Azure Training Classes in Fountain Valley, California
Learn Azure in Fountain Valley, California and surrounding areas via our hands-on, expert led courses. All of our classes either are offered on an onsite, online or public instructor led basis. Here is a list of our current Azure related training offerings in Fountain Valley, California: Azure Training
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20 May, 2024 - 21 May, 2024 - RED HAT SATELLITE V6 (FOREMAN/KATELLO) ADMINISTRATION
24 June, 2024 - 27 June, 2024 - Ruby on Rails
22 August, 2024 - 23 August, 2024 - VMware vSphere 8.0 Boot Camp
10 June, 2024 - 14 June, 2024 - Docker
22 July, 2024 - 24 July, 2024 - See our complete public course listing
Blog Entries publications that: entertain, make you think, offer insight
Data has always been important to business. While it wasn't long ago that businesses kept minimal information on people who bought their products, nowadays companies keep vast amounts of data. In the late 20th century, marketers began to take demographics seriously. It was hard to keep track of so much information without the help of computers.
Only large companies in the '60s and '70s could afford the research necessary to deliver real marketing insight. The marketers of yesteryear relied upon focus groups and expensive experiments to gauge consumer behavior in a controlled environment. Today even the smallest of companies can have access to a rich array of real-world data about their consumers' behavior and their consumers. The amount of data that is stored today dwarfs the data of only a few years ago by several orders of magnitude.
So what kind of information are businesses storing for marketing purposes? Some examples include:
- Demographic information — age, gender, ethnicity, education, occupation and various other individual characteristics.
I’ve been a technical recruiter for several years, let’s just say a long time. I’ll never forget how my first deal went bad and the lesson I learned from that experience. I was new to recruiting but had been a very good sales person in my previous position. I was about to place my first contractor on an assignment. I thought everything was fine. I nurtured and guided my candidate through the interview process with constant communication throughout. The candidate was very responsive throughout the process. From my initial contact with him, to the phone interview all went well and now he was completing his onsite interview with the hiring manager.
Shortly thereafter, I received the call from the hiring manager that my candidate was the chosen one for the contract position, I was thrilled. All my hard work had paid off. I was going to be a success at this new game! The entire office was thrilled for me, including my co-workers and my bosses. I made a good win-win deal. It was good pay for my candidate and a good margin for my recruiting firm. Everyone was happy.
I left a voicemail message for my candidate so I could deliver the good news. He had agreed to call me immediately after the interview so I could get his assessment of how well it went. Although, I heard from the hiring manager, there was no word from him. While waiting for his call back, I received a call from a Mercedes dealership to verify his employment for a car he was trying to lease. Technically he wasn’t working for us as he had not signed the contract yet…. nor, had he discussed this topic with me. I told the Mercedes office that I would get back to them. Still not having heard back from the candidate, I left him another message and mentioned the call I just received. Eventually he called back. He wanted more money.
I told him that would be impossible as he and I had previously agreed on his hourly rate and it was fine with him. I asked him what had changed since that agreement. He said he made had made much more money in doing the same thing when he lived in California. I reminded him this is a less costly marketplace than where he was living in California. I told him if he signed the deal I would be able to call the car dealership back and confirm that he was employed with us. He agreed to sign the deal.
I suspect that many of you are familiar with the term "hard coding a value" whereby the age of an individual or their location is written into the condition (or action) of a business rule (in this case) as shown below:
if customer.age > 21 and customer.city == 'denver'
then ...
Such coding practices are perfectly expectable provided that the conditional values, age and city, never change. They become entirely unacceptable if a need for different values could be anticipated. A classic example of where this practice occurred that caused considerable heartache in the IT industry was the Y2K issue where dates were updated using only the last 2 digits of a four digit number because the first 2 digits were hard-coded to 19 i.e. 1998, 1999. All was well provided that the date did not advance to a time beyond the 1900’s since no one could be certain of what would happen when the millennia arrived (2000). A considerably amount of work (albeit boring) and money, approximately $200 billion, went into revising systems by way of software rewrites and computer chip replacements in order to thwart any detrimental outcomes. It is obvious how a simple change or an assumption can have sweeping consequences.
You may wonder what Y2K has to do with Business Rule Management Systems (BRMS). Well, what if we considered rules themselves to be hard-coded. If we were to write 100s of rules in Java, .NET or whatever language that only worked for a given scenario or assumption, would that not constitute hard-coded logic? By hard-coded, we obviously mean compiled. For example, if a credit card company has a variety of bonus campaigns, each with their own unique list of rules that may change within a week’s time, what would be the most effective way of writing software to deal with these responsibilities?
Machine learning systems are equipped with artificial intelligence engines that provide these systems with the capability of learning by themselves without having to write programs to do so. They adjust and change programs as a result of being exposed to big data sets. The process of doing so is similar to the data mining concept where the data set is searched for patterns. The difference is in how those patterns are used. Data mining's purpose is to enhance human comprehension and understanding. Machine learning's algorithms purpose is to adjust some program's action without human supervision, learning from past searches and also continuously forward as it's exposed to new data.
