Categories
Technology

Machine Learning 101

Presenters

  • Greg Corrado, Senior Research Scientist, Google
  • Vincent Nestler, Professor & Assistant Director of Cybersecurity, CSU San Bernardino
  • David Vasilia, Enterprise Network Administrator & Faculty, CSU, San Bernardino
  • Internet2 & GCP: internet2.edu/gcp
  • CS edu grants: cloud.google.com/edu

Machine Learning 101

  • Already in everyday products: photos, inbox, maps
  • 2 disciplines: AI and machine learning
  • Traditional AI systems are programmed to be clever
  • ML-based AI systems are designed to learn to be clever
  • Classic AI works on rules and contingencies; ML AI learns from examples and data.
  • Machines learn by example: models (which have parameters) feed predictions, which feeds a learner, which in turn feeds the parameters. This is surprisingly simple and generic.
  • Need 4 things: computational resources, good tools & algorithms, training examples, creativity and ingenuity of people.
  • Effective, but very gradual process that takes millions or billions of examples for it to work. It needs to cycle many many times.
  • ML coming of age in this decade because the computational power is exists now and it’s cheap and plentiful enough, i.e. CPU, GPU, Google TPU.
  • tensorflow.org a toolkit for machine learning
    • Open standard
    • Next gen deep learning tools built in
    • One system flexible enough for ML research
    • Robust enough for use in real products
    • Same software Google researchers use
  • Deep learning not one function, but a set of composable subfunctions for model building.
  • Distributing ML Tech Globally
    • Shared Tools: TensorFlow + CloudML
    • Ready-made ML systems (Cloud Vision API, Cloud Speech API, Cloud Translate API, etc.)
    • Use our tools to build your own system!
    • Example: TensorFlow cucumber sorting tool (really!)
    • Shared knowledge: open research publication at intl conferences; global direct community education; funding academic research and education.
  • Google published 90+ papers in the last 4 years
  • Takeaways:
    • Differentiation between AI vs. ML vs. Robotics
    • It isn’t magic, just a tool
    • Machines learn best from examples
    • Why now? fast computation
    • Make ML work requires creativity/ingenuity, cheap/fast computation/examples to learn from (data), tools & algorithms, TensorFlow makes ML software available for free.
    • Google Cloud makes hardware available.

Cloud for Higher Ed

  • Programming a campus rover: students are given a sensor, a raspberry pi, and Python. Then, they need to figure out how to integrate it.
  • Hacking now means hacking things together. You don’t have to be an engineer and you don’t need to know everything.
  • How can I level the playing field for my students? Be able to connect to Chrome and a Google compute engine. Everyone can look at and work with this environment, and they can explore from there.
  • A project we worked on in class: Android mapping for WiFi signal strength on campus. War driving took signal strength and using mapping API to literally map it to a real topographical map. Now we can “see our WiFi.”
  • We used Intermapper software to map the Internet, specifically the CENIC network from Los Angeles. The students loved this.

Panel

  • What is the difference between deep learning and machine learning? ML is the larger field of making machines that learn. DL is a small subset of this.
  • How far is Google taking cultural sensitivity into account with ML? Take translate as an example: you can dig into what the algorithm did to come up with its response.
  • If we use a Google tool, does this tool report what it learns back to Google? NO. What is pricing model for Google Cloud for Google Apps customers? It is independent of G-Suite.

Next Steps

  • Google is now a member of Internet2.
  • Will work with universities across the US to explore how Google Cloud Platform can better serve higher education
  • Help students build what’s next!
  • GCP Education Grants are available to: faculty in US, teaching university courses in CS or related fields in 2016-17 academic year. Examples: general CS, Cybersecurity, systems administration, networking.
Categories
Technology

Your Legacy: An Organization That Delivers Strategic Value, Again and Again

Presenters

  • Dean Meyer, President, NDMA
  • Julie Little, VP, EDUCAUSE

There’s lots going on in education technology right now! Tons of enabling tech that changes business, education, and business models. Make learning engaging, contextual and visual.

Types of Strategic Value

Growing human intellect is absolutely strategic!

  1. Keep business running (deliver existing services)
  2. Reduce costs of IT
  3. Reduce costs of business (productivity)
  4. Improve human effectiveness (thinking, collaboration)
  5. Improve customer relationships, loyalty (engagement)
  6. Enhance product value

As a senior leader, what can you do to drive your organization up this ladder?

The Classic Definition of the Role of a CIO portrays us superman / superwoman, but the reality is that the CIO becomes a cog in the machine…often a bottleneck. Wouldn’t it be better to be the driver of the machine. To get there, you need to first be the designer of the machine. Our systems send signals that guide people. For CIOs, these signals are often about building an empire.

