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2021 Information/AI Wage Survey – O’Reilly

In June 2021, we requested the recipients of our Information & AI Publication to reply to a survey about compensation. The outcomes gave us perception into what our subscribers are paid, the place they’re positioned, what industries they work for, what their issues are, and what types of profession growth alternatives they’re pursuing.

Whereas it’s sadly untimely to say that the survey occurred on the finish of the COVID-19 pandemic (although we will all hope), it occurred at a time when restrictions have been loosening: we have been beginning to exit in public, have events, and in some instances even attend in-person conferences. The outcomes then present a spot to begin fascinated with what impact the pandemic had on employment. There was a number of uncertainty about stability, significantly at smaller firms: Would the corporate’s enterprise mannequin proceed to be efficient? Would your job nonetheless be there in a yr? On the similar time, workers have been reluctant to search for new jobs, particularly if they’d require relocating—no less than based on the rumor mill. Had been these issues mirrored in new patterns for employment?

Be taught sooner. Dig deeper. See farther.

Government Abstract

  • The common wage for information and AI professionals who responded to the survey was $146,000.
  • The common change in compensation over the past three years was $9,252. This corresponds to an annual improve of two.25%. Nevertheless, 8% of the correspondents reported decreased compensation, and 18% reported no change.
  • We don’t see proof of a “nice resignation.” 22% of respondents stated they meant to vary jobs, roughly what we might have anticipated. Respondents appeared involved about job safety, in all probability due to the pandemic’s impact on the financial system.
  • Common compensation was highest in California ($176,000), adopted by Japanese Seaboard states like New York and Massachusetts.
  • Compensation for girls was considerably decrease than for males (84%). Salaries have been decrease no matter training or job title. Girls have been extra doubtless than males to have superior levels, significantly PhDs.
  • Many respondents acquired certifications. Cloud certifications, particularly in AWS and Microsoft Azure, have been most strongly related to wage will increase.
  • Most respondents participated in coaching of some kind. Studying new expertise and bettering outdated ones have been the commonest causes for coaching, although hireability and job safety have been additionally elements. Firm-provided coaching alternatives have been most strongly related to pay will increase.


The survey was publicized by way of O’Reilly’s Information & AI Publication and was restricted to respondents in america and the UK. There have been 3,136 legitimate responses, 2,778 from the US and 284 from the UK. This report focuses on the respondents from the US, with solely restricted consideration paid to these from the UK. A small variety of respondents (74) recognized as residents of the US or UK, however their IP addresses indicated that they have been positioned elsewhere. We didn’t use the information from these respondents; in apply, discarding this information had no impact on the outcomes.

Of the two,778 US respondents, 2,225 (81%) recognized as males, and 383 (14%) recognized as girls (as recognized by their most popular pronouns). 113 (4%) recognized as “different,” and 14 (0.5%) used “they.”

The outcomes are biased by the survey’s recipients (subscribers to O’Reilly’s Information & AI Publication). Our viewers is especially robust within the software program (20% of respondents), laptop {hardware} (4%), and laptop safety (2%) industries—over 25% of the entire. Our viewers can also be robust within the states the place these industries are concentrated: 42% of the US respondents lived in California (20%), New York (9%), Massachusetts (6%), and Texas (7%), although these states solely make up 27% of the US inhabitants.

Compensation Fundamentals

The common annual wage for workers who labored in information or AI was $146,000. Most salaries have been between $100,000 and $150,000 yearly (34%); the subsequent most typical wage tier was from $150,000 to $200,000 (26%). Compensation depended strongly on location, with common salaries highest in California ($176,000).

The common wage change over the previous three years was $9,252, which is 2.25% per yr (assuming a closing wage equal to the common). A small variety of respondents (8%) reported wage decreases, and 18% reported no change. Financial uncertainty brought on by the pandemic could also be answerable for the declines in compensation. 19% reported will increase of $5,000 to $10,000 over that interval; 14% reported will increase of over $25,000. A research by the IEEE means that the common wage for technical workers elevated 3.6% per yr, larger than our respondents indicated.

