Tech Avidus

Tech Avidus

1619153309

Tech's Site on Strikingly

Education is an industry vertical that is quickly improving over time. We offer the most advanced eLearning Application Development Service in the area of education for our clients.

#elearningapp #educationapp #elearningdevelopment #elearningapplication

What is GEEK

Buddha Community

Tech's Site on Strikingly
Ahebwe  Oscar

Ahebwe Oscar

1623192840

Does My Site Work?

EPISODE SUMMARY

On this episode, we will discuss how you can verify that your site works and continues to work. We’re digging into automated testing and how to write tests for your Django apps.

Full show notes are available at https://www.mattlayman.com/django-riffs/13.

EPISODE NOTES

Full show notes are available at https://www.mattlayman.com/django-riffs/13.

#does my site work? #your site is #episode summary #episode notes #the site. #my site work

Ashley Troy

1618240774

Role of Professional Brand Consulters to Make Your Brand Successful

Each company wishes to remain present in the industry, and for that purpose, it indulges in user profile creation sites list to become popular in the online field. To achieve this, however, you need robust and productive strategies to maintain your existing customers’ loyalty. These plans should also attract new customers at precisely the same time.

Only the latest consulting firms are effective enough to observe the suitable approaches that can help you transform your business into your favourite brand. At Vowels Advertising in Dubai, Abu Dhabi, we offer various courses and suggestions to help your business and building to be effective by becoming a brand consultant. Branding advisers build new communication methods and a new identity. These advisers offer the brand of goods as required. They research your brand, which is made up of its identity and values. Brand Advisors have fantastic ideas and approaches to branding your products.

The most significant advantage of hiring a new consulting firm is that it has years of experience combined with the latest construction approaches. Since then, such companies are used to market trends and are continually being improved; they can quickly assess the competition and track competitor companies. In this way, they can analyze the manufacturer’s promotional requirements and evaluate the market to target a new product more effectively.

None of the other popular brands you see in the market today have started on a large scale right from the start. They achieved their present position thanks to ongoing trials and planned procedures. The whole long-term strategy was to introduce a new identity, place it precisely in the current market, and strengthen it by copying it to acceptable masses. Planning these elements and then implementing them appropriately is not a simple job. That is why companies from all over the world employ new consulting companies to implement the project.

If best, a new consulting firm can be tricky as there are an endless number of firms offering such services? The key to finding the perfect supplier for you is to analyze your condition first. As soon as you write down all the things, you can compare providers on the Internet and look for any gaps that may be useful to you. Also, start looking for reviews and connect with all previous customers to find out how satisfied they were with all the services.

By opting for Enterprises, they are responsible for creating the perfect brand identity for virtually any business, placing or repositioning it according to the market in which it operates, designing campaigns that advertise the business professionally and economically, and conducting thorough target market, competition, and market research. Thanks to all these activities, companies help to establish a small business and increase its visibility on the market. In the case of your small business, you can undoubtedly see it grow and reach a wider audience. You can find suitable new employees for a consulting company.

So this is how you can hire professional brand consulters to promote your brand amongst the customers effectively.

#profile creation sites #profile creation sites 2021 #profile creation sites list #high pr profile creation sites #free profile creation sites list #high da profile creation sites

Sasha  Lee

Sasha Lee

1650643200

Tech Ml Dataset: A Clojure Library for Data Processing and ML

tech.ml.dataset

tech.ml.dataset is a Clojure library for data processing and machine learning. Datasets are currently in-memory columnwise databases and we support parsing from file or input-stream. We support these formats: raw/gzipped csv/tsv, xls, xlsx, json, and sequences of maps as input sources. SQL and Clojurescript bindings are provided as separate libraries.

Data size in memory is minimized (primitive arrays), datetime types are often converted to an integer representation and strings are loaded into string tables. These features together dramatically decrease the working set size in memory. Because data is stored in columnar fashion columnwise operations on the dataset are very fast.

Conversion back into sequences of maps is very efficient and we have support for writing the dataset back out to csv, tsv, and gzipped varieties of those.

We have upgraded support for Apache Arrow. We have full support including mmap support for JDK-8->JDK-17 although if you are on an M-1 Mac you will need to use JDK-17. We also support per-column compression (LZ4, ZSTD) across all supported platforms. The official Arrow SDK does not support mmap, JDK-17, and has no user-accessible way to save a compressed streaming format file.

