3 Types of Employees That Can Use AI Offerings on Google Cloud

The Google Cloud Platform (GCP) comes with a number of services, databases, and tools to operationalize company-wide data management and analytics. With the insights and accessibility provided, you can leverage data into artificial intelligence (AI) and machine learning (ML) projects cost-efficiently. GCP empowers employees to apply their ideas and experience into data-based solutions and innovation for business growth. Here’s how.

1. Developers without Data Science Experience

With GCP, developers can connect their software engineering experience with AI capabilities to produce powerful results. Using product APIs, developers can incorporate ML into the product without ever having to build a model.

Let’s take training videos for example – Your company has thousands of training videos varying in length and across subjects. They include everything from full-day trainings on BigQuery, to minutes-long security trainings. How do you operationalize all that information for employees to quickly find exactly what they want?

Using Google’s Cloud Video Intelligence API, the developer can transcribe not only every single video, word-for-word, but also document the start and end time of every word, in every video. The developer builds a search index on top of the API, and just like that, users can search specific content in thousands of videos. Results display both the relevant videos and timestamps within the videos, where the keyword is found. Now employees can immediately find the topic they want to learn more about, without needing to sift through what could be hours of unrelated information.

Additional APIs include, Cloud Natural Language, Speech-to-Text, Text-to-Speech, Cloud Data Loss Prevention, and many others in ML.

2. Everyone without Data Science Experience, who isn’t a Developer

Cloud AutoML enables your less technical employees to harness the power of machine learning. It bridges the gap between the API and building your own ML model. Using AutoML, anyone can create custom models tailored to your business needs, and then integrate those models into applications and websites.

For this example, let’s say you’re a global organization who needs to translate communications across dialects and business domains. The intricacies and complexities of natural language require expensive linguists and specialist translators with domain-specific expertise. How do you communicate in real time effectively, respectfully, and cost-efficiently?

With AutoML Translation, almost anyone can create translation models that return query results specific to your domain, in 50 different language pairs. It graphically ingests your data from any type of Sheet or CSV file. The input data necessary is pairs of sentences that mean the same thing in both the language you want to translate from, and the one you want to translate to. Google goes the extra mile between generic translation and specific, niche vocabularies with an added layer of specificity to help the model get the right translation for domain-specific material. Within an hour, the model translates based on your domain, taxonomy, and the data you provided.

Cloud AutoML is available for platform, sight, structured data, and additional language capabilities.

3. Data Scientists

Data scientists have the experience and data knowledge to take full advantage of GCP AI tools for ML. One of the issues data scientists often confront is notebook functionality and accessibility. Whether its TensorFlow, PyTorch, or JupyterLab, these open source ML platforms require too many resources to run on a local computer, or easily connect to BigQuery.

Google AI Platform Notebooks is a managed service that provides a pre-configured environment to support these popular data science libraries. From a security standpoint, AI Platform Notebooks is attractive to enterprises for the added security of the cloud. Relying on a local device, you run the risk of human error, theft, and fatal accidents. Equipped with a hosted, integrated, secure, and protected JupyterLab environment, data scientists can do the following:

  • Virtualize in the cloud
  • Connect to GCP tools and services, including BigQuery
  • Develop new models
  • Access existing models
  • Customize instances
  • Use Git / GitHub
  • Add CPUs, RAM, and GPUs to scale
  • Deploy models into production
  • Backup machines

With a seamless experience from data to a deployed ML model, data scientists are empowered to work faster, smarter, and safer. Contact Us to further your organization’s ability to maximize data, AI, and ML.

Here are a few resources for those who wish to learn more about this subject:

Sam Tawfik, Sr Product Marketing Manager

3 Ways McDonald’s France is Preparing their Data for the Future

Data access is one of the biggest influences on business intelligence, innovation, and strategy to come out of digital modernization. Now that so much data is available, the competitive edge for any business is derived from understanding and applying it meaningfully. McDonald’s France is gaining business-changing insights after migrating to a data lake, but it’s not just fast food that can benefit. Regardless of your industry, gaining visibility into and governance around your data is the first step for what’s next.