39% of respondents reported promotions previously three years, and 37% reported altering employers throughout that interval. 22% reported that they have been contemplating altering jobs as a result of their salaries hadn’t elevated throughout the previous yr. Is that this an indication of what some have known as a “nice resignation”? Frequent knowledge has it that technical workers change jobs each three to 4 years. LinkedIn and Certainly each suggest staying for no less than three years, although they observe that youthful workers change jobs extra usually. LinkedIn elsewhere states that the annual turnover price for expertise workers is 13.2%—which means that workers keep at their jobs for roughly seven and a half years. If that’s right, the 37% that modified jobs over three years appears about proper, and the 22% who stated they “intend to depart their job resulting from a scarcity of compensation improve” doesn’t appear overly excessive. Remember that intent to vary and precise change aren’t the identical—and that there are numerous causes to vary jobs other than wage, together with flexibility round working hours and dealing from residence.

64% of the respondents took half in coaching or obtained certifications previously yr, and 31% reported spending over 100 hours in coaching applications, starting from formal graduate levels to studying weblog posts. As we’ll see later, cloud certifications (particularly in AWS and Microsoft Azure) have been the most well-liked and appeared to have the most important impact on salaries.

The explanations respondents gave for collaborating in coaching have been surprisingly constant. The overwhelming majority reported that they needed to be taught new expertise (91%) or enhance current expertise (84%). Information and AI professionals are clearly inquisitive about studying—and that studying is self-motivated, not imposed by administration. Comparatively few (22%) stated that coaching was required by their job, and even fewer participated in coaching as a result of they have been involved about shedding their job (9%).

Nevertheless, there have been different motives at work. 56% of our respondents stated that they needed to extend their “job safety,” which is at odds with the low quantity who have been involved about shedding their job. And 73% reported that they engaged in coaching or obtained certifications to extend their “hireability,” which can counsel extra concern about job stability than our respondents would admit. The pandemic was a menace to many companies, and workers have been justifiably involved that their job may vanish after a foul pandemic-influenced quarter. A need for elevated hireability may additionally point out that we’ll see extra folks trying to change jobs within the close to future.

Lastly, 61% of the respondents stated that they participated in coaching or earned certifications as a result of they needed a wage improve or a promotion (“improve in job title/duties”). It isn’t stunning that workers see coaching as a path to promotion—particularly as firms that wish to rent in fields like information science, machine studying, and AI cope with a scarcity of certified workers. Given the issue of hiring experience from exterior, we anticipate an rising variety of firms to develop their very own ML and AI expertise internally utilizing coaching applications.

Salaries by Gender

To no person’s shock, our survey confirmed that information science and AI professionals are largely male. The variety of respondents tells the story by itself: solely 14% recognized as girls, which is decrease than we’d have guessed, although it’s roughly according to our convention attendance (again after we had dwell conferences) and roughly equal to different technical fields. A small quantity (5%) reported their most popular pronoun as “they” or Different, however this pattern was too small to attract any important comparisons about compensation.

Girls’s salaries have been sharply decrease than males’s salaries, averaging $126,000 yearly, or 84% of the common wage for males ($150,000). That differential held no matter training, as Determine 1 reveals: the common wage for a lady with a doctorate or grasp’s diploma was 82% of the wage for a person with an equal diploma. The distinction wasn’t fairly as excessive for folks with bachelor’s levels or who have been nonetheless college students, but it surely was nonetheless important: girls with bachelor’s levels or who have been college students earned 86% or 87% of the common wage for males. The distinction in salaries was biggest between individuals who have been self-taught: in that case, girls’s salaries have been 72% of males’s. An affiliate’s diploma was the one diploma for which girls’s salaries have been larger than males’s.