How you define leadership changes as you advance in your career, describable through different lenses.

  1. Project management
  2. Supervision
  3. Business strategies
  4. Organizational designer

Leaving the legacy of an organization that can prosper, with or without you. Program the organizational system.

The Machine

“The programming language of leadership”

  1. Structure
  2. Metrics & rewards
  3. Internal Economy
  4. Culture
  5. Methods and Tools

Culture

The easiest thing to change. Mixture of values and behaviors. You critique the behavior, not the people.

Structure

  • Org chart
  • Workflows

Internal Economy

  • Planning
  • Dynamic governance

Methods & Tools

  • Individual competencies of individual groups
  • Fine tuning

Metrics & Rewards

  • Dashboards, consequences
  • Fine tuning

Value Chain

  • Expertise in linkage: business-IT. This is a kind of “bridging knowledge.” [Structure: “sales”]
  • Collaborative discovery: help others find the things they need. [Methods: discovery]
  • Broad, innovative catalog. You need a quiver full of different arrows to apply to any given problem. [Internal Economy: business planning]
  • Time to develop proposals. [Internal Economy: unbillable time]
  • Project funding. [Internal Economy: demand management]
  • Delivery: capability, teamwork. Deliver on-time and on-budget. [Structure: walk-throughs]
  • Benefits realization. Make sure things get used! [Culture: business within a business; Methods: benefits measurement]

3 Parallel Leadership Strategies

  • Business Value
  • Capabilities: tech, operations
  • Organization (often neglected by leadership because it’s so foundational to the success of the first two strategies)

Big three are Culture, Structure, and Internal Economy. After this, methods & tools, metrics & rewards.

Exercise

  • What does world class IT team mean to you?
  • Measure the gaps. These are the symptoms of something deeper in your organization. Keep asking WHY until you get to one of the fundamentals.
  • Sequence the Root Causes into your strategy.
  • Publicize your strategy among your staff and the peers in your institution.

Free yourself from the tyranny of urgency.

Categories
Technology

Initiative Impact Analysis to Prioritize Action and Resource Allocation

Presenters

  • Virginia Fraire, VP of Student Success, Austin Community College District
  • Laura Malcom, VP of Product, Civitas Learning Inc.
  • Angela Baldasare, Asst. Provost, Institutional Research, The University of Arizona
  • partnerships@civitaslearning.com
  • civitaslearning.com

University of Arizona

  • Goal: improve 1st year retention rate from 81% to 91% by 2025
  • How do we find and integrate good data to make good decisions that help our students?
  • When I came on board, I found out that we never had a centralized student academic support office
  • SALT office (Strategic Alternative Learning Techniques) – used to support students with learning disabilities. How can we adopt and adapt some of the techniques that worked there?
  • We were using siloed participant data that was not very helpful. It was not transformative and it didn’t tell us much.
  • We came to Civitas for help.
  • In 2009, U of A opened doors to the “Think Tank” to streamline and centralize a number of academic support services offered by nationally certified tutors; mission is to empower UA students by providing a positive environment where they can master the skills needed to become successful lifelong learners.
  • In one year, nearly 11,000 students make more than 70,000 visits and spend 85,000+ hours with support staff.

Think Tank Impact

  • Illume Impact used PPSM to measure 2.7%(pp) overall life in persistence for students using the writing center
  • 3.4% (pp) increase for 1st year students
  • Less than 10% of 1st year students taking advantage of this service!
  • These results will inform strategic campaigns to offer Think Tank services to students as part of first-year experience.
  • 8.2% persistence increase for students who were most at risk

Taking Initiative With Confidence

  • Sharing impact findings with academic colleges to discuss the need for increased referrals to Think Tank.
  • PPSM has changed the conversation with faculty who want rigorous data.
  • Bolstering credibility and validity to Think Tank services.

Austin Community College

Highland Campus is home to the ACCelerator, one of the largest high-tech learning environments in the country.

“The Accelerator”

  • Provides access to 600+ desktop computer stations spread over 32,000 square feet, surrounded by classrooms and study rooms.
  • Offers redesigned DevEd math courses powered by learning software with an onsite faculty members, tutors and academic coaches to increase personalization and engagement
  • Additional support services are offered, including non-math tutoring, advising, financial aid, supplemental instruction, and peer tutoring.
  • During the 2015-16 year, the ACCelerator served over 13,000 unique students in well over 170,000 interactions.

