an algorithm to make correct classifications based on several features.
Scaling Out with Distributed Processing vs. Scaling Up
Option A
Option B
These common pieces of machine learning pipelines may sound simple, but in working with big data, tasks
like these can add hours, days, or weeks to your timeline, or be too cost inefficient to complete at all.
More flexible
More easily scalable
With Databricks and Spark,
easy to spin up and manage
49. What is Spark?
2010
Started at
UC Berkeley
2013
Databricks
started &
donated to ASF 2014
Spark 1.0 and
additions to Spark
Core (SQL, ML,
GraphX)
2015
DataFrames/Datasets
Tungsten
ML Pipelines
Apache
Spark
2.0
2016
Apache Spark 3.0
released, Adaptive
Query Execution,
new Pandas
function APIs
2020
Continued feature
development to further
support distributed ML
2018
Easier
Smarter
Faster
is an open source framework enabling distributed
cluster computing for large scale data processing.
The Spark architecture works to scale processing out
across compute resources with a managing driver
node assigning processing tasks to worker nodes.
Spark was founded with the singular goal to
“democratize” the “super power” of big data by
offering high-level APIs and a unified engine to
complete processing at all steps of the data pipeline.
Since then, thousands of contributors have developed Spark projects that improve
the accessibility and versatility of the Spark framework and distributed processing.
. . .
50. Scaling Out with Databricks
is a unified platform powered by Apache Spark,
capable of abstracting complex cluster management to
scale out your data processing and machine learning
workloads, with intelligent optimizations to dynamically
reallocate workers given computational demands.
Databricks brings scaling out to your workloads in a way that’s easy to spin up,
familiar to work with, and integrates with tools you already use every day.
51. In an
accessible
setting
Multiple languages in Databricks Notebooks (Python, R, Scala, SQL)
Databricks Connect: connect external tools with Databricks (IDEs, RStudio, Jupyter…)
Work on a single node and utilize the most common ML frameworks
Familiar Options & Distributed Frameworks on Databricks
Distributed
machine
learning
Spark MLlib for distributed models
Migrate Single Node to distributed with just a few lines of code changes
Distributed hyperparameter search (Hyperopt, Gridsearch)
PandasUDF to distribute models over subsets of data or hyperparameters
Koalas: Pandas DataFrame API on Spark
Deep Learning distributed training (HorovodRunner)
52. Enhanced Accessibility on Azure Databricks
Not an Azure Marketplace or
a 3rd party hosted service
PAAS: Platform as a Service
Azure
Databricks is a
first party
service on
Azure.
Azure Storage Services: Directly
access data in Azure Blob Storage
and Azure Data Lake Store
Azure Active Directory: For user
authentication, eliminate the
need to maintain two separate
sets of users in Databricks and
Azure.
Azure
Databricks is
integrated
seamlessly with
Azure services.
53. CCGPractices
LET’S TAKE A BREAK! Return at 10:10 AM EST
Strategy and Governance
• Data Governance Solution
• Data Privacy Solution
• Strategy Roadmap Solution
Services
• Health Assessments
• Roadmaps
• Data Governance
• Data Privacy
• Master Data Management with
Profisee
• Metadata Management
Analytics and Insights
• Customer Intelligence Solution
• Visualization & Reporting Solutions
Services
• Dashboards & Visualizations
• Operational Reporting
• Data Exploration
• Customer Insights
• Marketing Analytics
• Power Platform
• D365 Customer Insights
AI and Data Science
• Machine Learning Solution
• Model As A Service
Services
• Predictive Analytics
• Prescriptive Analytics
• Azure Cognitive Services
• Natural Language Processing
• Computer Vision / Image
• ML Ops
• Data Mining
• Data Science Enablement
• Data Science Roadmap
• Data Science Center of Excellence
Data and Infrastructure
• Platform Modernization Solution
• Cloud Migration and Management
Services
• DR/BC
• Security
• Azure Governance
• Data Warehousing
• Data Integration
• Data Architecture
• PowerApps
• Synapse DW
54. MLflow is an open source platform for managing the end-to-end machine learning
lifecycle. MLflow offers an integrated experience for tracking and securing machine
learning model training runs and running machine learning projects.
What is MLflow?
55. Tracking
• Record and query
experiments: code,
data, configuration,
results
Projects
• Package data science
code in a format to
reproduce runs on
any platform
Models
• Deploy machine
learning models in
diverse serving
environments
Registry
• Store, annotate,
discover, and manage
models in a central
repository
Serving
• Host ML models as
REST endpoints that
are updated
automatically
MLflow’s Five Key Components
58. Train and evaluate model
Azure Machine Learning offers a suite of tools for managing the Machine
Learning lifecycle.
Organize model assets
A
B
C
Deploy and manage model
59. Train and evaluate model
Azure Machine Learning offers a suite of tools for managing the Machine
Learning lifecycle.
Organize model assets
A
B
C
Deploy and manage model
60. Automated ML provides an easy way to quickly iterate through
multiple models. Train Organize
A
B
C
Deploy
How much is this car worth?