Determine 1. Girls’s and males’s salaries by diploma

Regardless of the wage differential, the next share of ladies had superior levels than males: 16% of ladies had a doctorate, versus 13% of males. And 47% of ladies had a grasp’s diploma, versus 46% of males. (If these percentages appear excessive, needless to say many professionals in information science and AI are escapees from academia.)

Girls’s salaries additionally lagged males’s salaries after we in contrast ladies and men with related job titles (see Determine 2). On the government stage, the common wage for girls was $163,000 versus $205,000 for males (a 20% distinction). On the director stage, the distinction was a lot smaller—$180,000 for girls versus $184,000 for males—and girls’s salaries have been really larger than these on the government stage. It’s straightforward to hypothesize about this distinction, however we’re at a loss to elucidate it. For managers, girls’s salaries have been $143,000 versus $154,000 for males (a 7% distinction).

Profession development can also be a problem: 18% of the ladies who participated within the survey have been executives or administrators, in contrast with 23% of the lads.

Determine 2. Girls’s and males’s salaries by job title

Earlier than shifting on from our consideration of the impact of gender on wage, let’s take a short take a look at how salaries modified over the previous three years. As Determine 3 reveals, the proportion of women and men respondents who noticed no change was just about an identical (18%). However extra girls than males noticed their salaries lower (10% versus 7%). Correspondingly, extra males noticed their salaries improve. Girls have been additionally extra more likely to have a smaller improve: 24% of ladies had a rise of underneath $5,000 versus 17% of males. On the excessive finish of the wage spectrum, the distinction between women and men was smaller, although nonetheless not zero: 19% of males noticed their salaries improve by over $20,000, however solely 18% of ladies did. So probably the most important variations have been within the midrange. One anomaly stands proud: a barely larger share of ladies than males obtained wage will increase within the $15,000 to $20,000 vary (8% versus 6%).

Determine 3. Change in wage for ladies and men over three years

Salaries by Programming Language

After we checked out the most well-liked programming languages for information and AI practitioners, we didn’t see any surprises: Python was dominant (61%), adopted by SQL (54%), JavaScript (32%), HTML (29%), Bash (29%), Java (24%), and R (20%). C++, C#, and C have been additional again within the checklist (12%, 12%, and 11%, respectively).

Discussing the connection between programming languages and wage is difficult as a result of respondents have been allowed to examine a number of languages, and most did. However after we regarded on the languages related to the best salaries, we acquired a considerably completely different checklist. Essentially the most extensively used and in style languages, like Python ($150,000), SQL ($144,000), Java ($155,000), and JavaScript ($146,000), have been solidly in the course of the wage vary. The outliers have been Rust, which had the best common wage (over $180,000), Go ($179,000), and Scala ($178,000). Different much less frequent languages related to excessive salaries have been Erlang, Julia, Swift, and F#. Net languages (HTML, PHP, and CSS) have been on the backside (throughout $135,000). See Determine 4 for the total checklist.

Determine 4. Wage vs. programming language

How will we clarify this? It’s tough to say that information and AI builders who use Rust command the next wage, since most respondents checked a number of languages. However we consider that this information reveals one thing important. The provision of expertise for newer languages like Rust and Go is comparatively small. Whereas there might not be an enormous demand for information scientists who use these languages (but), there’s clearly some demand—and with skilled Go and Rust programmers briefly provide, they command the next wage. Maybe it’s even easier: whatever the language somebody will use at work, employers interpret information of Rust and Go as an indication of competence and willingness to be taught, which will increase candidates’ worth. The same argument may be made for Scala, which is the native language for the extensively used Spark platform. Languages like Python and SQL are desk stakes: an applicant who can’t use them may simply be penalized, however competence doesn’t confer any particular distinction.

One shock is that 10% of the respondents stated that they didn’t use any programming languages. We’re undecided what which means. It’s potential they labored solely in Excel, which ought to be thought of a programming language however usually isn’t. It’s additionally potential that they have been managers or executives who not did any programming.

