A company is only as strong as its crew, so we are driven to foster and create a work environment that is motivating, inspiring, and open.
Through transparency, communication, and open feedback, we bring every department together, working towards the same goal.
Everyone has valuable ideas, and we want your unique perspective on how to make Blacklane an even better place to work. This is your chance to make your mark and be part of the Blacklane journey! Create a personal development plan. Set goals, learn new skills, and connect with a mentor to help you reach what you want to achieve. Budget for online courses, conferences, and books bolsters your learning journey.
Discover a company culture where open feedback and communication is valued. We offer monthly chats with one of our C-level team members — Jens, Frank, and Sascha — as we believe feedback makes Blacklane a better place to work. Get to know your fellow Blacklaners! Once a month, everyone is randomly sorted into small groups and given a budget to go for lunch.
This strengthens bonds across departments, and opens the flow of communication. From Friday evening drinks at the office, to twice-a-year department events with a budget for every team, we offer plenty of opportunities to connect with your team and build relationships. And not to brag, but our summer and winter parties are legendary.Cozy maid from tucson
Ensure your work life and home life exist in harmony. Find out what works for you and your team with flexible working hours, and home office days. Core working hours cater to both the early birds and night owls. Provide a service that customers can rely on. Be someone your colleagues can count on. Work only with reliable drivers that we trust. Earn trust and keep it. Look for ways to iterate and improve. Encourage curiosity. Learn quickly from mistakes and grow from the experience. Aim to be better every day.
Seek to create an amazing service, be the industry leader and smash customer expectations. Care about passengers, drivers and each other - about our community and how we give back. Care about making our product and service exceptional.I called back to say that a new card should be in there within 24 hours and to apologize for the mix up.
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They have asked for the same items over and over for months, even once they had the information they were asking for earlier in the same email chain. The only reason I still have the account is because they are so slow on porting out the numbers, that I have no choice but to keep paying.
They fail on all fronts. There are way too many good options out there. Simple layout in the administrative panel that allows for easy setup and configuration for new users. Also great mobile solutions for when employees are out of the office. The few times that I actually do need to call customer service, they are always happy to help and never give up on finding a solution.
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If your business is in the search for nothing but the best, then Nextiva should be the first option you consider. Originally founded by CEO Tomas Gorny in 2006, Nextiva has yet to slow down with year after year of innovation.
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With a reputation for one of the top companies to work for, Nextiva offers a warm and positive atmosphere that even extends to their client base. Nextiva has won multiple awards for their outstanding U. Innovation is also something Nextiva is highly regarded for, as the provider is constantly changing the landscape and introducing revolutionary solutions and products.
Their next generation call center platform, NextOS and data analytic solutions put Nextiva in a class of their own.To be able to match the returned predictions with your input instances, you must have instance keys defined. An instance key is a value that every instance has that is unique among the instances in a set of data.Climate and disaster risk screening tools
The simplest key is an index number. You should pass the keys through your graph unaltered in your training application. If your data doesn't already have instance keys, you can add them as part of your data preprocessing.
As new versions of Cloud ML Engine are released, it is possible that models developed against older versions will become obsolete. This is particularly pertinent if you arrive at an effective model version that remains unchanged for a long period. You should review the Cloud ML Engine versioning policy and make sure that you understand the Cloud ML Engine runtime version that you use to train your model versions.
You can specify a supported Cloud ML Engine runtime version when you create a model version. Doing so establishes the model version's default setting. If you don't specify one explicitly, Cloud ML Engine creates your version using the current default runtime version (typically the most recent stable version). You can specify a runtime version to use when you start a batch prediction job. This is to accommodate getting predictions using a model that is not deployed on Cloud ML Engine.
You should never use a different runtime version than the default for a deployed model. Doing so is likely to cause unexpected errors. You cannot request online predictions from models outside of Cloud ML Engine, so there is no option to override the default runtime version in your request. The default runtime version set for a model version cannot be changed. To specify a different runtime version for a model version, deploy a new version using the same training artifacts that you used initially.
Google Cloud Platform uses zones and regions to define the geographic locations of physical computing resources. Cloud ML Engine uses regions to designate its processing.
When you deploy a model for prediction, you specify the default region that you want prediction to run in. When you start a batch prediction job, you can specify a region to run the job in, overriding the default region.
Online predictions are always served from the region set when the model was created.See the Section on filtering sources for more details. Each new field is created using a Flatline expression and optionally a name, label, and description. A Flatline expression is a lisp-like expresion that allows you to make references and process columns and rows of the origin dataset.
See the full Flatline reference here. Let's see a first example that clones a dataset and adds a new field named "Celsius" to it using an expression that converts the values from the "Fahrenheit" field to Celsius.