Accelerator Impact

  • Students who visit the lab at least once each term persist at a higher rate.
  • 4x persistence impact found for DevEd students.
  • Part-time DevEd students and DevEd students with the lowest persistence predictions had even better outcomes.
  • 6.15% increase in persistence for students visiting the learning lab.
  • Results are informing strategic decisions about creating similar learning spaces at other campuses.
  • Impact results have helped validate ACC data and in-house analyses
  • Discussions with math faculty continue to strengthen the developmental math redesign
  • Persistence results leading to further investigation of other metrics related to accelerated learning, particularly for DevEd students.
  • For this kind of approach to work, silos need to be broken down.

 

 

 

Categories
Technology Uncategorized

Preparing for That IT Strategic Planning Project: A Data-Driven Approach

Presenters

  • Jerrold Grochow, CIO-in-Residence, Internet2
  • Sara Jeanes, Program Manager, Internet2

Underlying Ideas for this Seminar

  • Data: facts and statistics collected together for reference or analysis.
  • If you can’t define it, you don’t know what your data says
  • If you don’t analyze it, you don’t know what your data means
  • If you don’t organize and present your analysis, you can’t convince anyone of what it means
  • Data is most valuable when it can be turned into information that can be used for action

Goals

  • Understand what data is important to different constituencies
  • Learn practical approaches to collecting, organizing and presenting that data
  • Start to apply this framework to your own strategic planning projects

What is Strategic Planning all About?

  • Determining where we are now (org assessment)
  • Determining what drives us to the future (drivers & trends)
  • Determining where we want to be in the future (ID strategic issues)
  • Determining how we’re going to to get to that future (develop strategic business plan)
  • “If you don’t know where you want go go, any road will get you there.”
  • “If you don’t know where you are, it’s tough to figure out how to get to where you want to be.”
  • Being aware of external drivers that influence our organization
  • In short: where/what/how, now and in the future

What Makes Data Important?

  • Things that get measured get managed
  • Can be used to look for trends
  • Helps democratize the process by removing emotions
  • Value to the institution
  • Allows for effective SWOT exercise
  • Sets the stage
  • Raises a strategic issus
  • Highlights a trend
  • Distinguishes a constituency
  • Presents a resource concern

Different Types of Strategic Planning Projects

  • Initial plan
  • Revisited plan: why? what changed?
  • Plan update
  • Organizational focus: resources, culture
  • Service focus
  • Technology focus

Strategic Planning Data Planning Framework

  1. Determine type of project and focus
  2. Determine key questions/issues
  3. Assess & define data
  4. Collect data
  5. Perform analysis
  6. Organize & present

Types of Data Needed

  • Skills assessment: what do we need?
  • Who is using our resources, and how?
  • Retention and recruitment: who is leaving and why? What’s their demographic? What are the demographics of the various departments?
  • What’s the data we’ve already got? Staff counts, project portfolio, budgets, etc.

Two Principal Types of Data

  • Primary: data you collect specifically to serve the needs of the strategic planning activity
  • Secondary: data you have (or can get) that was collected for other purposes but that will be useful
  • You are going to have to use data you already have
  • Internal secondary data: operational data, i.e. logs, usage data, help desk ticketing system, admissions data, anything in IR, IPEDS, infrastructure, monitoring, etc.

Operational Data: Service Utilization

  • Definition: how much of a particular service is used by different groups of users
  • Measure: what best shows usage
  • Analysis: trends/patterns
  • Organization/presentation: table, chart, interactive graphic

External Secondary Data

  • What data can you readily get that would be useful?
  • EDUCAUSE Core Data Service, NSSE, IPEDS, census, industry surveys (Gartner, Forrester, McKinsey)

Internal/External can be both primary and secondary

  • Internal: about the organization
  • External: about the environment

How do you collect data?

  • Instrument your systems, surveys, questionnaires, focus groups
  • Sensors

What Kind of Data?

  • Text, numbers, pictures
  • Qualitative
  • Quantitative
  • USE BOTH, to show impact and value

Timing: When Do You Collect Data?

  • Before: to help ID and frame issues, and to ensure the planning process can proceed smoothly.
  • During: as discussion uncovers additional data that would be useful
  • After: to better manage your organization and monitor progress against plan
  • Always: for the reasons mentioned above

How Can We Best Present Data?