61. Automated ML provides an easy way to quickly iterate through
multiple models. Train Organize
A
B
C
Deploy
Mileage
Condition
Car brand
Year of make
Regulations
…
Parameter 1
Parameter 2
Parameter 3
Parameter 4
…
Gradient Boosted
Nearest Neighbors
SGD
Bayesian Regression
LGBM
…
Mileage Gradient Boosted Criterion
Loss
Min Samples Split
Min Samples Leaf
XYZ Model
Which algorithm? Which parameters?Which features?
Car brand
Year of make
62. Automated ML provides an easy way to quickly iterate through
multiple models. Train Organize
A
B
C
Deploy
Which algorithm? Which parameters?Which features?
Mileage
Condition
Car brand
Year of make
Regulations
…
Gradient Boosted
Nearest Neighbors
SGD
Bayesian Regression
LGBM
…
Nearest Neighbors
Criterion
Loss
Min Samples Split
Min Samples Leaf
XYZ Model
Iterate
Gradient Boosted N Neighbors
Weights
Metric
P
ZYX
Mileage
Car brand
Year of make
Car brand
Year of make
Condition
63. Automated ML provides an easy way to quickly iterate through
multiple models. Train Organize
A
B
C
Deploy
Which algorithm? Which parameters?Which features?
Iterate
64. Automated ML provides an easy way to quickly iterate through
multiple models. Train Organize
A
B
C
Deploy
Enter data
Define goals
Apply constraints
Input Intelligently test multiple models in parallel
Optimized model
65. For those who prefer a no-code experience, there’s a drag-n-drop
interface in Azure Machine Learning Designer. Train Organize
A
B
C
Deploy
66. Azure Machine Learning allows you to take advantage of cloud
compute through local tools or Azure Notebooks. Train Organize
A
B
C
Deploy
67. For image data, you can also train custom object detection models
with the intuitive Labeling interface. Train Organize
A
B
C
Deploy
68. Train and evaluate model
Azure Machine Learning offers a suite of tools for managing the Machine
Learning lifecycle.
Organize model assets
A
B
C
Deploy and manage model
69. Experiments allow you to capture training metrics to run side-by-side
comparisons and easily select the best model. Train Organize
A
B
C
Deploy
70. Pipelines can organize multiple data preparation and modeling steps
into a single resource. Train Organize
A
B
C
Deploy
71. Explain machine learning models to support business users and
compliance processes. Train Organize
A
B
C
Deploy
73. Train and evaluate model
Azure Machine Learning offers a suite of tools for managing the Machine
Learning lifecycle.
Organize model assets
A
B
C
Deploy and manage model
75. Models can be deployed to containers and shipped to the edge or
accessed via Rest APIs. Train Organize
A
B
C
Deploy
• Identify and promote your best models
• Capture model telemetry
• Retrain models with APIs
• Deploy models anywhere
• Scale out to containers
• Infuse intelligence into the IoT edge
• Build and deploy models in minutes
• Iterate quickly on serverless infrastructure
• Easily change environments
Proactively manage
model performance
Deploy models
closer to your data
Bring models
to life quickly
Train and evaluate models
Model MGMT, experimentation,
and run history
Azure
ML service
Containers
AKS ACI
IoT edge
Docker
Azure
ML service
76. Monitor data drift over time to know when your model may require
re-training. Train Organize
A
B
C
Deploy
77. Now let’s see some of the
Azure Machine Learning tools in action.
80. We can work with your business to deliver custom predictive and prescriptive
analytics across the lifecycle.
Machine Learning Strategy
• Develop a backlog of
predictive and prescriptive
use cases
• Refine and prioritize use
cases by value
• Develop a predictive
roadmap
Model Development /
Data Mining
• Aggregate data from across
internal and external data
sources
• Perform correlation
analyses, develop models,
and find new relationships
in your data
Model Maintenance
• Monitor and maintain
statistical models to sustain
predictive power
• Develop a model telemetry
dashboard
• Test model design changes
to improve predictive power
Model Governance & Operating Model
• Assess existing Data Science & Artificial Intelligence maturity
• Develop standards and processes to help guide data science output
• Build a Data Science Center of Excellence
Model Deployment / MLOps
• Customize and deploy pre-
existing models from Azure
Cognitive Services
• Deploy custom model as an
API or batch job, or support
deployment in existing
systems
RapidInsight Prototype Offering
Model as a Service Subscription OfferingElastic AI Research & Development
MLOps POC
Managed Services
Accelerators
81. CCG is a full-service cloud analytics provider.
Strategy and Governance
• Data Governance Solution
• Data Privacy Solution
• Strategy Roadmap Solution
Services
• Health Assessments
• Roadmaps
• Data Governance
• Data Privacy
• Master Data Management with
Profisee
• Metadata Management
Analytics and Insights
• Customer Intelligence Solution
• Visualization & Reporting Solutions
Services
• Dashboards & Visualizations
• Operational Reporting
• Data Exploration
• Customer Insights
• Marketing Analytics
• Power BI
• D365 Customer Insights
AI and Data Science
• Machine Learning Solution
• Model As A Service
Services
• Prescriptive Analytics
• Azure Cognitive Services
• Natural Language Processing
• Computer Vision / Image
• ML Ops
• Data Mining
• Data Science Enablement
• Data Science Roadmap
Data and Infrastructure
• Platform Modernization Solution
• Cloud Migration and Management
Services
• DR/BC
• Security
• Azure Governance
• Data Warehousing
• Data Integration
• Data Architecture
• PowerApps
• Synapse DW