Large aggregations of potentially out-of-memory datasets are represented by a sequence of datasets. This is consistent with the design of the parquet and arrow data storage systems and aggregation operations involving large-scale datasets are efficiently implemented in the tech.v3.dataset.reductions namespace. We have started to integrate algorithms from the Apache Data Sketches system in the apache-data-sketch namespace. Summations/means in this area are implemented using the Kahan compensated summation algorithm.

Mini Walkthrough

user> (require '[tech.v3.dataset :as ds])
nil
;; We support many file formats
user> (def csv-data (ds/->dataset "https://github.com/techascent/tech.ml.dataset/raw/master/test/data/stocks.csv"))
#'user/csv-data
user> (ds/head csv-data)
test/data/stocks.csv [5 3]:

| symbol |       date | price |
|--------|------------|-------|
|   MSFT | 2000-01-01 | 39.81 |
|   MSFT | 2000-02-01 | 36.35 |
|   MSFT | 2000-03-01 | 43.22 |
|   MSFT | 2000-04-01 | 28.37 |
|   MSFT | 2000-05-01 | 25.45 |

;; tech.v3.libs.poi registers xls, tech.v3.libs.fastexcel registers xlsx.  If you want
;; to use poi for everything use workbook->datasets in the tech.v3.libs.poi namespace.
user> (require '[tech.v3.libs.poi])
nil
user> (def xls-data (ds/->dataset "https://github.com/techascent/tech.ml.dataset/raw/master/test/data/file_example_XLS_1000.xls"))
#'user/xls-data
user> (ds/head xls-data)
https://github.com/techascent/tech.v3.dataset/raw/master/test/data/file_example_XLS_1000.xls [5 8]:

| column-0 | First Name | Last Name | Gender |       Country |  Age |       Date |     Id |
|----------|------------|-----------|--------|---------------|------|------------|--------|
|      1.0 |      Dulce |     Abril | Female | United States | 32.0 | 15/10/2017 | 1562.0 |
|      2.0 |       Mara | Hashimoto | Female | Great Britain | 25.0 | 16/08/2016 | 1582.0 |
|      3.0 |     Philip |      Gent |   Male |        France | 36.0 | 21/05/2015 | 2587.0 |
|      4.0 |   Kathleen |    Hanner | Female | United States | 25.0 | 15/10/2017 | 3549.0 |
|      5.0 |    Nereida |   Magwood | Female | United States | 58.0 | 16/08/2016 | 2468.0 |

;;And you have fine grained control over parsing

user> (ds/head (ds/->dataset "https://github.com/techascent/tech.ml.dataset/raw/master/test/data/file_example_XLS_1000.xls"
                             {:parser-fn {"Date" [:local-date "dd/MM/yyyy"]}}))
https://github.com/techascent/tech.v3.dataset/raw/master/test/data/file_example_XLS_1000.xls [5 8]:

| column-0 | First Name | Last Name | Gender |       Country |  Age |       Date |     Id |
|----------|------------|-----------|--------|---------------|------|------------|--------|
|      1.0 |      Dulce |     Abril | Female | United States | 32.0 | 2017-10-15 | 1562.0 |
|      2.0 |       Mara | Hashimoto | Female | Great Britain | 25.0 | 2016-08-16 | 1582.0 |
|      3.0 |     Philip |      Gent |   Male |        France | 36.0 | 2015-05-21 | 2587.0 |
|      4.0 |   Kathleen |    Hanner | Female | United States | 25.0 | 2017-10-15 | 3549.0 |
|      5.0 |    Nereida |   Magwood | Female | United States | 58.0 | 2016-08-16 | 2468.0 |
user>