Salaries by Instrument and Platform

We additionally requested respondents what instruments they used for statistics and machine studying and what platforms they used for information analytics and information administration. We noticed a few of the similar patterns that we noticed with programming languages. And the identical warning applies: respondents have been allowed to pick out a number of solutions to our questions in regards to the instruments and platforms that they use. (Nevertheless, a number of solutions weren’t as frequent as for programming languages.) As well as, when you’re acquainted with instruments and platforms for machine studying and statistics, that the boundary between them is fuzzy. Is Spark a instrument or a platform? We thought of it a platform, although two Spark libraries are within the checklist of instruments. What about Kafka? A platform, clearly, however a platform for constructing information pipelines that’s qualitatively completely different from a platform like Ray, Spark, or Hadoop.

Simply as with programming languages, we discovered that probably the most extensively used instruments and platforms have been related to midrange salaries; older instruments, even when they’re nonetheless extensively used, have been related to decrease salaries; and a few of the instruments and platforms with the fewest customers corresponded to the best salaries. (See Determine 5 for the total checklist.)

The commonest responses to the query about instruments for machine studying or statistics have been “I don’t use any instruments” (40%) or Excel (31%). Ignoring the query of how one does machine studying or statistics with out instruments, we’ll solely notice that those that didn’t use instruments had a mean wage of $143,000, and Excel customers had a mean wage of $138,000—each beneath common. Stata ($120,000) was additionally on the backside of the checklist; it’s an older package deal with comparatively few customers and is clearly falling out of favor.

The favored machine studying packages PyTorch (19% of customers, $166,000 common wage), TensorFlow (20%, $164,000), and scikit-learn (27%, $157,000) occupied the center floor. These salaries have been above the common for all respondents, which was pulled down by the big numbers who didn’t use instruments or solely used Excel. The best salaries have been related to H2O (3%, $183,000), KNIME (2%, $180,000), Spark NLP (5%, $179,000), and Spark MLlib (8%, $175,000). It’s exhausting to belief conclusions based mostly on 2% or 3% of the respondents, however it seems that salaries are larger for individuals who work with instruments which have a number of “buzz” however aren’t but extensively used. Employers pay a premium for specialised experience.

Determine 5. Common wage by instruments for statistics or machine studying

We see virtually precisely the identical factor after we take a look at information frameworks (Determine 6). Once more, the commonest response was from individuals who didn’t use a framework; that group additionally obtained the bottom salaries (30% of customers, $133,000 common wage).

In 2021, Hadoop usually looks like legacy software program, however 15% of the respondents have been engaged on the Hadoop platform, with a mean wage of $166,000. That was above the common wage for all customers and on the low finish of the midrange for salaries sorted by platform.

The best salaries have been related to Clicktale (now ContentSquare), a cloud-based analytics system for researching buyer expertise: solely 0.2% of respondents use it, however they’ve a mean wage of $225,000. Different frameworks related to excessive salaries have been Tecton (the business model of Michelangelo, at $218,000), Ray ($191,000), and Amundsen ($189,000). These frameworks had comparatively few customers—probably the most extensively used on this group was Amundsen with 0.8% of respondents (and once more, we warning in opposition to studying an excessive amount of into outcomes based mostly on so few respondents). All of those platforms are comparatively new, often mentioned within the tech press and social media, and look like rising healthily. Kafka, Spark, Google BigQuery, and Dask have been within the center, with a number of customers (15%, 19%, 8%, and 5%) and above-average salaries ($179,000, $172,000, $170,000, and $170,000). Once more, the most well-liked platforms occupied the center of the vary; expertise with much less often used and rising platforms commanded a premium.