The News Feed service in Facebook is an example, automatically personalizing a user's feed from his interaction with his or her friend's posts. The "machine" uses statistical and predictive analysis that identify interaction patterns (skipped, like, read, comment) and uses the results to adjust the News Feed output continuously without human intervention.
Impact on Existing and Emerging Markets
The NBA is using machine analytics created by a California-based startup to create predictive models that allow coaches to better discern a player's ability. Fed with many seasons of data, the machine can make predictions of a player's abilities. Players can have good days and bad days, get sick or lose motivation, but over time a good player will be good and a bad player can be spotted. By examining big data sets of individual performance over many seasons, the machine develops predictive models that feed into the coach’s decision-making process when faced with certain teams or particular situations.
General Electric, who has been around for 119 years is spending millions of dollars in artificial intelligence learning systems. Its many years of data from oil exploration and jet engine research is being fed to an IBM-developed system to reduce maintenance costs, optimize performance and anticipate breakdowns.
Over a dozen banks in Europe replaced their human-based statistical modeling processes with machines. The new engines create recommendations for low-profit customers such as retail clients, small and medium-sized companies. The lower-cost, faster results approach allows the bank to create micro-target models for forecasting service cancellations and loan defaults and then how to act under those potential situations. As a result of these new models and inputs into decision making some banks have experienced new product sales increases of 10 percent, lower capital expenses and increased collections by 20 percent.
Emerging markets and industries
By now we have seen how cell phones and emerging and developing economies go together. This relationship has generated big data sets that hold information about behaviors and mobility patterns. Machine learning examines and analyzes the data to extract information in usage patterns for these new and little understood emergent economies. Both private and public policymakers can use this information to assess technology-based programs proposed by public officials and technology companies can use it to focus on developing personalized services and investment decisions.
Machine learning service providers targeting emerging economies in this example focus on evaluating demographic and socio-economic indicators and its impact on the way people use mobile technologies. The socioeconomic status of an individual or a population can be used to understand its access and expectations on education, housing, health and vital utilities such as water and electricity. Predictive models can then be created around customer's purchasing power and marketing campaigns created to offer new products. Instead of relying exclusively on phone interviews, focus groups or other kinds of person-to-person interactions, auto-learning algorithms can also be applied to the huge amounts of data collected by other entities such as Google and Facebook.
A warning
Traditional industries trying to profit from emerging markets will see a slowdown unless they adapt to new competitive forces unleashed in part by new technologies such as artificial intelligence that offer unprecedented capabilities at a lower entry and support cost than before. But small high-tech based companies are introducing new flexible, adaptable business models more suitable to new high-risk markets. Digital platforms rely on algorithms to host at a low cost and with quality services thousands of small and mid-size enterprises in countries such as China, India, Central America and Asia. These collaborations based on new technologies and tools gives the emerging market enterprises the reach and resources needed to challenge traditional business model companies.
Tech Life in California
| Company Name | City | Industry | Secondary Industry |
|---|---|---|---|
| Mattel, Inc. | El Segundo | Retail | Sporting Goods, Hobby, Book, and Music Stores |
| Spectrum Group International, Inc. | Irvine | Retail | Retail Other |
| Chevron Corp | San Ramon | Energy and Utilities | Gasoline and Oil Refineries |
| Jacobs Engineering Group, Inc. | Pasadena | Real Estate and Construction | Construction and Remodeling |
| eBay Inc. | San Jose | Software and Internet | E-commerce and Internet Businesses |
| Broadcom Corporation | Irvine | Computers and Electronics | Semiconductor and Microchip Manufacturing |
| Franklin Templeton Investments | San Mateo | Financial Services | Investment Banking and Venture Capital |
| Pacific Life Insurance Company | Newport Beach | Financial Services | Insurance and Risk Management |
| Tutor Perini Corporation | Sylmar | Real Estate and Construction | Construction and Remodeling |
| SYNNEX Corporation | Fremont | Software and Internet | Data Analytics, Management and Storage |
| Core-Mark International Inc | South San Francisco | Manufacturing | Food and Dairy Product Manufacturing and Packaging |
| Occidental Petroleum Corporation | Los Angeles | Manufacturing | Chemicals and Petrochemicals |
| Yahoo!, Inc. | Sunnyvale | Software and Internet | Software and Internet Other |
| Edison International | Rosemead | Energy and Utilities | Gas and Electric Utilities |