;;Loading from the web is no problem
user>
user> (def airports (ds/->dataset "https://raw.githubusercontent.com/jpatokal/openflights/master/data/airports.dat"
                                  {:header-row? false :file-type :csv}))
#'user/airports
user> (ds/head airports)
https://raw.githubusercontent.com/jpatokal/openflights/master/data/airports.dat [5 14]:

| column-0 |                                    column-1 |     column-2 |         column-3 | column-4 | column-5 |    column-6 |     column-7 | column-8 | column-9 | column-10 |            column-11 | column-12 |   column-13 |
|----------|---------------------------------------------|--------------|------------------|----------|----------|-------------|--------------|----------|----------|-----------|----------------------|-----------|-------------|
|        1 |                              Goroka Airport |       Goroka | Papua New Guinea |      GKA |     AYGA | -6.08168983 | 145.39199829 |     5282 |     10.0 |         U | Pacific/Port_Moresby |   airport | OurAirports |
|        2 |                              Madang Airport |       Madang | Papua New Guinea |      MAG |     AYMD | -5.20707989 | 145.78900147 |       20 |     10.0 |         U | Pacific/Port_Moresby |   airport | OurAirports |
|        3 |                Mount Hagen Kagamuga Airport |  Mount Hagen | Papua New Guinea |      HGU |     AYMH | -5.82678986 | 144.29600525 |     5388 |     10.0 |         U | Pacific/Port_Moresby |   airport | OurAirports |
|        4 |                              Nadzab Airport |       Nadzab | Papua New Guinea |      LAE |     AYNZ | -6.56980300 | 146.72597700 |      239 |     10.0 |         U | Pacific/Port_Moresby |   airport | OurAirports |
|        5 | Port Moresby Jacksons International Airport | Port Moresby | Papua New Guinea |      POM |     AYPY | -9.44338036 | 147.22000122 |      146 |     10.0 |         U | Pacific/Port_Moresby |   airport | OurAirports |

;;At any point you can get a sequence of maps back.  We implement a special version
;;of Clojure's APersistentMap that is much more efficient than even records and shares
;;the backing store with the dataset.

user> (take 2 (ds/mapseq-reader csv-data))
({"date" #object[java.time.LocalDate 0x4a998af0 "2000-01-01"],
  "symbol" "MSFT",
  "price" 39.81}
 {"date" #object[java.time.LocalDate 0x6d8c0bcd "2000-02-01"],
  "symbol" "MSFT",
  "price" 36.35})

;;Datasets are comprised of named columns, and provide a Clojure hashmap-compatible
;;collection.  Datasets allow reading and updating column data associated with a column name,
;;and provide a sequential view of [column-name column] entries.

;;You can look up columns via `get`, keyword lookup, and invoking the dataset as a function on
;;a key (a column name). `keys` and `vals` retrieve respective sequences of column names and columns.
;;The functions `assoc` and `dissoc` work to define new associations to conveniently
;;add, update, or remove columns, with add/update semantics defined by`tech.v3.dataset/add-or-update-column`.

;;Column data is stored in primitive arrays (even most datetimes!) and strings are stored
;;in string tables.  You can load really large datasets with this thing!

;;Columns themselves are sequences of their entries.
user> (csv-data "symbol")
#tech.v3.dataset.column<string>[560]
symbol
[MSFT, MSFT, MSFT, MSFT, MSFT, MSFT, MSFT, MSFT, MSFT, MSFT, MSFT, MSFT, MSFT, MSFT, MSFT, MSFT, MSFT, MSFT, MSFT, MSFT, ...]
user> (xls-data "Gender")
#tech.v3.dataset.column<string>[1000]
Gender
[Female, Female, Male, Female, Female, Male, Female, Female, Female, Female, Female, Male, Female, Male, Female, Female, Female, Female, Female, Female, ...]
user> (take 5 (xls-data "Gender"))
("Female" "Female" "Male" "Female" "Female")


;;Datasets and columns implement the clojure metadata interfaces (`meta`, `with-meta`, `vary-meta`)

;;You can access a sequence of columns of a dataset with `ds/columns`, or `vals` like a map,
;;and access the metadata with `meta`:

user> (->> csv-data
           vals  ;synonymous with ds/columns
           (map (fn [column]
                  (meta column))))
({:categorical? true, :name "symbol", :size 560, :datatype :string}
 {:name "date", :size 560, :datatype :packed-local-date}
 {:name "price", :size 560, :datatype :float32})

;;We can similarly destructure datasets like normal clojure
;;maps:

user> (for [[k column] csv-data]
        [k (meta column)])
(["symbol" {:categorical? true, :name "symbol", :size 560, :datatype :string}]
 ["date" {:name "date", :size 560, :datatype :packed-local-date}]
 ["price" {:name "price", :size 560, :datatype :float64}])

user> (let [{:strs [symbol date]} csv-data]
        [symbol (meta date)])
[#tech.v3.dataset.column<string>[560]
symbol
[MSFT, MSFT, MSFT, MSFT, MSFT, MSFT, MSFT, MSFT, MSFT, MSFT, MSFT, MSFT, MSFT, MSFT, MSFT, MSFT, MSFT, MSFT, MSFT, MSFT, ...]
 {:name "date", :size 560, :datatype :packed-local-date}]