Determine 6. Common wage by information framework or platform

Salaries by Business

The best variety of respondents labored within the software program trade (20% of the entire), adopted by consulting (11%) and healthcare, banking, and training (every at 8%). Comparatively few respondents listed themselves as consultants (additionally 2%), although consultancy tends to be cyclic, relying on present pondering on outsourcing, tax regulation, and different elements. The common earnings for consultants was $150,000, which is simply barely larger than the common for all respondents ($146,000). Which will point out that we’re at the moment in some sort of an equilibrium between consultants and in-house expertise.

Whereas information evaluation has turn out to be important to each sort of enterprise and AI is discovering many purposes exterior of computing, salaries have been highest within the laptop trade itself, as Determine 7 makes clear. For our functions, the “laptop trade” was divided into 4 segments: laptop {hardware}, cloud providers and internet hosting, safety, and software program. Common salaries in these industries ranged from $171,000 (for laptop {hardware}) to $164,000 (for software program). Salaries for the promoting trade (together with social media) have been surprisingly low, solely $150,000.

Determine 7. Common wage by trade

Schooling and nonprofit organizations (together with commerce associations) have been on the backside finish of the size, with compensation simply above $100,000 ($106,000 and $103,000, respectively). Salaries for technical employees in authorities have been barely larger ($124,000).

Salaries by State

When taking a look at information and AI practitioners geographically, there weren’t any large surprises. The states with probably the most respondents have been California, New York, Texas, and Massachusetts. California accounted for 19% of the entire, with over double the variety of respondents from New York (8%). To grasp how these 4 states dominate, do not forget that they make up 42% of our respondents however solely 27% of america’ inhabitants.

Salaries in California have been the best, averaging $176,000. The Japanese Seaboard did properly, with a mean wage of $157,000 in Massachusetts (second highest). New York, Delaware, New Jersey, Maryland, and Washington, DC, all reported common salaries within the neighborhood of $150,000 (as did North Dakota, with 5 respondents). The common wage reported for Texas was $148,000, which is barely above the nationwide common however nonetheless appears on the low facet for a state with a big expertise trade.

Salaries within the Pacific Northwest weren’t as excessive as we anticipated. Washington simply barely made it into the highest 10 when it comes to the variety of respondents, and common salaries in Washington and Oregon have been $138,000 and $133,000, respectively. (See Determine 8 for the total checklist.)

The best-paying jobs, with salaries over $300,000, have been concentrated in California (5% of the state’s respondents) and Massachusetts (4%). There have been just a few fascinating outliers: North Dakota and Nevada each had only a few respondents, however every had one respondent making over $300,000. In Nevada, we’re guessing that’s somebody who works for the on line casino trade—in any case, the origins of likelihood and statistics are tied to playing. Most states had no respondents with compensation over $300,000.

Determine 8. Common wage by state

The bottom salaries have been, for probably the most half, from states with the fewest respondents. We’re reluctant to say greater than that. These states sometimes had underneath 10 respondents, which signifies that averaging salaries is extraordinarily noisy. For instance, Alaska solely had two respondents and a mean wage of $75,000; Mississippi and Louisiana every solely had 5 respondents, and Rhode Island solely had three. In any of those states, one or two further respondents on the government stage would have an enormous impact on the states common. Moreover, the averages in these states are so low that each one (or virtually all) respondents have to be college students, interns, or in entry-level positions. So we don’t suppose we will make any assertion stronger than “the excessive paying jobs are the place you’d anticipate them to be.”

Job Change by Wage

Regardless of the variations between states, we discovered that the need to vary jobs based mostly on lack of compensation didn’t rely considerably on geography. There have been outliers at each extremes, however they have been all in states the place the variety of respondents was small and one or two folks trying to change jobs would make a big distinction. It’s not terribly fascinating to say that 24% of respondents from California intend to vary jobs (solely 2% above the nationwide common); in any case, you’d anticipate California to dominate. There could also be a small sign from states like New York, with 232 respondents, of whom 27% intend to vary jobs, or from a state like Virginia, with 137 respondents, of whom solely 19% have been pondering of fixing. However once more, these numbers aren’t a lot completely different from the entire share of potential job changers.