In ways that best resonate with the audience, in ways that show importance. For example: “That’s the equivalent of a the cost of a full-time grad assistant” or “IT capital plan == building capital plan” or “system maintenance == building maintenance”

  • Text: quotations, narrative, video
  • Numbers: tables, charts, graphs
  • Pictures: infographics, photos

Institutional Strategic Priorities

  • Understand research and learning/teaching focus areas! This will tell you where senior leadership of the institution wants to go.
  • Understand the financial areas! This will interact with research and learning/teaching focus areas.
  • Understand the technology focus! You’ll be able to explain how this will interact with all the other areas.
Categories
Education Technology Uncategorized

Moving to the Cloud with Amazon Web Services

Presenters

  • Ron Kraemer, VP and CIO, University of Notre Dame
  • Ryan Frazier, Director, System Engineering & Operations, Harvard Business School
  • Sarah Christen, Director of Community Platforms and CIO, Cornell University
  • Mike Chapple, Senior Director, IT Service Delivery, University of Notre Dame
  • Blake Chism, IT Transformation Sr., Amazon Web Services

Resource

Session Introduction

RC: we want to accomplish 1 major goal: roadmap and framework to take back to campus and “deal with the cloud in your culture and your world.”

It’s not perfect, and it’s a lot of work. BUT, it’s better service to our universities if we do it well.

SC: we’re a cloud-first institution. Lots of leadership change since that initiative started. We have 62 accounts under our master contract (master contract signed 18 months ago). Lots of accounts outside our contract. About $300K annual spend outside the IT org…we have a very distributed IT model.

We call the transformation “cloudification.” It’s a partnership with campus IT units. We refactor for most effective use of cloud technologies and containerization vs. “lift and shift.” Central IT must be the expert that campus wants to come to for help. We want to enable, not enforce (we do have SOME requirements to move to the master contract). We understand that if IaaS isn’t better with us, campus will make the move without us. We allow campus technologists to focus on unit differentiators central IT can help with the utilities.

Reqs for Cornell Master Contract

  • Onboarding discussion
  • Attestation
  • Shibboleth for authentication
  • DUO for multi-factor authentication for AWS Console access
  • Lock down root account, escrow with security office
  • Activation of AWS config
  • Activation of CloudTrail
  • CloudTrail logs sent to Security office
  • Activation of Cloudcheckr

What About Researcher AWS Accounts?

  • Easy onboarding without a lot of steps or complication
  • No interference with their research. No overhead (cost or performance)
  • Solutions for export control data and other compliance reqs.
  • Standard network config not always a good fit. “I am an island, not part of Cornell campus.”
  • Technical consultation options: docker, data storage, training, devops support

Today

  • All centrally hosted apps are being moved if possible
  • Infrastructure services are a large part of our on prem inventory
  • Campus units are moving more quickly than our central IT org

Biggest Challenge to Cloud Transformation: RESISTANCE TO CHANGE

RF: I’m director of Infrastructure Customer and Project Services. Initiated cloud strategy and planning when I was in the central IT division.

Cloud @Harvard

  • <2013: Exploration. Very early adopters at Harvard Medical School (research lab), pockets of uncoordinated use, little use within central and school-level IT departments.
  • 2013-2016: Alignment. We got enterprise agreement, direct billing and enterprise support services, laid technical foundations, brought on early adopters, developed cloud strategy.
  • 2016-?: Implementation. Accelerating adoption at all levels, i.e. labs, initiatives, schools, and central IT; shared service roadmaps; early adopters beginning to focus on optimization.

The Case for Cloud

  • Quality, cost, reliability, speed.
  • cloud.huit.harvard.edu
  • Our goal was to have 75% of our infrastructure at AWS by 2017. We’re currently at 31%.

HBX: Can We Deliver the Rich Interactive Experience of the Business School Online?

  • LET’S TRY
  • Move fast – 90 days to build, implement and launch application and registration system, < 1 year for complete course platform
  • Run independent of HBS IT – minimize impact on eisting services, enable new approaches to new needs
  • Be able to scale up or down rapidly – prepare for success or failure of the experiment

AWS Service Mix

  • 17 VPCs, 23 ELBs, 135 EC2 instances, 345 EBS volumes, 18TB instance storage, 4 Redshift Clusters, 18 RDS DBs, 30+ TB loaded via snowball, 78 TB object storage
  • Storage is a very small part of our spend (data transfer is 1%)
  • EC2 is about 58% of our spend

Notre Dame’s Journey to the Cloud

Why move at all? For us, we were sitting on an aging data center infrastructure. A capital investment – particularly cooling – had to happen if were going to continue. Tech demands from students, faculty and administrators outpaced our time and budget. In 2012, emergency communications were a critical concern.

2012

Originally we moved the web site as part of an emergency mitigation effort – “can we move the site in the event of an emergency?”