;;We can get a brief description of the dataset:

user> (ds/brief csv-data)
({:min #object[java.time.LocalDate 0x5b2ea1d5 "2000-01-01"],
  :n-missing 0,
  :col-name "date",
  :mean #object[java.time.LocalDate 0x729b7395 "2005-05-12"],
  :datatype :packed-local-date,
  :quartile-3 #object[java.time.LocalDate 0x6c75fa43 "2007-11-23"],
  :n-valid 560,
  :quartile-1 #object[java.time.LocalDate 0x13d9aabe "2002-11-08"],
  :max #object[java.time.LocalDate 0x493bf7ef "2010-03-01"]}
 {:min 5.97,
  :n-missing 0,
  :col-name "price",
  :mean 100.7342857142857,
  :datatype :float64,
  :skew 2.4130946430619233,
  :standard-deviation 132.55477114107083,
  :quartile-3 100.88,
  :n-valid 560,
  :quartile-1 24.169999999999998,
  :max 707.0}
 {:mode "MSFT",
  :values ["MSFT" "AMZN" "IBM" "AAPL" "GOOG"],
  :n-values 5,
  :n-valid 560,
  :col-name "symbol",
  :n-missing 0,
  :datatype :string,
  :histogram (["MSFT" 123] ["AMZN" 123] ["IBM" 123] ["AAPL" 123] ["GOOG" 68])})

;;Another view of that brief:

user> (ds/descriptive-stats csv-data)
https://github.com/techascent/tech.v3.dataset/raw/master/test/data/stocks.csv: descriptive-stats [3 10]:

| :col-name |          :datatype | :n-valid | :n-missing |       :min |      :mean | :mode |       :max | :standard-deviation |      :skew |
|-----------|--------------------|----------|------------|------------|------------|-------|------------|---------------------|------------|
|      date | :packed-local-date |      560 |          0 | 2000-01-01 | 2005-05-12 |       | 2010-03-01 |                     |            |
|     price |           :float64 |      560 |          0 |      5.970 |      100.7 |       |      707.0 |        132.55477114 | 2.41309464 |
|    symbol |            :string |      560 |          0 |            |            |  MSFT |            |                     |            |


;;There are analogues of the clojure.core functions that apply to dataset:
;;filter, group-by, sort-by.  These are all implemented efficiently.

;;You can add/remove/update columns, or use the map idioms of `assoc` and `dissoc`

user> (-> csv-data
          (assoc "always-ten" 10) ;scalar values are expanded as needed
          (assoc "random"   (repeatedly (ds/row-count csv-data) #(rand-int 100)))
          ds/head)
https://github.com/techascent/tech.v3.dataset/raw/master/test/data/stocks.csv [5 5]:

| symbol |       date | price | always-ten | random |
|--------|------------|-------|------------|--------|
|   MSFT | 2000-01-01 | 39.81 |         10 |     47 |
|   MSFT | 2000-02-01 | 36.35 |         10 |     35 |
|   MSFT | 2000-03-01 | 43.22 |         10 |     54 |
|   MSFT | 2000-04-01 | 28.37 |         10 |      6 |
|   MSFT | 2000-05-01 | 25.45 |         10 |     52 |

user> (-> csv-data
          (dissoc "price")
          ds/head)
https://github.com/techascent/tech.v3.dataset/raw/master/test/data/stocks.csv [5 2]:

| symbol |       date |
|--------|------------|
|   MSFT | 2000-01-01 |
|   MSFT | 2000-02-01 |
|   MSFT | 2000-03-01 |
|   MSFT | 2000-04-01 |
|   MSFT | 2000-05-01 |


;;since `conj` works as with clojure maps and sequences of map-entries or pairs,
;;you can use idioms like `reduce conj` or `into` to construct new datasets on the
;;fly with familiar clojure idioms:

user> (let [new-cols [["always-ten" 10] ["new-price" (map inc (csv-data "price"))]]
            new-data (into (dissoc csv-data "price") new-cols)]
            (ds/head new-data))
https://github.com/techascent/tech.v3.dataset/raw/master/test/data/stocks.csv [5 4]:

| symbol |       date | always-ten | new-price |
|--------|------------|------------|-----------|
|   MSFT | 2000-01-01 |         10 |     40.81 |
|   MSFT | 2000-02-01 |         10 |     37.35 |
|   MSFT | 2000-03-01 |         10 |     44.22 |
|   MSFT | 2000-04-01 |         10 |     29.37 |
|   MSFT | 2000-05-01 |         10 |     26.45 |

;;You can write out the result back to csv, tsv, and gzipped variations of those.