1. No More Manual Legacy Tools

Businesses continuing to rely on spreadsheets and legacy tools that require manual processes are putting in a lot more than they’re getting out. Not only are these outdated methods long, tedious, subject to human error, and expensive in both time and resources – but there’s a high probability the information is incomplete or inaccurate. Data-based decision making is powerful, however, without a data platform, a strong strategy, automation, and governance, you can’t easily or confidently implement takeaways.

Business analysts at McDonald’s France historically relied on Excel-based modeling to understand their data. Since partnering with 2nd Watch, they’ve been able to take advantage of big data analytics by leveraging a data lake and data platform. Architected from data strategy and ingestion, to management and pipeline integration, the platform provides business intelligence, data science, and self-service analytics. Now, McDonald’s France can rely on their data with certainty.

2. Granular Insights Become Opportunities for Smart Optimization

Once intuitive solutions for understanding your data are implemented, you gain finite visibility into your business. Since completing the transition from data warehouse to data lake, McDonald’s France has new means to integrate and analyze data at the transaction level. Aggregate information from locations worldwide provides McDonald’s with actionable takeaways.

For instance, after establishing the McDonald’s France data lake, one of the organization’s initial projects focused on speed of service and order fulfilment. Speed of service encompasses both food preparation time and time spent talking to customers in restaurants, drive-thrus, and on the online application. Order fulfilment is the time it takes to serve a customer – from when the order is placed to when it’s delivered. With transaction-level purchase data available, business analysts can deliver specific insights into each contributing factor of both processes. Maybe prep time is taking too long because restaurants need updated equipment, or the online app is confusing and user experience needs improvement. Perhaps the menu isn’t displayed intuitively and it’s adding unnecessary time to speed of service.

Multiple optimization points provide more opportunity to test improvements, scale successes, apply widespread change, fail fast, and move ahead quickly and cost-effectively. Organizations that make use of data modernization can evolve with agility to changing customer behaviors, preferences, and trends. Understanding these elements empowers businesses to deliver a positive overall experience throughout their customer journey – thereby impacting brand loyalty and overall profit potential.

3. Machine Learning, Artificial Intelligence, and Data Science

Clean data is absolutely essential for utilizing machine learning (ML), artificial intelligence (AI), and data science to conserve resources, lower costs, enable customers and users, and increase profits. Leveraging data for computers to make human-like decisions is no longer a thing of the future, but of the present. In fact, 78% of companies have already deployed ML, and 90% of them have made more money as a result.

McDonald’s France identifies opportunity as the most important outcome of migrating to a data lake and strategizing on a data platform. Now that a wealth of data is not only accessible, but organized and informative, McDonald’s looks forward to ML implementation in the foreseeable future. Unobstructed data visibility allows organizations in any industry to predict the next best product, execute on new best practices ahead of the competition, tailor customer experience, speed up services and returns, and on, and on. We may not know the boundaries of AI, but the possibilities are growing exponentially.

Now it’s Time to Start Preparing Your Data

Organizations worldwide are revolutionizing their customer experience based on data they already collect. Now is the time to look at your data and use it to reach new goals. 2nd Watch Data and Analytics Services uses a five-step process to build a modern data management platform with strategy to ingest all your business data and manage the data in the best fit database. Contact Us to take the next step in preparing your data for the future.

-Ian Willoughby, Chief Architect and Vice President

Listen to the McDonald’s team talk about this project on the 2nd Watch Cloud Crunch podcast.

3 Steps to Implementing a Machine Learning Project

Once you understand the benefits and structure of data science and machine learning (ML), it’s time to start implementation. While it’s not an overly complicated process, planning change management from implementation through replication can help mitigate potential pitfalls. We recommend following this 3-step process.

Step 1: Find Your Purpose

It can be fun to tinker around with shiny, new technology toys, but without specific goals, the organization suffers. Time and resources are wasted, and without proof of value-added, the buy-in necessary from leadership won’t happen. Why are you implementing this solution, and what do you hope to get out of the data you put in?

ML projects can produce several outcomes contributing to decisions fueled by data and gaining insights into customer buying behavior, which can be used to optimize the sales cycle with new marketing campaigns. Other uses could include utilizing predictive search to improve user experience, streamlining warehouse inventory with image processing, real-time fraud detection, predictive maintenance, or elevating customer service with voice to text speech recognition.