  • www.nd.edu
  • 3 web servers
  • load-driven autoscaling
  • Geographic diversity
  • It was really an easy move for us

2013

  • 435 web sites
  • 4 million monthly views
  • db as a service
  • ElastiCache

Cloud First

  • In 2013, we began having conversations about “why don’t we move everything over?”
  • We wanted to take advantage of what the cloud offers: 80% by the end of 2017; we’re at 59% today.
  • SaaS first, then PaaS, then IaaS, then on-prem.
  • Setting a goal created “a line in the sand,” that made it real for our people.

What We Learned

  • Rethink technical roles. NOBODY IS GOING TO LOSE THEIR JOB! However, you might not be doing the same job three years from now…
  • We were a very siloed organization prior to the cloud move. As a result of our move, those silos are breaking down.
  • Rethink security processes and tools (this was hard for us). We’re not mapping THINGS 1-to-1, we’re mapping OBJECTIVES.
  • Leverage automation – we’ve used ansible
  • Practical financial engineering. Our data center manager is now the guy who is our financial expert, who gives us insight into our costs. We’ve standardized on regions, instances (T2 class – about 3/4 of all our instances), use of reserve instances, etc.
  • Make a few choices and just go with them!

Cloud Transformation Maturity Model

  • Project Stage: limited knowledge, executive support, inability to purchase, limited confidence, no clear ownership or direction.
  • Foundation Stage
  • Migration Stage
  • Optimization Stage

Blake Chism from AWS: we developed this model to help you figure out where you are in the process. We’ve found that for most of our customers, procurement conversations are getting easier, but they’re still a challenge. If the central IT team helps take ownership, it can help organizations move forward more effectively, i.e. central IT not perceived as “being in the way.”

If your team has good processes now, your move will be much easier.

Project Stage

No matter what, you need to have a business case, a reason why you’re doing it. The roadmap helps describe how you’re doing it. Governance models evolve, and you get better at understanding them. Services change, and you need to have a plan about how you’ll integrate them (or not).

POC are much easier because if it doesn’t work, you can simply shut it off and you’re only out a few bucks. Try things out!

During the Project Stage, establish a “Cloud Center of Excellence” or “Cloud Competency Center” to get the organization moving in the right direction.

Foundation Stage

Lack of a detailed organizational transformation plan can be a challenge. Do a staff skills gap analysis to help you here.

Migration Stage

Should be as short as possible to get over the hump of hybrid and duplicate hosting. All-in will allow you to BEGIN doing new and exciting things. Imagine a space where the default state of, say, development environments, is OFF. All in is just the end of the adoption journey.

Were your enterprise systems like LMS, SIS, HR, Financials and the portal viewed as special and treated differently from smaller apps? Have you moved them yet?

  • Cornell: our KFS (Kuali) finance moved first (we dockerize ours) high availability on file shares was an early challenge (EFS – Elastic File Services are out now)
  • Harvard: IdM was first, we do Peoplesoft now, Oracle e-business is happening now
  • ND: ERP and LMS  – do not separate db servers and application servers!

AWS Cloud Adoption Journey

ALL: we use our AWS solutions architects extensively, and we’ve relied on AWS consulting almost exclusively for our migrations. These interactions have helped to accelerate our staff learning, because our staff are the ones who will need to maintain it long-term.

The professional services unit can help you figure out the high-level ecosystem you need for your particular situation. Enterprise support services is a bit pricey, but it’s useful in many cases.

SC: at Cornell, we created a 100 day training program that includes getting Amazon Solutions Architect certification. This is a good way to assure a certain level of competency. Some schools are using our model for training up their people, and they’re also using it as a way to network and learn new things, i.e. get names of people at other institutions that are going through the same problems.

Building the Roadmap – “Cloud Adoption Framework”

More details here: https://aws.amazon.com/education/movingtothecloudworkshop/

Organizes and describes the perspectives in planning, creating, managing, and supporting a modern IT service. Provides practical guidance and comprehensive guidelines for establishing, developing and running AWS cloud-enabled environments.

Don’t try to use all the components at once! Have your Cloud Center of Excellence (or whatever you choose to call it) do it in sprints by taking five or six of the elements and working through them.

In the private sector, the push to move to the cloud typically comes from the top. In higher ed IT, the push to move to the cloud typically comes from below. What we’ve often done is break off a small part of our budget, and use it to fund an “engineering SkunkWorks” where we can do the POCs and get staff buy-in. If the “where you do computing versus how you do computing” equation doesn’t click in your leadership’s minds, you’re going to have a hard time going anywhere.