| Ingram Micro, Inc. | Santa Ana | Computers and Electronics | Consumer Electronics, Parts and Repair |
| Safeway, Inc. | Pleasanton | Retail | Grocery and Specialty Food Stores |
| Gilead Sciences, Inc. | San Mateo | Healthcare, Pharmaceuticals and Biotech | Pharmaceuticals |
| AECOM Technology Corporation | Los Angeles | Real Estate and Construction | Architecture,Engineering and Design |
| Reliance Steel and Aluminum | Los Angeles | Manufacturing | Metals Manufacturing |
| Live Nation, Inc. | Beverly Hills | Media and Entertainment | Performing Arts |
| Advanced Micro Devices, Inc. | Sunnyvale | Computers and Electronics | Semiconductor and Microchip Manufacturing |
| Pacific Gas and Electric Corp | San Francisco | Energy and Utilities | Gas and Electric Utilities |
| Electronic Arts Inc. | Redwood City | Software and Internet | Games and Gaming |
| Oracle Corporation | Redwood City | Software and Internet | Software and Internet Other |
| Symantec Corporation | Mountain View | Software and Internet | Data Analytics, Management and Storage |
| Dole Food Company, Inc. | Thousand Oaks | Manufacturing | Food and Dairy Product Manufacturing and Packaging |
| CBRE Group, Inc. | Los Angeles | Real Estate and Construction | Real Estate Investment and Development |
| First American Financial Corporation | Santa Ana | Financial Services | Financial Services Other |
| The Gap, Inc. | San Francisco | Retail | Clothing and Shoes Stores |
| Ross Stores, Inc. | Pleasanton | Retail | Clothing and Shoes Stores |
| Qualcomm Incorporated | San Diego | Telecommunications | Wireless and Mobile |
| Charles Schwab Corporation | San Francisco | Financial Services | Securities Agents and Brokers |
| Sempra Energy | San Diego | Energy and Utilities | Gas and Electric Utilities |
| Western Digital Corporation | Irvine | Computers and Electronics | Consumer Electronics, Parts and Repair |
| Health Net, Inc. | Woodland Hills | Healthcare, Pharmaceuticals and Biotech | Healthcare, Pharmaceuticals, and Biotech Other |
| Allergan, Inc. | Irvine | Healthcare, Pharmaceuticals and Biotech | Biotechnology |
| The Walt Disney Company | Burbank | Media and Entertainment | Motion Picture and Recording Producers |
| Hewlett-Packard Company | Palo Alto | Computers and Electronics | Consumer Electronics, Parts and Repair |
| URS Corporation | San Francisco | Real Estate and Construction | Architecture,Engineering and Design |
| Cisco Systems, Inc. | San Jose | Computers and Electronics | Networking Equipment and Systems |
| Wells Fargo and Company | San Francisco | Financial Services | Banks |
| Intel Corporation | Santa Clara | Computers and Electronics | Semiconductor and Microchip Manufacturing |
| Applied Materials, Inc. | Santa Clara | Computers and Electronics | Semiconductor and Microchip Manufacturing |
| Sanmina Corporation | San Jose | Computers and Electronics | Semiconductor and Microchip Manufacturing |
| Agilent Technologies, Inc. | Santa Clara | Telecommunications | Telecommunications Equipment and Accessories |
| Avery Dennison Corporation | Pasadena | Manufacturing | Paper and Paper Products |
| The Clorox Company | Oakland | Manufacturing | Chemicals and Petrochemicals |
| Apple Inc. | Cupertino | Computers and Electronics | Consumer Electronics, Parts and Repair |
| Amgen Inc | Thousand Oaks | Healthcare, Pharmaceuticals and Biotech | Biotechnology |
| McKesson Corporation | San Francisco | Healthcare, Pharmaceuticals and Biotech | Pharmaceuticals |
| DIRECTV | El Segundo | Telecommunications | Cable Television Providers |
| Visa, Inc. | San Mateo | Financial Services | Credit Cards and Related Services |
| Google, Inc. | Mountain View | Software and Internet | E-commerce and Internet Businesses |
training details locations, tags and why hsg
The Hartmann Software Group understands these issues and addresses them and others during any training engagement. Although no IT educational institution can guarantee career or application development success, HSG can get you closer to your goals at a far faster rate than self paced learning and, arguably, than the competition. Here are the reasons why we are so successful at teaching:
- Learn from the experts.
- We have provided software development and other IT related training to many major corporations in California since 2002.
- Our educators have years of consulting and training experience; moreover, we require each trainer to have cross-discipline expertise i.e. be Java and .NET experts so that you get a broad understanding of how industry wide experts work and think.
- Discover tips and tricks about Azure programming
- Get your questions answered by easy to follow, organized Azure experts
- Get up to speed with vital Azure programming tools
- Save on travel expenses by learning right from your desk or home office. Enroll in an online instructor led class. Nearly all of our classes are offered in this way.
- Prepare to hit the ground running for a new job or a new position
- See the big picture and have the instructor fill in the gaps
- We teach with sophisticated learning tools and provide excellent supporting course material
- Books and course material are provided in advance
- Get a book of your choice from the HSG Store as a gift from us when you register for a class
- Gain a lot of practical skills in a short amount of time
- We teach what we know…software
- We care…
Interesting Reads Take a class with us and receive a book of your choosing for 50% off MSRP.