If intent to vary jobs resulting from compensation isn’t depending on location, then what does it rely on? Wage. It’s in no way stunning that respondents with the bottom salaries (underneath $50,000/yr) are extremely motivated to vary jobs (29%); this group consists largely of scholars, interns, and others who’re beginning their careers. The group that confirmed the second highest need to vary jobs, nevertheless, had the best salaries: over $400,000/yr (27%). It’s an fascinating pairing: these with the best and lowest salaries have been most intent on getting a wage improve.

26% of these with annual salaries between $50,000 and $100,000 indicated that they intend to vary jobs due to compensation. For the rest of the respondents (these with salaries between $100,000 and $400,000), the proportion who intend to vary jobs was 22% or decrease.

Salaries by Certification

Over a 3rd of the respondents (37%) replied that they hadn’t obtained any certifications previously yr. The subsequent largest group replied “different” (14%), that means that that they had obtained certifications previously yr however not one of many certifications we listed. We allowed them to put in writing in their very own responses, and so they shared 352 distinctive solutions, starting from vendor-specific certifications (e.g., DataRobot) to college levels (e.g., College of Texas) to well-established certifications in any variety of fields (e.g., Licensed Info Programs Safety Skilled a.ok.a. CISSP). Whereas there have been definitely instances the place respondents used completely different phrases to explain the identical factor, the quantity of distinctive write-in responses displays the nice variety of certifications obtainable.

Cloud certifications have been by far the most well-liked. The highest certification was for AWS (3.9% obtained AWS Licensed Options Architect-Affiliate), adopted by Microsoft Azure (3.8% had AZ-900: Microsoft Azure Fundamentals), then two extra AWS certifications and CompTIA’s Safety+ certification (1% every). Remember that 1% solely represents 27 respondents, and all the opposite certifications had even fewer respondents.

As Determine 9 reveals, the best salaries have been related to AWS certifications, the Microsoft AZ-104 (Azure Administrator Affiliate) certification, and the CISSP safety certification. The common wage for folks itemizing these certifications was larger than the common wage for US respondents as an entire. And the common wage for respondents who wrote in a certification was barely above the common for individuals who didn’t earn any certifications ($149,000 versus $143,000).

Determine 9. Common wage by certification earned

Certifications have been additionally related to wage will increase (Determine 10). Once more AWS and Microsoft Azure dominate, with Microsoft’s AZ-104 main the best way, adopted by three AWS certifications. And on the entire, respondents with certifications seem to have obtained bigger wage will increase than those that didn’t earn any technical certifications.

Determine 10. Common wage change by certification

Google Cloud is an apparent omission from this story. Whereas Google is the third-most-important cloud supplier, solely 26 respondents (roughly 1%) claimed any Google certification, all underneath the “Different” class.

Amongst our respondents, safety certifications have been comparatively unusual and didn’t look like related to considerably larger salaries or wage will increase. Cisco’s CCNP was related to larger wage will increase; respondents who earned the CompTIA Safety+ or CISSP certifications obtained smaller will increase. Does this mirror that administration undervalues safety coaching? If this speculation is right, undervaluing safety is clearly a big mistake, given the continued significance of safety and the opportunity of new assaults in opposition to AI and different data-driven programs.

Cloud certifications clearly had the best impact on wage will increase. With only a few exceptions, any certification was higher than no certification: respondents who wrote in a certification underneath “Different” averaged a $9,600 wage improve over the previous few years, versus $8,900 for respondents who didn’t receive a certification and $9,300 for all respondents no matter certification.