ML projects are typically led by a data scientist who is responsible for understanding the business requirements and who leverages data to train a computer model to learn patterns in very large volumes of data to predict outcomes while also improving the outcomes over time.

Successful ML solutions can generate 4-5% higher profit margins, so identify benchmarks, set growth goals, and integrate regular progress measurements to make sure you’re always on track with your purpose in mind.

Step 2: Apply Machine Learning

The revolutionary appeal for ML is that it does not require an explicit computer program to deliver analytics and predictions, it leverages a computer model that can be trained to predict and improve the outcomes. After the data scientist’s analysis defines the business requirements, they wrangle the necessary data to train the ML model by leveraging an algorithm, which is the engine that turns the data into a model.

Data Wrangling

Data preparation is critical to the success of the ML project because it is the foundation of everything that follows. Garbage in equals garbage out, but value in produces more value.

Raw data can be tempting, but data that isn’t clean, governed, and appropriate for business use corrupts the model and invalidates the outcome. Data needs to be prepared and ready, meaning it has been reviewed for accuracy, and it’s available and accessible to all users. Data is typically stored in a cloud data warehouse or data lake and it must be maintained with ongoing governance.

A common mistake organizations make is relying on data scientists to clean the data. Studies have found that data scientists spend 70% of their time wrangling data and only 30% of the time implementing the solution and delivering business value. These highly paid and skilled professionals are scarce resources trained for innovation and analyzing data, not cleaning data. Only after the data is clean should data scientists start their analysis.

ML Models

The data scientist’s core expertise is in selecting the appropriate algorithm to process and analyze the data. The science in ML is figuring out which algorithm to use and how to optimize it to deliver accurate and reliable results.

Thankfully, ML algorithms are available today in all the major service provider platforms, and many Python and R libraries. The general use cases within reach include:

  • Classification (is this a cat or is this not a cat) using anomaly detection, marketing segmentation, and recommendation engines.
  • NLP (natural language progression) using autocomplete, sentiment, and understanding (i.e., chatbots).
  • Timeseries using forecasting.

Algorithms are either supervised or unsupervised. Supervised learning algorithms start with training data and correct answers. Labeled data trains the model using the algorithm and feedback. Think texting and autocorrect – the algorithm is always learning new words based on your interaction with autocorrect. That feedback is delivered to the live model for updates and the feedback loop never ends.

Unsupervised learning algorithms start with unlabeled data. The algorithm divides the data into meaningful clusters used to make inferences about the records. These algorithms are useful for segmentation of click stream data or email lists.

Some popular algorithms include CNN (convolutional neuro network), a deep learning algorithm, K Means Clustering, PCA, Support Vector Machine, Decision Trees, and Logistic Regression.

Model Quality

With everything in place, it’s time to see if the model is doing what you need it to do. When evaluating model quality, consider bias and variance. Bias quantifies the algorithm’s limited flexibility to learn the pattern. Variance quantifies the algorithm’s sensitivity to specific sets of training.

Three things can happen when optimizing the model:

  1. Over-fitting: Low bias + high variance. The model is too tightly fitted to the training data, and it won’t generalize data it hasn’t seen before.
  2. Under-fitting: High bias + low variance. The model is new and hasn’t reached a point of accuracy. Get to over-fitting first, then back up and reiterate until the model fits.
  3. Limiting/preventing under/over-fitting: There are too many features in the model (i.e. data points used to build the model), and you need to either reduce them, or create new features from existing features.

Before unleashing your ML project on customers, experiment first with employees. Solutions like virtual assistance and chat bots that are customer-facing can jeopardize your reputation if they don’t add value to interactions with customers. Because ML influences decision-making, accuracy is a must before real-world implementation.

Step 3: Experiment and Push into Production

With software projects, it either works or it crashes. With data science projects, you have to see, touch, and feel the results to know if it’s working. Reach out to users for feedback and to ensure any changes to user experience are positive. Luckily, with the cloud, the cost of experimentation is low, so don’t be afraid to beta test before a full launch.

Once the model fits and you’ve pushed the project into production, make noise about it around the organization. Promote that you’re implementing something new and garner the attention of executive leadership. Unfortunately, 70% of data projects fail because they don’t have an executive champion.