A new field can actually generate multiple fields. In that case their names can be specified using the names arguments. In addition to horizontally selecting different fields in the same row, you can keep the field fixed and select vertical windows of its value, via the window and related operators. For example, the following request will generate a new field using a sliding window of 7 values for the field named "Fahrenheit" and will also generate two additional fields named "Yesterday" and "Tomorrow" with the previous and next value of the current row for the field 0.
The list of values generated from each input row that way constitutes an output row of the generated dataset. See the table below for more details. See the Section on filtering rows for more details.
Example: "description": "This field is a transformation" descriptions optional Array A description for every of the new fields generated. Example: "fields": "(window Price -2 0)" label optional Array Label of the new field. Example: "label": "New price" labels Array Labels for each of the new fields generated. Example: "name": "Price" names optional Array Names for each of the new fields generated.
Basically, a Flatline expresion can easily be translated to its json-like variant and vice versa by just changing parentheses to brackets, symbols to quoted strings, and adding commas to separate each sub-expression. For example, the following two expressions are the same for BigML. If you specify both sampling and filtering arguments, the former are applied first. As with filters applied to datasources, dataset filters can use the full Flatline language to specify the boolean expression to use when sifting the input.
Flatline performs type inference, and will in general figure out the proper optype for the generated fields, which are subsequently summarized by the dataset creation process, reaching then their final datatype (just as with a regular dataset created from a datasource). In case you need to fine-tune Flatline's inferences, you can provide an optype (or optypes) key and value in the corresponding output field entry (together with generator and names), but in general this shouldn't be needed.
Samples Last Updated: Monday, 2017-10-30 10:31 A sample provides fast-access to the raw data of a dataset on an on-demand basis. When a new sample is requested, a copy of the dataset is stored in a special format in an in-memory cache. Multiple and different samples of the data can then be extracted using HTTPS parameterized requests by sampling sizes and simple query string filters.
That is to say, a sample will be available as long as GETs are requested within periods smaller than a pre-established TTL (Time to Live).
The expiration timer of a sample is reset every time a new GET is received. If requested, a sample can also perform linear regression and compute Pearson's and Spearman's correlations for either one numeric field against all other numeric fields or between two specific numeric fields. You can also list all of your samples. You can also use curl to customize a new sample with a name. Once a sample has been successfully created it will have the following properties.
Through the status field in the sample you can determine when the sample has been fully processed and ready to be used. Thus when retrieving a sample, it's possible to specify that only a subset of fields be retrieved, by using any combination of the following parameters in the query string (unrecognized parameters are ignored): Fields Filter Parameters Parameter TypeDescription fields optional Comma-separated list A comma-separated list of field IDs to retrieve.
A sample might be composed of thousands or even millions of rows. Thus when retrieving a sample, it's possible to specify that only a subset of rows be retrieved, by using any combination of the following parameters in the query string (unrecognized parameters are ignored). BigML will never return more than 1000 rows in the same response.Norsika (4) odds Scratched 2.
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How does the provider get rated. Only businesses who use the specially developed eKomi software to collect real feedback can obtain the eKomi Seal of Approval. Didn't have any connection problems that a simple reboot couldn't solve. Battery life was only about 4-6 hours, but I already carry an external charger so recharging on the go wasn't an issue.
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My only complaint is that the data usage counter on the hotspot itself seemed to reset at some point. We were nowhere close to going over the data limit, but I was just curious how much we had used - and couldn't really tell. However my last rental was often times frustrating. Despite this I would consider renting from them again depending on the country I was traveling. In France the service was definitely amazing. Overall the unit worked well and we were happy with it. My only complaint is that there was an issue our first day using it - the SIM wasn't registering on the network and I had to call Hippo for resolution which was an added expense given that I got the unit to save on these types of network charges.
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I'll use Hippo again for future travels to Europe. Overall the network connectivity was good. However I payed for the unlimited data plan and it ran out about half way through our trip.
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Candles may be pretty to look at but they are a cause of home fires and home fire deaths. Remember, a candle is an open flame, which means that it can easily ignite anything that can burn.
Tip sheets in multiple languages Easy-to-read tip sheets Featured safety tip sheetsWinter holiday safetyWinter holidays are a time for families and friends to get together. Candle safetyCandles may be pretty to look at but they are a cause of home fires and home fire deaths. Download the tip sheet. Get the latest trends, offers, and insights when you sign-up on the Videoblocks blog. November 27, 2017 By Natasha WarcholakThe hardest part is already over. But, in this case, the end is actually just the beginning of post-production, that is.
When creating a cinematic trailer templates for inspiration. A great way to do this is by Read more about Lights, Camera, Preview: How To Showcase Your Movie Like A ProFiled Under: Video Editing Tutorials Tagged With: After Effects Tutorial, cinematic trailer templates, filmmaking, movie trailer templates, stock video, trailer templatesNovember 17, 2017 By Natasha WarcholakPart of what makes the occasional polar bear encounter. Our new Purchase this polar wildlife video. Purchase this Arctic Owl video.
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