;;Joins (left, right, inner) are all implemented.

;;Columnwise arithmetic manipulations (+,-, and many more) are provided via the
;;tech.v2.datatype.functional namespace.

;;Datetime columns can be operated on - plus,minus, get-years, get-days, and
;;many more - uniformly via the tech.v2.datatype.datetime.operations namespace.

;;There is much more.  Please checkout the walkthough and try it out!

Arrow Support

JDK-17, compression and memory mapping are supported - Arrow api.

Parquet Support

Parquet now has first class support. That means we should be able to load most Parquet files and support their full range of datatypes.

More Documentation

Questions, Community

Further Reading


Author: techascent
Source Code: https://github.com/techascent/tech.ml.dataset
License: EPL-1.0 License

#machine-learning 

Madelyn  Frami

Madelyn Frami

1599927180

Here are the Important Differences Between SLI, SLO, and SLA

When embarking on your SRE journey, it can seem daunting to decipher all the acronyms. What are SLOs versus SLAs? What’s the difference between SLIs and SLOs? In this blog post, we’ll cover what SLI, SLO, and SLA mean and how they contribute to your reliability goals.

What’s the Difference Between SLI, SLO, and SLA?

Below are the definitions for each of these terms, as well as a brief description. Definitions are according to the Google SRE Handbook.

SLI: “a carefully defined quantitative measure of some aspect of the level of service that is provided.”

SLIs are a quantitative measure, typically provided through your APM platform. Traditionally, these refer to either latency or availability, which are defined as response times, including queue/wait time, in milliseconds. A collection of SLIs, or composite SLIs, are a group of SLIs attributed to a larger SLO. These indicators are points on a digital user journey that contribute to customer experience and satisfaction.

When a developer sets up SLIs measuring their service, they do them in two stages:

  1. SLIs that will directly impact the customer.
  2. SLIs that directly influence the health and the availability or the latency and performance of certain services.

Once you have SLIs set up, you move into your SLOs, which are targets against your SLI.

SLO: “a target value or range of values for a service level that is measured by an SLI. A natural structure for SLOs is thus SLI ≤ target, or lower bound ≤ SLI ≤ upper bound.”

Service level objectives become the common language that companies use that allows teams to set guardrails and incentives to drive high levels of service reliability.

Today many companies operate in a constantly reactive mode. They’re reacting to NPS scores, churn, or incidents. This is an expensive, unsustainable use of time, and resources, let alone the potentially irrecoverable damage to customer satisfaction and the business. SLOs give you the objective language and measure of how to prioritize reliability work for proactive service health.

SLAs: “an explicit or implicit contract with your users that includes consequences of the meeting (or missing) the SLOs they contain.”

Service level agreements are set by the business rather than engineers, SREs, or ops. When anything happens to an SLO, SLAs kick in; they’re the actions that are taken when your SLO fails and often result in financial or contractual consequences.

#devops #site reliability engineering #site reliability #site reliability engineer #site reliability engineering tools #service level agreements

Wilford  Pagac

Wilford Pagac

1599937200

How to Build Your SRE Team

As you implement SRE practices and culture at your organization, you’ll realize everyone has a part to play. From engineers setting SLOs to management upholding the virtue of blamelessness to marketing teams conducting retrospectives on email campaigns, there’s no part of an organization that doesn’t benefit from the SRE mentality.

However, while it’s not necessary to have people with the title of ‘SRE’ to successfully adopt the best practices of SRE, having people who are dedicated to stewardship of SRE practices is important to achieve reliability excellence. In this blog post, we’ll look at some of the many roles an SRE can play, and how to find people with those skill sets.

#devops #teams #site reliability engineering #site reliability #site reliability engineer #site reliability engineering tools