Collaborating in coaching resulted in wage will increase—however solely for individuals who spent greater than 100 hours in a coaching program. As Determine 11 reveals, these respondents had a mean wage improve of $11,000. This was additionally the most important group of respondents (19%). Respondents who solely reported endeavor 1–19 hours of coaching (8%) noticed decrease wage will increase, with a mean of $7,100. It’s fascinating that those that participated in 1–19 hours of coaching noticed smaller will increase than those that didn’t take part in coaching in any respect. It doesn’t make sense to take a position about this distinction, however the information does make one factor clear: when you have interaction in coaching, be severe about it.

Determine 11. Common wage change vs. hours of coaching

We additionally requested what forms of coaching respondents engaged in: whether or not it was firm offered (for which there have been three alternate options), a certification program, a convention, or another sort of coaching (detailed in Determine 12). Respondents who took benefit of company-provided alternatives had the best common salaries ($156,000, $150,000, and $149,000). Those that obtained certifications have been subsequent ($148,000). The outcomes are related if we take a look at wage will increase over the previous three years: Those that participated in varied types of company-offered coaching obtained will increase between $11,000 and $10,000. Wage will increase for respondents who obtained a certification have been in the identical vary ($11,000).

Determine 12. Common wage change vs. sort of coaching

The Final Phrase

Information and AI professionals—a rubric underneath which we embrace information scientists, information engineers, and specialists in AI and ML—are well-paid, reporting a mean wage just below $150,000. Nevertheless, there have been sharp state-by-state variations: salaries have been considerably larger in California, although the Northeast (with some exceptions) did properly.

There have been additionally important variations between salaries for women and men. Males’s salaries have been larger no matter job title, no matter coaching and no matter tutorial levels—despite the fact that girls have been extra more likely to have a sophisticated tutorial diploma (PhD or grasp’s diploma) than have been males.

We don’t see proof of a “nice resignation.” Job turnover by way of the pandemic was roughly what we’d anticipate (maybe barely beneath regular). Respondents did look like involved about job safety, although they didn’t wish to admit it explicitly. However except the least- and most-highly compensated respondents, the intent to vary jobs due to wage was surprisingly constant and nothing to be alarmed at.

Coaching was essential, partly as a result of it was related to hireability and job safety however extra as a result of respondents have been genuinely inquisitive about studying new expertise and bettering present ones. Cloud coaching, significantly in AWS and Microsoft Azure, was probably the most strongly related to larger wage will increase.

However maybe we must always go away the final phrase to our respondents. The ultimate query in our survey requested what areas of expertise would have the most important impact on wage and promotions within the coming yr. It wasn’t a shock that many of the respondents stated machine studying (63%)—as of late, ML is the most well liked subject within the information world. It was extra of a shock that “programming languages” was famous by simply 34% of respondents. (Solely “Different” obtained fewer responses—see Determine 13 for full particulars.) Our respondents clearly aren’t impressed by programming languages, despite the fact that the information means that employers are prepared to pay a premium for Rust, Go, and Scala.

There’s one other sign value being attentive to if we glance past the extremes. Information instruments, cloud and containers, and automation have been almost tied (46, 47, and 44%). The cloud and containers class consists of instruments like Docker and Kubernetes, cloud suppliers like AWS and Microsoft Azure, and disciplines like MLOps. The instruments class consists of instruments for constructing and sustaining information pipelines, like Kafka. “Automation” can imply a number of issues however on this context in all probability means automated coaching and deployment.

Determine 13. What applied sciences could have the most important impact on compensation within the coming yr?

We’ve argued for a while that operations—efficiently deploying and managing purposes in manufacturing—is the most important concern going through ML practitioners within the coming years. If you wish to keep on high of what’s occurring in information, and if you wish to maximize your job safety, hireability, and wage, don’t simply learn to construct AI fashions; learn to deploy purposes that dwell within the cloud.

Within the traditional film The Graduate, one character famously says, “There’s a fantastic future in plastics. Give it some thought.” In 2021, and with out being anyplace close to as repulsive, we’d say, “There’s a fantastic future within the cloud. Give it some thought.”



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