Share your learnings internally using data, charts, results, and emphasizing company-wide impact. You’re not going to get buy in on day one, but as you move up the chain of command, earning more and more supporters, your budget will allow for more machine learning solutions. Utilize buzzwords and visual representations of the project – remember data science needs to be seen, touched, and felt.

Ensure ML and data science success with best practices for introducing, completing, and repeating implementation. 2nd Watch Data and Analytic Solutions help your organization realize the power of ML with proper data cleaning, the right algorithm selection, and quality model deployment. Contact Us to see how you can do more with the data you have.

-Sam Tawfik, Sr Marketing Manager, Data & Analytics

Understanding Data Science, Artificial Intelligence, and Machine Learning

Amazing possibilities are available in data science with artificial intelligence (AI) and machine learning (ML). Large sets of data, inexpensive storage options, and cloud processing capabilities are enabling computers to make human-like decisions. Across industries, businesses are leveraging these algorithm-based models to save time, reduce costs, enable users, and grow profits.

What’s the difference between Data Science, Artificial Intelligence, and Machine Learning

Data science, AI, and ML can get lumped together, but there are some distinctions to understand. Simply put, AI is a computer doing things that typically would require human scrutiny or reasoning. ML is the application of statistical learning techniques to automatically learn patterns in data. These patterns are used to develop a model to make more accurate predictions about the world. And both terms utilize data science to accomplish outcomes.

With these central terms defined, we recommend using ‘machine learning’ or ‘ML’ to describe data science projects internally because there is sometimes an aura of fear around AI that “the robots are going to take my job.” Although joking (a bit), buy-in from executives is critical to a successful data project, so ML is recommended over AI.

Utilizing Machine Learning for Profit Growth

A recent study showed 78% of companies have already deployed ML, and 90% of them have made more money as a result. Manufacturing and supply-chain management are experiencing the largest average cost decrease, and marketing, sales, product and service development are reaching the highest average revenue gains. Additionally, a McKinsey survey revealed that organizations with a high diffusion of ML had 4-5% higher profit margins than their peers with no ML. Not only can ML reduce your overall costs, but it also enables you to grow your bottom line. If your organization is not utilizing ML, now is the time to start.

From Data to Model

Machine learning is already a staple in many of the functions we utilize daily. Predictive search in Google and within catalogues, fraud detection on suspicious credit card purchases, near-instant credit approval, social network suggestions via mutual connections, and voice recognition are all common today. Behind these intelligent decisions is a model that acts as a function or program. The model is trained on sample data using a machine learning algorithm to learn patterns. Based on the information learned about the sample data, the model is applied to inputs it may or may not have seen before and predicts an outcome.

Traditional programming depends on the written program and the input data it’s fed. The computer runs the program against the data, and you get an output directly tied to the logic or function of the program. Only the data that can be processed by the program gets analyzed, and outliers are removed.

In ML, the computer is still given input data. For example, what you know about your customer – time stamps, demographics, spend, etc. – but it doesn’t have a written program. Instead, it’s given the output you desire. For example, you might want to know which customers churn. Then you build a model by training programmed algorithms to analyze input data and predict an output. Essentially, the model recognizes the correlation between the output results and the input data. Here, the model utilizes algorithms to identify patterns in data that that heavily influence the customer churn score.

In this example, an organization might discover that most customers stop doing business with them after a certain promo ends, or a high percentage of customers who come in through a specific lead gen pipeline don’t stay for long. Using this information, the organization can make informed and specific decisions about how to reduce churn based on known patterns.

All relevant data is taken into account in ML to deliver a more comprehensive story about why things are happening in your organization. Machine learning can quickly affirm or discredit intuition and allow organizations to fail faster, and in the right direction, to meet overall goals more efficiently.

A best practices approach is necessary to streamline the process of introducing, completing, and repeating a data science project. With 2nd Watch Data and Analytics Services, you realize the power of machine learning with the right algorithm selection and model deployment. Contact Us to see how machine learning can positively impact your organization or download our eBook, “Artificial Intelligence and Machine Learning: 3 Steps to Set the Table for Data Science in 2021” to learn about the 3 steps necessary to producing valid and applicable results from your data science project.

– Rob Whelan, Practice Director, Data & Analytics